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2        <title>Intel's Long Awaited Xeon 600 Workstation Processors | Exxact Blog</title>
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299.modal-marketo-form .modal-content{border-radius:8px;overflow:hidden}@media only screen and (max-width:576px){.modal-marketo-form .modal-content{border-radius:unset}}.form,.form-2,.form-1{display:flex!important;flex-direction:column;position:relative}.form input,.form-2 input,.form-1 input,.form .form-input,.form-2 .form-input,.form-1 .form-input{width:100%!important}.form span.required-asterisk,.form-2 span.required-asterisk,.form-1 span.required-asterisk{color:#ff6201}.form .form-card-element,.form-2 .form-card-element,.form-1 .form-card-element{width:100%}.form .form-error,.form-2 .form-error,.form-1 .form-error,.form .form-messages,.form-2 .form-messages,.form-1 .form-messages{height:0;font-size:.75rem;font-weight:600;opacity:0;transition:height .4s ease-in-out,opacity .4s ease-in-out}.form .form-error.show,.form-2 .form-error.show,.form-1 .form-error.show,.form .form-messages.show,.form-2 .form-messages.show,.form-1 .form-messages.show{height:2em;opacity:1}.form .form-error,.form-2 .form-error,.form-1 .form-error{display:flex!important;flex-direction:column;justify-content:center}.form .form-checkbox-group input,.form-2 .form-checkbox-group input,.form-1 .form-checkbox-group input,.form .form-checkbox-group label,.form-2 .form-checkbox-group label,.form-1 .form-checkbox-group label{margin-right:8px}.form .form-checkbox-group input,.form-2 .form-checkbox-group input,.form-1 .form-checkbox-group input{width:auto}.form .form-messages,.form-2 .form-messages,.form-1 .form-messages{opacity:1;height:2em;text-align:center}.form ::placeholder,.form-2 ::placeholder,.form-1 ::placeholder{color:#c3cfe2;opacity:1}.form :-ms-input-placeholder,.form-2 :-ms-input-placeholder,.form-1 :-ms-input-placeholder{color:#c3cfe2}.form ::-ms-input-placeholder,.form-2 ::-ms-input-placeholder,.form-1 ::-ms-input-placeholder{color:#c3cfe2}.form-1>*{margin-bottom:6px}.form-1 input,.form-1 .form-input{border:1px solid #e6eff7!important;border-radius:5px!important;padding:7px 16px!important;font-size:1rem!important;background-color:#f5f7fa!important}.form-1 input:focus,.form-1 .form-input:focus{box-shadow:inset 0 1px 1px #e6eff7,0 0 8px #91c3ff!important}.form-1 input.error,.form-1 .form-input.error{border-color:#c3cfe2!important;background-color:#f5f7fa!important}.form-1 .form-card-element{width:100%}.form-1 .form-error,.form-1 .form-messages{color:#ff6201}.form-2 .form-field{margin-bottom:8px}.form-2 label{color:#93a6c1;font-size:.75rem}.form-2 input,.form-2 .form-input,.form-2 .StripeElement{border:1px solid #e6eff7;border-radius:5px;padding:7px 16px;font-size:1rem;color:#293963;background-color:#fff;transition:border-color .2s linear}.form-2 .StripeElement{padding:8px 16px}.form-2 input:focus,.form-2 .StripeElement--focus{border-color:#c3cfe2}.form-2 input.error,.form-2 .StripeElement--invalid{border-color:#ff6201;background-color:#f5f7fa}.form-2 .select-options{border:1px solid #e6eff7;border-right:2px solid #e6eff7}.form-2 .open input{border-color:#c3cfe2}.form-2 .open .select-options{border-right:2px solid #c3cfe2;border-left:1px solid #c3cfe2;border-bottom:1px solid #c3cfe2}.form-2 .form-error,.form-2 .form-messages{color:#ff6201}app-root{display:flex;flex-direction:column}app-root main{flex-grow:1;min-height:calc(100vh - 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299left:50%;top:50%;transform:translate(-50%,-50%)}.swiper.swiper-multi .swiper-multi-slide .swiper-multi-slide-heading{color:#293963;display:-webkit-box;font-size:.875rem;-webkit-line-clamp:2;-webkit-box-orient:vertical}.swiper.swiper-multi .swiper-multi-slide .swiper-multi-slide-heading:hover{color:#00c5c2}.swiper .swiper-pagination .arrow{display:inline-block;border-width:0 1px 1px 0;padding:3px;position:relative;z-index:-2;margin:0;pointer-events:all}.swiper .swiper-pagination .arrow:after{font-size:1rem;width:30px;height:30px}.swiper .swiper-pagination span{color:#687aa0;margin:24px}.swiper .swiper-pagination .swiper-pagination-bullet-active{border:#00c5c2 solid 1px;background-color:#b5eeee}.swiper .swiper-pagination .disabled{color:#e6eff7}.swiper .swiper-multi-slide .swiper-multi-image-wrapper{margin-bottom:24px;border-radius:8px;padding:24px;background-color:#f5f7fa}.swiper .swiper-multi-slide .swiper-multi-image-wrapper img{mix-blend-mode:multiply}.swiper .heading-exx{text-align:left!important;color:#293963}
300/*!
301 * Bootstrap Grid v5.3.8 (https://getbootstrap.com/)
302 * Copyright 2011-2025 The Bootstrap Authors
303 * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
304 */
305/*!
306 * Bootstrap Utilities v5.3.8 (https://getbootstrap.com/)
307 * Copyright 2011-2025 The Bootstrap Authors
308 * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
309 */
310</style><style ng-app-id="ng">.accordion[_ngcontent-ng-c1770694409]   .accordion-header[_ngcontent-ng-c1770694409]{margin-bottom:16px;font-weight:600}.accordion[_ngcontent-ng-c1770694409]   .accordion-item[_ngcontent-ng-c1770694409]{border:none;background-color:unset;border-bottom:1px solid #c3cfe2;margin-bottom:16px}.accordion[_ngcontent-ng-c1770694409]   .accordion-item[_ngcontent-ng-c1770694409]   .accordion-button[_ngcontent-ng-c1770694409]{color:#293963;background-color:unset;box-shadow:unset;padding:unset;cursor:pointer}.accordion[_ngcontent-ng-c1770694409]   .accordion-item[_ngcontent-ng-c1770694409]   .accordion-body[_ngcontent-ng-c1770694409]{padding:0;display:flex!important;flex-direction:column}.accordion[_ngcontent-ng-c1770694409]   .accordion-item[_ngcontent-ng-c1770694409]   .accordion-body[_ngcontent-ng-c1770694409]   a[_ngcontent-ng-c1770694409]{color:#687aa0;padding-bottom:8px;transition:color .3s linear}.accordion[_ngcontent-ng-c1770694409]   .accordion-item[_ngcontent-ng-c1770694409]   .accordion-body[_ngcontent-ng-c1770694409]   a[_ngcontent-ng-c1770694409]:last-of-type{padding-bottom:16px}.accordion[_ngcontent-ng-c1770694409]   .accordion-item[_ngcontent-ng-c1770694409]   .accordion-body[_ngcontent-ng-c1770694409]   a[_ngcontent-ng-c1770694409]:hover{color:#006cff;font-weight:500}.accordion[_ngcontent-ng-c1770694409]   .accordion-item[_ngcontent-ng-c1770694409]   .accordion-button[_ngcontent-ng-c1770694409]{justify-content:space-between}.plus-icon[_ngcontent-ng-c1770694409]{display:none}.icon-container[_ngcontent-ng-c1770694409]{width:20px;height:20px;background-image:url(https://resources.exxactcorp.com/resources/exx/assets/icons/minus.svg)}.collapsed[_ngcontent-ng-c1770694409]   .plus-icon[_ngcontent-ng-c1770694409]{display:block}#footer[_ngcontent-ng-c1770694409]{padding:0;background-color:#f5f7fa;z-index:100}#footer[_ngcontent-ng-c1770694409]   section[_ngcontent-ng-c1770694409]{width:100%;max-width:1200px}#footer[_ngcontent-ng-c1770694409]   .vertical-bar[_ngcontent-ng-c1770694409]{display:inline-block;color:#c3cfe2}#footer[_ngcontent-ng-c1770694409]   .footer-container[_ngcontent-ng-c1770694409]{padding:0 24px}#footer[_ngcontent-ng-c1770694409]   #footer-row-1[_ngcontent-ng-c1770694409]{padding:16px 0!important}#footer[_ngcontent-ng-c1770694409]   #footer-row-1[_ngcontent-ng-c1770694409]   .footer-menu-header[_ngcontent-ng-c1770694409]{width:100%;overflow:hidden}#footer[_ngcontent-ng-c1770694409]   #footer-row-1[_ngcontent-ng-c1770694409]   .footer-menu-header[_ngcontent-ng-c1770694409]   .feather[_ngcontent-ng-c1770694409]{text-align:center;stroke-width:0}#footer[_ngcontent-ng-c1770694409]   #footer-row-1[_ngcontent-ng-c1770694409]   .footer-menu-header[_ngcontent-ng-c1770694409]   .button[_ngcontent-ng-c1770694409]{padding:0;display:block;line-height:50px;width:35px;height:35px;background-color:#fff;box-shadow:0 6px 10px #0003;border-radius:50%}#footer[_ngcontent-ng-c1770694409]   #footer-row-1[_ngcontent-ng-c1770694409]   .footer-menu-header[_ngcontent-ng-c1770694409]   .copy[_ngcontent-ng-c1770694409]{dis
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314.pe-xxl-0[_ngcontent-ng-c558412000]{padding-right:0!important}.pe-xxl-1[_ngcontent-ng-c558412000]{padding-right:.25rem!important}.pe-xxl-2[_ngcontent-ng-c558412000]{padding-right:.5rem!important}.pe-xxl-3[_ngcontent-ng-c558412000]{padding-right:1rem!important}.pe-xxl-4[_ngcontent-ng-c558412000]{padding-right:1.5rem!important}.pe-xxl-5[_ngcontent-ng-c558412000]{padding-right:3rem!important}.pb-xxl-0[_ngcontent-ng-c558412000]{padding-bottom:0!important}.pb-xxl-1[_ngcontent-ng-c558412000]{padding-bottom:.25rem!important}.pb-xxl-2[_ngcontent-ng-c558412000]{padding-bottom:.5rem!important}.pb-xxl-3[_ngcontent-ng-c558412000]{padding-bottom:1rem!important}.pb-xxl-4[_ngcontent-ng-c558412000]{padding-bottom:1.5rem!important}.pb-xxl-5[_ngcontent-ng-c558412000]{padding-bottom:3rem!important}.ps-xxl-0[_ngcontent-ng-c558412000]{padding-left:0!important}.ps-xxl-1[_ngcontent-ng-c558412000]{padding-left:.25rem!important}.ps-xxl-2[_ngcontent-ng-c558412000]{padding-left:.5rem!important}.ps-xxl-3[_ngcontent-ng-c558412000]{padding-left:1rem!important}.ps-xxl-4[_ngcontent-ng-c558412000]{padding-left:1.5rem!important}.ps-xxl-5[_ngcontent-ng-c558412000]{padding-left:3rem!important}.gap-xxl-0[_ngcontent-ng-c558412000]{gap:0!important}.gap-xxl-1[_ngcontent-ng-c558412000]{gap:.25rem!important}.gap-xxl-2[_ngcontent-ng-c558412000]{gap:.5rem!important}.gap-xxl-3[_ngcontent-ng-c558412000]{gap:1rem!important}.gap-xxl-4[_ngcontent-ng-c558412000]{gap:1.5rem!important}.gap-xxl-5[_ngcontent-ng-c558412000]{gap:3rem!important}.row-gap-xxl-0[_ngcontent-ng-c558412000]{row-gap:0!important}.row-gap-xxl-1[_ngcontent-ng-c558412000]{row-gap:.25rem!important}.row-gap-xxl-2[_ngcontent-ng-c558412000]{row-gap:.5rem!important}.row-gap-xxl-3[_ngcontent-ng-c558412000]{row-gap:1rem!important}.row-gap-xxl-4[_ngcontent-ng-c558412000]{row-gap:1.5rem!important}.row-gap-xxl-5[_ngcontent-ng-c558412000]{row-gap:3rem!important}.column-gap-xxl-0[_ngcontent-ng-c558412000]{column-gap:0!important}.column-gap-xxl-1[_ngcontent-ng-c558412000]{column-gap:.25rem!important}.column-gap-xxl-2[_ngcontent-ng-c558412000]{column-gap:.5rem!important}.column-gap-xxl-3[_ngcontent-ng-c558412000]{column-gap:1rem!important}.column-gap-xxl-4[_ngcontent-ng-c558412000]{column-gap:1.5rem!important}.column-gap-xxl-5[_ngcontent-ng-c558412000]{column-gap:3rem!important}.text-xxl-start[_ngcontent-ng-c558412000]{text-align:left!important}.text-xxl-end[_ngcontent-ng-c558412000]{text-align:right!important}.text-xxl-center[_ngcontent-ng-c558412000]{text-align:center!important}}@media(min-width:1200px){.fs-1[_ngcontent-ng-c558412000]{font-size:2.5rem!important}.fs-2[_ngcontent-ng-c558412000]{font-size:2rem!important}.fs-3[_ngcontent-ng-c558412000]{font-size:1.75rem!important}.fs-4[_ngcontent-ng-c558412000]{font-size:1.5rem!important}}@media print{.d-print-inline[_ngcontent-ng-c558412000]{display:inline!important}.d-print-inline-block[_ngcontent-ng-c558412000]{display:inline-block!important}.d-print-block[_ngcontent-ng-c558412000]{display:block!important}.d-print-grid[_ngcontent-ng-c558412000]{display:grid!important}.d-print-inline-grid[_ngcontent-ng-c558412000]{display:inline-grid!important}.d-print-table[_ngcontent-ng-c558412000]{display:table!important}.d-print-table-row[_ngcontent-ng-c558412000]{display:table-row!important}.d-print-table-cell[_ngcontent-ng-c558412000]{display:table-cell!important}.d-print-flex[_ngcontent-ng-c558412000]{display:flex!important}.d-print-inline-flex[_ngcontent-ng-c558412000]{display:inline-flex!important}.d-print-none[_ngcontent-ng-c558412000]{display:none!important}}.dropup[_ngcontent-ng-c558412000], .dropend[_ngcontent-ng-c558412000], .dropdown[_ngcontent-ng-c558412000], .dropstart[_ngcontent-ng-c558412000], .dropup-center[_ngcontent-ng-c558412000], .dropdown-center[_ngcontent-ng-c558412000]{position:relative}.dropdown-toggle[_ngcontent-ng-c558412000]{white-space:nowrap}.dropdown-toggle[_ngcontent-ng-c558412000]:after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:.3em solid;border-right:.3em solid transparent;border-bottom:0;border-left:.3em solid transparent}.dropdown-toggle[_ngcontent-ng-c558412000]:empty:after{margin-left:0}.dropdown-menu[_ngcontent-ng-c558412000]{--bs-dropdown-zindex: 1000;--bs-dropdown-min-width: 10rem;--bs-dropdown-padding-x: 0;--bs-dropdown-padding-y: .5rem;--bs-dropdown-spacer: .125rem;--bs-dropdown-font-size: 1rem;--bs-dropdown-color: var(--bs-body-color);--bs-dropdown-bg: var(--bs-body-bg);--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-border-radius: var(--bs-border-radius);--bs-dropdown-border-width: var(--bs-border-width);--bs-dropdown-inner-border-radius: calc(var(--bs-border-radius) - var(--bs-border-width));--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-divider-margin-y: .5rem;
314--bs-dropdown-box-shadow: var(--bs-box-shadow);--bs-dropdown-link-color: var(--bs-body-color);--bs-dropdown-link-hover-color: var(--bs-body-color);--bs-dropdown-link-hover-bg: var(--bs-tertiary-bg);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #0d6efd;--bs-dropdown-link-disabled-color: var(--bs-tertiary-color);--bs-dropdown-item-padding-x: 1rem;--bs-dropdown-item-padding-y: .25rem;--bs-dropdown-header-color: #6c757d;--bs-dropdown-header-padding-x: 1rem;--bs-dropdown-header-padding-y: .5rem;position:absolute;z-index:var(--bs-dropdown-zindex);display:none;min-width:var(--bs-dropdown-min-width);padding:var(--bs-dropdown-padding-y) var(--bs-dropdown-padding-x);margin:0;font-size:var(--bs-dropdown-font-size);color:var(--bs-dropdown-color);text-align:left;list-style:none;background-color:var(--bs-dropdown-bg);background-clip:padding-box;border:var(--bs-dropdown-border-width) solid var(--bs-dropdown-border-color);border-radius:var(--bs-dropdown-border-radius)}.dropdown-menu[data-bs-popper][_ngcontent-ng-c558412000]{top:100%;left:0;margin-top:var(--bs-dropdown-spacer)}.dropdown-menu-start[_ngcontent-ng-c558412000]{--bs-position: start}.dropdown-menu-start[data-bs-popper][_ngcontent-ng-c558412000]{right:auto;left:0}.dropdown-menu-end[_ngcontent-ng-c558412000]{--bs-position: end}.dropdown-menu-end[data-bs-popper][_ngcontent-ng-c558412000]{right:0;left:auto}@media(min-width:576px){.dropdown-menu-sm-start[_ngcontent-ng-c558412000]{--bs-position: start}.dropdown-menu-sm-start[data-bs-popper][_ngcontent-ng-c558412000]{right:auto;left:0}.dropdown-menu-sm-end[_ngcontent-ng-c558412000]{--bs-position: end}.dropdown-menu-sm-end[data-bs-popper][_ngcontent-ng-c558412000]{right:0;left:auto}}@media(min-width:768px){.dropdown-menu-md-start[_ngcontent-ng-c558412000]{--bs-position: 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.dropdown-toggle[_ngcontent-ng-c558412000]:before{display:inline-block;margin-right:.255em;vertical-align:.255em;content:"";border-top:.3em solid transparent;border-right:.3em solid;border-bottom:.3em solid transparent}.dropstart[_ngcontent-ng-c558412000]   .dropdown-toggle[_ngcontent-ng-c558412000]:empty:after{margin-left:0}.dropstart[_ngcontent-ng-c558412000]   .dropdown-toggle[_ngcontent-ng-c558412000]:before{vertical-align:0}.dropdown-divider[_ngcontent-ng-c558412000]{height:0;margin:var(--bs-dropdown-divider-margin-y) 0;overflow:hidden;border-top:1px solid var(--bs-dropdown-divider-bg);opacity:1}.dropdown-item[_ngcontent-ng-c558412000]{display:block;width:100%;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);clear:both;font-weight:400;color:var(--bs-dropdown-link-color);text-align:inherit;text-decoration:none;white-space:nowrap;background-color:transparent;border:0;border-radius:var(--bs-dropdown-item-border-radius, 0)}.dropdown-item[_ngcontent-ng-c558412000]:hover, .dropdown-item[_ngcontent-ng-c558412000]:focus{color:var(--bs-dropdown-link-hover-color);background-color:var(--bs-dropdown-link-hover-bg)}.dropdown-item.active[_ngcontent-ng-c558412000], .dropdown-item[_ngcontent-ng-c558412000]:active{color:var(--bs-dropdown-link-active-color);text-decoration:none;background-color:var(--bs-dropdown-link-active-bg)}.dropdown-item.disabled[_ngcontent-ng-c558412000], .dropdown-item[_ngcontent-ng-c558412000]:disabled{color:var(--bs-dropdown-link-disabled-color);pointer-events:none;background-color:transparent}.dropdown-menu.show[_ngcontent-ng-c558412000]{display:block}.dropdown-header[_ngcontent-ng-c558412000]{display:block;padding:var(--bs-dropdown-header-padding-y) var(--bs-dropdown-header-padding-x);margin-bottom:0;font-size:.875rem;color:var(--bs-dropdown-header-color);white-space:nowrap}.dropdown-item-text[_ngcontent-ng-c558412000]{display:block;padding:var(--bs-dropdown-item-padding-y) 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324ompared to dual‑socket servers while still supporting substantial parallel workloads. Exxact is looking forward to offering configurable Intel Xeon 600 for AI, scientific computing, and engineering simulation workloads.</p><p>We will go over the specifications of the Intel Xeon 600, set to be officially released later this year, with a late March 2026 launch.</p></div><!----><!----><!----><!----></div><div class="group mb-4" id="IntelXeon600vsIntelXeonW-3500/2500ArchitecturalDifferences"><div class="text-block-code-block mb-3"><h2>Intel Xeon 600 vs Intel Xeon W-3500/2500 Architectural Differences</h2><p>Intel Xeon 600 utilizes the chiplet-based Granite Rapids architecture borrowed from their Xeon 6000 server processors. These new workstation processors will be supported on their new W890 motherboard and chipset beginning late March. Exxact is particularly excited to showcase the Intel Xeon 600 platform at <a href="/News-Events" target="_blank">NVIDIA GTC 2026;</a> come visit and say hello!</p><p>Compared to the W-3500/2500 last-generation processors, the Intel Xeon 600 has</p><ul><li>Up to 86 full-fat performance cores only</li><li>Increased L2 and L3 cache</li><li>Up to 128 PCIe 5.0 lanes</li><li>9% increase in single-threaded performance</li><li>61% increase in multi-threaded performance</li></ul></div><!----><!----><!----><!----></div><div class="group mb-4" id="IntelXeon600Specifications"><div class="text-block-code-block mb-3"><h2>Intel Xeon 600 Specifications</h2><p>Intel Xeon 600 Series processors are workstation processors targeting professional workloads that benefit from many performance cores and wide memory and I/O subsystems.</p><table><tbody><tr><td>Model</td><td>Cores / Threads</td><td>Max Turbo Clock Speed</td><td>All Core Turbo Clock Speed</td><td>Base Clock Speed</td><td>PCIe Lanes</td><td>Cache</td><td>TDP</td></tr><tr><td>Intel Xeon 698X</td><td>86 / 172</td><td>4.8 GHz</td><td>3.0 GHz</td><td>2.0 GHz</td><td>128</td><td>336 MB</td><td>350W</td></tr><tr><td>Intel Xeon 696X</td><td>64 / 128</td><td>4.8 GHz</td><td>3.5 GHz</td><td>2.4 GHz</td><td>128</td><td>336 MB</td><td>350W</td></tr><tr><td>Intel Xeon 678X</td><td>48 / 96</td><td>4.9 GHz</td><td>3.8 GHz</td><td>2.4 GHz</td><td>128</td><td>192 MB</td><td>300W</td></tr><tr><td>Intel Xeon 676X</td><td>32 / 64</td><td>4.9 GHz</td><td>4.3 GHz</td><td>2.8 GHz</td><td>128</td><td>144 MB</td><td>275W</td></tr><tr><td>Intel Xeon 674X</td><td>28 / 56</td><td>4.9 GHz</td><td>4.3 GHz</td><td>3.0 GHz</td><td>128</td><td>144 MB</td><td>270W</td></tr><tr><td>Intel Xeon 658X</td><td>24 / 48</td><td>4.9 GHz</td><td>4.3 GHz</td><td>3.0 GHz</td><td>128</td><td>144 MB</td><td>250W</td></tr><tr><td>Intel Xeon 656</td><td>20 / 40</td><td>4.8 GHz</td><td>4.5 GHz</td><td>2.9 GHz</td><td>128</td><td>72 MB</td><td>210W</td></tr><tr><td>Intel Xeon 654</td><td>18 / 36</td><td>4.8 GHz</td><td>4.5 GHz</td><td>3.1 GHz</td><td>128</td><td>72 MB</td><td>200W</td></tr><tr><td>Intel Xeon 638</td><td>16 / 32</td><td>4.8 GHz</td><td>4.5 GHz</td><td>3.2 GHz</td><td>80</td><td>72 MB</td><td>180W</td></tr><tr><td>Intel Xeon 636</td><td>12 / 24</td><td>4.7 GHz</td><td>4.5 GHz</td><td>3.5 GHz</td><td>80</td><td>48 MB</td><td>170W</td></tr><tr><td>Intel Xeon 634</td><td>12 / 24</td><td>4.6 GHz</td><td>3.9 GHz</td><td>2.7 GHz</td><td>80</td><td>48 MB</td><td>150W</td></tr></tbody></table><p>As usual, high core count models have lower all-core turbo clock speeds, while lower core count models can clock higher. Understanding whether your workload prioritizes clock speed or core count will help you select the optimal processor. At Exxact, we can help configure the right system for your unique workload!</p><p>&nbsp;</p></div><!----><!----><!----><!----></div><div class="group mb-4" id=""><!----><!----><!----><div class="color-block-desktop mb-3"><div class="color-block-container" style="background-image: url(https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0adfffce84eb3abb/65cbbd5323dbef12a7ffeb92/Background-Pattern.png);"><div class="image-container"><img src="https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltb941e0955dfc0d16/67ed8efbb41d38c45677ff9e/exx-vws-ph.png"></div><!----><div class="text-container"><div><h4 class="bold">Accelerate Your Unique Workloads with the Latest Hardware</h4><p>We extensively stock the latest CPUs and most powerful GPUs; accelerate your workloads with a workstation optimized to your deployment, budget, and desired performance.</p><a href="/category/Standard-Workstations">Configure Now <i-feather name="chevron-right" _nghost-ng-c1868629123="" ngh="1"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" class="feather feather-chevron-right">
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326Memory, PCIe 5.0, and CXL 2.0 Support</h3><p>In the SKU stack, Intel Xeon 600 has split the 18-cores and higher and the 16-cores and lower options that differ in memory channels and PCIe lanes.</p><p>Across the range, Xeon 600 supports DDR5 6400 MT/s RDIMM and 8000 MT/s MRDIMM, and plenty of PCIe 5.0 lanes.</p><ul><li>Channels: 8 channels (on 18-cores-and-up SKUs), 4 channels on (16-cores-and-below SKUs)</li><li>5.0 PCIe Lanes: 128 lanes (on 18-cores-and-up SKUs), 80 lanes (on 16-cores-and-below SKUs)</li></ul><p>The platform also provides substantial expansion headroom. Intel Xeon 600 processors for workstations feature 128 PCIe 5.0 lanes on the majority of SKUs, with 80 PCIe 5.0 lanes on lower core count models (16 cores and below).</p><p>In addition to supporting more GPUs, storage devices, and networking, these PCIe lanes can also be used for added memory via CXL 2.0.</p></div><!----><!----><!----><!----></div><div class="group mb-4" id="IntelXeon600WorkstationsforAI/ML,ScientificComputing,andEngineeringSimulation"><div class="text-block-code-block mb-3"><h2>Intel Xeon 600 Workstations for AI/ML, Scientific Computing, and Engineering Simulation</h2><p>Intel Xeon 600 workstations can be tailored to a range of compute‑heavy workloads. Across AI/ML, scientific research, and engineering simulation, the most important build variables tend to be CPU core count, memory capacity and bandwidth, and PCIe expansion for GPUs, storage, and networking.</p><p>Key considerations by workload:</p><ul><li><strong>AI and Machine Learning</strong><ul><li>Higher‑core Xeon 600 SKUs for data preprocessing, orchestration, and mixed CPU/GPU pipelines.</li><li>Large DDR5 memory footprints to keep datasets and intermediate results in memory.</li><li>Enough PCIe 5.0 lanes for dual or quad high‑end GPUs, plus NVMe storage and network adapters.</li></ul></li><li><strong>Scientific Computing Research</strong><ul><li>Strong multi‑core performance for molecular dynamics, cryo‑EM processing, and materials simulations.</li><li>Multi‑channel DDR5 configurations tuned for bandwidth and total capacity.</li><li>Expansion room for accelerator cards and high‑performance storage for large datasets and output files.</li></ul></li><li><strong>Engineering Simulation</strong><ul><li>Mid‑ to high‑core SKUs to support parallel solvers (CFD, FEA, particle methods).</li><li>Memory sized to keep meshes and particle sets in RAM during computation.</li><li>PCIe resources reserved for GPU accelerators (when supported) and fast NVMe scratch/results storage.</li></ul></li></ul></div><!----><!----><!----><!----></div><div class="group mb-4" id="Conclusion:MatchingXeon600toRealRequirements"><div class="text-block-code-block mb-3"><h2>Conclusion: Matching Xeon 600 to Real Requirements</h2><p>The Intel Xeon 600 Processors for workstations will be another CPU option alongside the AMD Threadripper PRO for high-performance computing.</p><p>As a solutions integrator, our role is to translate platform capabilities into concrete system configurations. We look at core count, memory channels and capacity, GPU count, storage layout, and network requirements, then align those with the actual models, simulations, and datasets you plan to run.</p><p>A workstation can power the majority of the workloads for most individuals in AI research, scientific computing, and engineering simulation. Intel Xeon 600</p><p>Talk to our engineering team about your code, typical problem sizes, and preferred software stack, and our team at Exxact is more than happy to <a href="/Exxact-VWS-130523500-E130523500" target="_blank">build your ideal Xeon 600 configuration</a> that is sized appropriately and fits your budget.</p></div><!----><!----><!----><!----></div><div class="group mb-4" id=""><!----><!----><!----><div class="color-block-desktop mb-3"><div class="color-block-container" style="background-image: url(https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0adfffce84eb3abb/65cbbd5323dbef12a7ffeb92/Background-Pattern.png);"><div class="image-container"><img src="https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt18434672773c5755/65cbbd6100d72e2e94f52f62/blog-color-block-img-ws.png"></div><!----><div class="text-container"><div><h4 class="bold">We're Here to Deliver the Tools to Power Your Research </h4><p>With access to the highest performing hardware, at Exxact, we can offer the platform optimized for your deployment, budget, and desired performance so you can make an impact with your research! </p><a href="/Contact-Us">Talk to an Engineer Today <i-feather name="chevron-right" _nghost-ng-c1868629123="" ngh="1"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" class="feather feather-chevron-right">
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330it from many performance cores and wide memory and I/O subsystems.</p><table><tbody><tr><td>Model</td><td>Cores / Threads</td><td>Max Turbo Clock Speed</td><td>All Core Turbo Clock Speed</td><td>Base Clock Speed</td><td>PCIe Lanes</td><td>Cache</td><td>TDP</td></tr><tr><td>Intel Xeon 698X</td><td>86 / 172</td><td>4.8 GHz</td><td>3.0 GHz</td><td>2.0 GHz</td><td>128</td><td>336 MB</td><td>350W</td></tr><tr><td>Intel Xeon 696X</td><td>64 / 128</td><td>4.8 GHz</td><td>3.5 GHz</td><td>2.4 GHz</td><td>128</td><td>336 MB</td><td>350W</td></tr><tr><td>Intel Xeon 678X</td><td>48 / 96</td><td>4.9 GHz</td><td>3.8 GHz</td><td>2.4 GHz</td><td>128</td><td>192 MB</td><td>300W</td></tr><tr><td>Intel Xeon 676X</td><td>32 / 64</td><td>4.9 GHz</td><td>4.3 GHz</td><td>2.8 GHz</td><td>128</td><td>144 MB</td><td>275W</td></tr><tr><td>Intel Xeon 674X</td><td>28 / 56</td><td>4.9 GHz</td><td>4.3 GHz</td><td>3.0 GHz</td><td>128</td><td>144 MB</td><td>270W</td></tr><tr><td>Intel Xeon 658X</td><td>24 / 48</td><td>4.9 GHz</td><td>4.3 GHz</td><td>3.0 GHz</td><td>128</td><td>144 MB</td><td>250W</td></tr><tr><td>Intel Xeon 656</td><td>20 / 40</td><td>4.8 GHz</td><td>4.5 GHz</td><td>2.9 GHz</td><td>128</td><td>72 MB</td><td>210W</td></tr><tr><td>Intel Xeon 654</td><td>18 / 36</td><td>4.8 GHz</td><td>4.5 GHz</td><td>3.1 GHz</td><td>128</td><td>72 MB</td><td>200W</td></tr><tr><td>Intel Xeon 638</td><td>16 / 32</td><td>4.8 GHz</td><td>4.5 GHz</td><td>3.2 GHz</td><td>80</td><td>72 MB</td><td>180W</td></tr><tr><td>Intel Xeon 636</td><td>12 / 24</td><td>4.7 GHz</td><td>4.5 GHz</td><td>3.5 GHz</td><td>80</td><td>48 MB</td><td>170W</td></tr><tr><td>Intel Xeon 634</td><td>12 / 24</td><td>4.6 GHz</td><td>3.9 GHz</td><td>2.7 GHz</td><td>80</td><td>48 MB</td><td>150W</td></tr></tbody></table><p>As usual, high core count models have lower all-core turbo clock speeds, while lower core count models can clock higher. Understanding whether your workload prioritizes clock speed or core count will help you select the optimal processor. At Exxact, we can help configure the right system for your unique workload!</p><p>&nbsp;</p></div><!----><!----><!----><!----></div><div class="group mb-4"><span class="mobile-observe" id="-mobile"></span><!----><!----><!----><div class="color-block-mobile"><div class="color-block-container" style="background-image: url(https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0adfffce84eb3abb/65cbbd5323dbef12a7ffeb92/Background-Pattern.png);"><div class="image-container"><img src="https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltb941e0955dfc0d16/67ed8efbb41d38c45677ff9e/exx-vws-ph.png"></div><!----><div class="text-container"><div><h4 class="bold">Accelerate Your Unique Workloads with the Latest Hardware</h4><p>We extensively stock the latest CPUs and most powerful GPUs; accelerate your workloads with a workstation optimized to your deployment, budget, and desired performance.</p><a href="/category/Standard-Workstations">Configure Now <i-feather name="chevron-right" _nghost-ng-c1868629123="" ngh="1"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" class="feather feather-chevron-right">
331    <polyline points="9 18 15 12 9 6"></polyline>
332</svg></i-feather></a></div></div></div></div><!----></div><div class="group mb-4"><span class="mobile-observe" id="-mobile"></span><div class="text-block-code-block"><h3>Memory, PCIe 5.0, and CXL 2.0 Support</h3><p>In the SKU stack, Intel Xeon 600 has split the 18-cores and higher and the 16-cores and lower options that differ in memory channels and PCIe lanes.</p><p>Across the range, Xeon 600 supports DDR5 6400 MT/s RDIMM and 8000 MT/s MRDIMM, and plenty of PCIe 5.0 lanes.</p><ul><li>Channels: 8 channels (on 18-cores-and-up SKUs), 4 channels on (16-cores-and-below SKUs)</li><li>5.0 PCIe Lanes: 128 lanes (on 18-cores-and-up SKUs), 80 lanes (on 16-cores-and-below SKUs)</li></ul><p>The platform also provides substantial expansion headroom. Intel Xeon 600 processors for workstations feature 128 PCIe 5.0 lanes on the majority of SKUs, with 80 PCIe 5.0 lanes on lower core count models (16 cores and below).</p><p>In addition to supporting more GPUs, storage devices, and networking, these PCIe lanes can also be used for added memory via CXL 2.0.</p></div><!----><!----><!----><!----></div><div class="group mb-4"><span class="mobile-observe" id="IntelXeon600WorkstationsforAI/ML,ScientificComputing,andEngineeringSimulation-mobile"></span><div class="text-block-code-block"><h2>Intel Xeon 600 Workstations for AI/ML, Scientific Computing, and Engineering Simulation</h2><p>Intel Xeon 600 workstations can be tailored to a range of compute‑heavy workloads. Across AI/ML, scientific research, and engineering simulation, the most important build variables tend to be CPU core count, memory capacity and bandwidth, and PCIe expansion for GPUs, storage, and networking.</p><p>
332Key considerations by workload:</p><ul><li><strong>AI and Machine Learning</strong><ul><li>Higher‑core Xeon 600 SKUs for data preprocessing, orchestration, and mixed CPU/GPU pipelines.</li><li>Large DDR5 memory footprints to keep datasets and intermediate results in memory.</li><li>Enough PCIe 5.0 lanes for dual or quad high‑end GPUs, plus NVMe storage and network adapters.</li></ul></li><li><strong>Scientific Computing Research</strong><ul><li>Strong multi‑core performance for molecular dynamics, cryo‑EM processing, and materials simulations.</li><li>Multi‑channel DDR5 configurations tuned for bandwidth and total capacity.</li><li>Expansion room for accelerator cards and high‑performance storage for large datasets and output files.</li></ul></li><li><strong>Engineering Simulation</strong><ul><li>Mid‑ to high‑core SKUs to support parallel solvers (CFD, FEA, particle methods).</li><li>Memory sized to keep meshes and particle sets in RAM during computation.</li><li>PCIe resources reserved for GPU accelerators (when supported) and fast NVMe scratch/results storage.</li></ul></li></ul></div><!----><!----><!----><!----></div><div class="group mb-4"><span class="mobile-observe" id="Conclusion:MatchingXeon600toRealRequirements-mobile"></span><div class="text-block-code-block"><h2>Conclusion: Matching Xeon 600 to Real Requirements</h2><p>The Intel Xeon 600 Processors for workstations will be another CPU option alongside the AMD Threadripper PRO for high-performance computing.</p><p>As a solutions integrator, our role is to translate platform capabilities into concrete system configurations. We look at core count, memory channels and capacity, GPU count, storage layout, and network requirements, then align those with the actual models, simulations, and datasets you plan to run.</p><p>A workstation can power the majority of the workloads for most individuals in AI research, scientific computing, and engineering simulation. Intel Xeon 600</p><p>Talk to our engineering team about your code, typical problem sizes, and preferred software stack, and our team at Exxact is more than happy to <a href="/Exxact-VWS-130523500-E130523500" target="_blank">build your ideal Xeon 600 configuration</a> that is sized appropriately and fits your budget.</p></div><!----><!----><!----><!----></div><div class="group mb-4"><span class="mobile-observe" id="-mobile"></span><!----><!----><!----><div class="color-block-mobile"><div class="color-block-container" style="background-image: url(https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0adfffce84eb3abb/65cbbd5323dbef12a7ffeb92/Background-Pattern.png);"><div class="image-container"><img src="https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt18434672773c5755/65cbbd6100d72e2e94f52f62/blog-color-block-img-ws.png"></div><!----><div class="text-container"><div><h4 class="bold">We're Here to Deliver the Tools to Power Your Research </h4><p>With access to the highest performing hardware, at Exxact, we can offer the platform optimized for your deployment, budget, and desired performance so you can make an impact with your research! </p><a href="/Contact-Us">Talk to an Engineer Today <i-feather name="chevron-right" _nghost-ng-c1868629123="" ngh="1"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" class="feather feather-chevron-right">
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341,"_in_progress":false,"created_at":"2020-08-31T20:22:17.797Z","created_by":"blt13a2948a46a0ad84","description":"","tags":[],"title":"News","updated_at":"2021-08-20T18:18:52.183Z","updated_by":"bltc816d579f2d0d71a","url":"/blog/News","_content_type_uid":"exx_blog_category","publish_details":{"environment":"bltfe7355d60efbec6e","locale":"en-us","time":"2021-08-20T18:18:53.082Z","user":"bltc816d579f2d0d71a"},"routerLink":"/blog/News","queryParams":{}}],"blog_content_v3":{"new_or_old_blog":"new_blog","blog_title":"AMD Instinct MI350P - Why PCIe Data Center GPUs Still Matter","enable_table_of_contents":true,"feature_image":{"feature_image_link":"","feature_image_file":null,"feature_image_alt":""},"section":{"group":[{"section_title":"Introduction","_metadata":{"uid":"cs4982782a51af42a7"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Cp>On May 7th, 2026, AMD released an addition to their data center GPU lineup: the AMD Instinct MI350P, a PCIe card! After seeing socketed data center GPUs from both leading GPU manufacturers, this is a welcomed edition to AMD Instinct’s lineup of GPUs and the first we’ve seen in nearly 5 years.\u003C/p>\u003Cp>The Instinct MI350P brings AMD’s current generation GPU architecture to the data center via a PCIe based GPU. Why offer a PCIe data center GPU in the midst of OAM integrated GPUs? AMD is targeting mainstream server architectures for those who want to do on-premises AI 
341inference, but either cannot high thermals and power density of OAM and SXM based nodes or cannot.\u003C/p>\u003Cp>If you are looking for AMD Instinct MI350P data center GPUs, talk to our sales engineers today for more information! \u003Ca href=\"/category/AMD-Instinct-Solutions\" target=\"_self\">Configure an Exxact TensorEX server with AMD Instinct GPUs today.\u003C/a>\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"csbe57c9a156e62c89"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>AMD Instinct MI350P Specifications\u003C/h2>\u003Cp>AMD is essentially taking one of its MI350X accelerators and cutting it in half, resulting in a card with half as many compute units and half as much memory at just over half the power consumption.\u003C/p>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>\u003Cstrong>GPU\u003C/strong>\u003C/th>\u003Cth>\u003Cstrong>MI350P\u003C/strong>\u003C/th>\u003Cth>\u003Cstrong>MI350X\u003C/strong>\u003C/th>\u003C/tr>\u003C/thead>\u003Ctbody>\u003Ctr>\u003Ctd>\u003Cstrong>Compute Units\u003C/strong>\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>256\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>\u003Cstrong>Matrix Cores\u003C/strong>\u003C/td>\u003Ctd>512\u003C/td>\u003Ctd>1024\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>\u003Cstrong>Peak Engine Clock\u003C/strong>\u003C/td>\u003Ctd>2200MHz\u003C/td>\u003Ctd>2200MHz\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>\u003Cstrong>Memory\u003C/strong>\u003C/td>\u003Ctd>144GB HBM3E\u003C/td>\u003Ctd>288GB HBM3E\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>\u003Cstrong>Memory Bandwidth\u003C/strong>\u003C/td>\u003Ctd>4TB/sec (8Gbps x 4096-bits)\u003C/td>\u003Ctd>8TB/sec (8Gbps x 8192-bits)\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>\u003Cstrong>Matrix Perf (MXFP8)\u003C/strong>\u003C/td>\u003Ctd>2.3 PFLOPS\u003C/td>\u003Ctd>4.6 PFLOPS\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>\u003Cstrong>I/O\u003C/strong>\u003C/td>\u003Ctd>PCIe Gen5 x16\u003C/td>\u003Ctd>
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These solutions deliver the best scale-up and scale-out performance through proprietary GPU interconnects. But they come with significant infrastructure requirements:\u003C/p>\u003Cul>\u003Cli>11kW per node\u003C/li>\u003Cli>Proprietary server design\u003C/li>\u003Cli>Low upgradability.\u003C/li>\u003C/ul>\u003Cp>This has left a gap in the market for customers who need serious AI hardware without the overhead of high-density non-modular systems. GPU vendors have attempted to address this gap with workstation-class products repurposed for the data center. While capable, these graphics-based GPUs fall short in key areas:\u003C/p>\u003Cul>\u003Cli>Lower peak performance compared to flagship server GPUs\u003C/li>\u003Cli>No HBM memory, limiting memory capacity and bandwidth\u003C/li>\u003Cli>Not a fully validated replacement for data center-class AI inference workloads\u003C/li>\u003C/ul>\u003Cp>To properly fill this gap, AMD is releasing the Instinct MI350P — its first Instinct PCIe accelerator built on its current-generation CDNA 4 architecture since the MI210 in 2022. The goal is straightforward: deliver flagship-class AI hardware to traditional server and data center customers in a standard, readily replaceable PCIe form factor that can plug into existing server designs.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs0613e7dbffcb8c9f"},"select":"image_block","images_optional":{"image":[{"image_enlargement":false,"_metadata":{"uid":"csff4c9fb00014e30b"},"image_header":"","image_link":"","image_file":{"uid":"blt18796290f632f310","_version":1,"title":"EXX-Blog-amd-instinct-mi350i-2.jpg","parent_uid":"bltca9c53775898e821","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-07-08T22:30:08.783Z","updated_at":"2026-07-08T22:30:08.783Z","content_type":"image/jpeg","file_size":"71398","filename":"EXX-Blog-amd-instinct-mi350i-2.jpg","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-07-08T23:39:33.489Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt18796290f632f310/6a4ecf70c3e19c411e3353f6/EXX-Blog-amd-instinct-mi350i-2.jpg","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt18796290f632f310/6a4ecf70c3e19c411e3353f6/EXX-Blog-amd-instinct-mi350i-2.jpg","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt18796290f632f310/6a4ecf70c3e19c411e3353f6/EXX-Blog-amd-instinct-mi350i-2.webp","queryParams":{}},"image_alt_text":"AMD Instinct MI350P front image","ad_link":"","image_caption_subheader":""}]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Instinct MI350P: Half a MI350X At Half the Power","_metadata":{"uid":"cs78ef5b7cb0fa25fd"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Instinct MI350P: Half a MI350X At Half the Power\u003C/h2>\u003Cp>The MI350P is effectively a precision half-slice of AMD’s flagship MI350X. Where the MI350X uses two I/O dies (IODs), each stacked with four accelerator complex dies (XCDs), for eight total XCDs, the MI350P uses a single IOD with four XCDs clocked identically to the MI350X. The result is roughly half the compute performance in a PCIe GPU.\u003C/p>\u003Cp>The die reduction scales evenly across memory as well:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Memory:\u003C/strong> 144GB HBM3e (half the XCDs mean half the 288GB on the MI350X)\u003C/li>\u003Cli>\u003Cstrong>Memory Bandwidth:\u003C/strong> 4TB/s (half of the MI350X’s 8TB/s)\u003C/li>\u003Cli>\u003Cstrong>Peak Matrix Performance (MXFP8):\u003C/strong> 2.3 PFLOPS vs. 4.6 PFLOPS\u003C/li>\u003C/ul>\u003Cp>Physically, the MI350P is a standard full-height, full-length (FHFL) dual-slot PCIe card available in two TBP configurations: 600W standard and 450W optional. It is designed for air cooling and targets mainstream server form factors.\u003C/p>\u003Cp>AMD did not include Infinity Fabric GPU-to-GPU interconnect support on the MI350P. Multi-GPU setups are limited to PCIe Gen5 x16 communication between cards, which caps usable AI model sizes to what fits within the 144GB of VRAM. This is the biggest difference from its MI350X OAM counterpart.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"FAQ on AMD Instinct MI350P","_metadata":{"uid":"csb602c3aeaa840536"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>FAQ on AMD Instinct MI350P\u003C/h3>\u003Ch4>What is the AMD Instinct MI350P?\u003C/h4>\u003Cp>The AMD Instinct MI350P is a data center GPU built in a standard PCIe add-in card form factor. It brings AMD’s CDNA 4 architecture, 144GB of HBM3E memory, and 2.3 PFLOPS of MXFP8 matrix performance to mainstream server platforms without requiring an OAM-based accelerator node.\u003C/p>\u003Ch4>What workloads is the AMD Instinct MI350P designed for?\u003C/h4>\u003Cp>The AMD Instinct MI350P is designed for enterprise AI inference, generative AI, retrieval-augmented generation (RAG), small and large language model deployment, and other accelerator-driven data center workloads. Its 144GB of HBM3E memory gives it enough capacity for many on-premises inference deployments while staying within a standard PCIe server design.\u003C/p>\u003Ch4>How much power does the AMD Instinct MI350P use?\u003C/h4>\u003Cp>The AMD Instinct MI350P has a 600W total board power configuration, with an optional 450W mode for systems with tighter power or thermal requirements. It is a passive, air-cooled full-height, full-length dual-slot PCIe card 
341intended for compatible server platforms.\u003C/p>\u003Ch4>Who should consider using the AMD Instinct MI350P?\u003C/h4>\u003Cp>The AMD Instinct MI350P is a strong fit for organizations that want to deploy on-premises AI infrastructure but do not need, or cannot support, the density and power requirements of OAM or SXM GPU systems. It is especially relevant for teams looking to add high-capacity AI acceleration to existing PCIe-based server environments.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Conclusion","_metadata":{"uid":"csf004b030eb80c330"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Conclusion\u003C/h2>\u003Cp>AMD has made significant strides in the data center GPU market, and with the Instinct MI350P, the company is finally returning to a long-underserved segment: the standard PCIe form factor. This is AMD's first Instinct PCIe accelerator built on its current-generation architecture in nearly five years, arriving at a time when demand for accessible, high-performance AI hardware is growing rapidly.\u003C/p>\u003Cp>The MI350P is a purpose-built product, not a compromise. It won't match the raw throughput of the MI350X, but it delivers the highest performance AMD can package into a standard PCIe card — complete with CDNA 4 architecture, HBM3E memory, and data center-class capabilities — in a form factor that fits the infrastructure most organizations already have.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs933e24e874c330bc"},"select":"ad_block","images_optional":{"image":[]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[{"uid":"blt74922f1792f95854","_content_type_uid":"blog_ad_block"}]}]}},"blog_title":"AMD Instinct MI350P - Why PCIe Data Center GPUs Still Matter","created_at":"2026-07-08T22:58:48.461Z","created_by":"bltf69a05e284b3d18a","date":"2026-07-08T21:53:41.000Z","feature_image":{"uid":"bltd7341aaba39a9a6a","_version":1,"title":"EXX-Blog-amd-instinct-mi350i.jpg","parent_uid":"bltca9c53775898e821","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-07-08T21:55:03.987Z","updated_at":"2026-07-08T21:55:03.987Z","content_type":"image/jpeg","file_size":"143067","filename":"EXX-Blog-amd-instinct-mi350i.jpg","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-07-08T23:39:33.454Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltd7341aaba39a9a6a/6a4ec737fdb55694141042a1/EXX-Blog-amd-instinct-mi350i.jpg","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltd7341aaba39a9a6a/6a4ec737fdb55694141042a1/EXX-Blog-amd-instinct-mi350i.jpg","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltd7341aaba39a9a6a/6a4ec737fdb55694141042a1/EXX-Blog-amd-instinct-mi350i.webp","queryParams":{}},"seo_meta_data":{"title":"AMD Instinct MI350P - Why PCIe Data Center GPUs Still Matter | Exxact Blog","description":"AMD Instinct MI350P brings CDNA 4 performance to standard PCIe servers for on-prem AI inference without OAM infrastructure.","keywords":"AMD Instinct MI350P, MI350P PCIe GPU, AMD CDNA 4, data center PCIe accelerator, HBM3E GPU, AI inference on-prem, PCIe Gen5 x16 GPU, MI350X comparison, enterprise AI deployment, RAG inference hardware, GPU memory bandwidth, 600W PCIe GPU, passive air-cooled GPU, traditional server AI, Exxact TensorEX","og_title":"AMD Instinct MI350P - Why PCIe Data Center GPUs Still Matter | Exxact Blog","og_description":"AMD Instinct MI350P brings CDNA 4 performance to standard PCIe servers for on-prem AI inference without OAM infrastructure.","og_image":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltd7341aaba39a9a6a/6a4ec737fdb55694141042a1/EXX-Blog-amd-instinct-mi350i.jpg","collection":""},"tags":["amd","gpu","ai","instinct"],"title":"AMD Instinct MI350P - Why PCIe Data Center GPUs Still Matter","unique_pageviews":"","unique_quarterly_views":0,"updated_at":"2026-07-08T23:39:43.665Z","updated_by":"bltf69a05e284b3d18a","url":"/blog/news/amd-instinct-mi350p","publish_details":{"time":"2026-07-08T23:39:48.052Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"routerLink":"/blog/news/amd-instinct-mi350p","queryParams":{}},{"uid":"blt14b600f7b0f27dc9","locale":"en-us","_version":7,"ACL":{},"_in_progress":false,"ad_blocks_side":[],"article":"","blog_category":[{"_version":7,"locale":"en-us","uid":"blt30782cc2c304a058","ACL":{}
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Jensen Huang took the stage to cover everything from chip architecture and inference economics to open-source agentic frameworks and physical robotics — a lot of ground for one keynote.\u003C/p>\u003Cp>At Exxact, we pay close attention to GTC every year because what NVIDIA announces directly shapes the AI infrastructure landscape our customers are building on. This recap breaks down the highlights and what they mean for organizations investing in AI today.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"CUDA Turns 20. The Foundation Behind NVIDIA's Platform","_metadata":{"uid":"cs98cadaa35ae98d8b"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>CUDA Turns 20. The Foundation Behind NVIDIA's Platform\u003C/h3>\u003Cp>CUDA, NVIDIA's parallel computing platform, turned 20 this year. Jensen used the anniversary as a framing device for the rest of the keynote, most of what NVIDIA has built, and continues to build, traces back to this foundation.\u003C/p>\u003Cp>A few things worth noting about where CUDA stands today:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Installed base:\u003C/strong> Hundreds of millions of CUDA-capable GPUs are deployed across every major cloud provider, OEM, and industry vertical.\u003C/li>\u003Cli>\u003Cstrong>Backward compatibility:\u003C/strong> NVIDIA continues to optimize its software stack across older architectures, meaning deployed hardware gains performance improvements over time without replacement.\u003C/li>\u003Cli>\u003Cstrong>Ecosystem scale:\u003C/strong> Thousands of libraries, frameworks, and tools are built on CUDA, with hundreds of thousands of public projects dependent on it.\u003C/li>\u003Cli>\u003Cstrong>Library downloads\u003C/strong> are growing faster than ever, reflecting the expanding range of workloads CUDA now supports, from traditional HPC to AI training, inference, and data processing.\u003C/li>\u003C/ul>\u003Cp>The core point Jensen made is that the value of NVIDIA's platform isn't just the hardware, it's the compounding effect of two decades of software, tooling, and developer adoption built on top of it. That installed base is why workloads running on older NVIDIA hardware, like Ampere, are still seeing rising cloud pricing despite being several generations old.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"The Inference Inflection Point","_metadata":{"uid":"cs63de6390d3a1f616"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>The Inference Inflection Point\u003C/h3>\u003Cp>For most of NVIDIA's history, training the model was the dominant and focus in AI workload. As of recently, that has shifted. Jensen made the case that inference is now the primary driver of compute demand and that the economics of AI businesses are increasingly defined by how efficie
341ntly they can generate tokens. The more tokens you can hold in memory, the faster you can process these tokens, and the faster you can generate tokens, the smarter the AI.\u003C/p>\u003Cp>Three developments over the past two years drove this shift:\u003C/p>\u003Cul>\u003Cli>\u003Ca href=\"/blog/deep-learning/generative-ai-use-cases\" target=\"_blank\">\u003Cstrong>Generative AI (2022–2023):\u003C/strong>\u003C/a> Models moved from perception and classification to content generation, fundamentally changing how compute is consumed.\u003C/li>\u003Cli>\u003Cstrong>Reasoning models (2024):\u003C/strong> Models like OpenAI's o1 and o3 introduced chain-of-thought processing — the model \"thinks\" before responding, which significantly increases both input and output token usage per query.\u003C/li>\u003Cli>\u003Ca href=\"/blog/deep-learning/agentic-ai-ai-that-reasons\" target=\"_blank\">\u003Cstrong>Agentic AI (2024–2025):\u003C/strong>\u003C/a> Tools like Claude Code enabled models to autonomously read files, write and test code, and iterate on tasks. This moved AI from answering questions to completing multi-step work, multiplying compute demand further.\u003C/li>\u003C/ul>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs5399f547ea5cba7c"},"select":"image_block","images_optional":{"image":[{"image_enlargement":true,"_metadata":{"uid":"cs1b252c9a6bd3ad7f"},"image_header":"","image_link":"","image_file":{"uid":"blt999e42dfcd3ce4c2","_version":1,"parent_uid":"blt50d5c471d4007ad8","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-03-17T23:18:02.939Z","updated_at":"2026-03-17T23:18:02.939Z","content_type":"image/png","file_size":"1007061","filename":"EXX-Blog-NVDIA-GTC-2026-Recap-1.png","title":"EXX-Blog-NVDIA-GTC-2026-Recap-1.png","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-03-18T00:01:25.147Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt999e42dfcd3ce4c2/69b9e12aee827a62c22052aa/EXX-Blog-NVDIA-GTC-2026-Recap-1.png","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt999e42dfcd3ce4c2/69b9e12aee827a62c22052aa/EXX-Blog-NVDIA-GTC-2026-Recap-1.png","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt999e42dfcd3ce4c2/69b9e12aee827a62c22052aa/EXX-Blog-NVDIA-GTC-2026-Recap-1.webp","queryParams":{}},"image_alt_text":"NVIDIA graph showing Tokens per Watt and Performance against Cost","ad_link":"","image_caption_subheader":"Source: NVIDIA"}]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs64ad85c2b029f292"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Cp>The cumulative effect: Jensen estimated that compute demand per AI workload has increased roughly 10,000x over two years, while overall usage has grown approximately 100x. This implying total compute demand on the order of 1 million times greater than two years ago.\u003C/p>\u003Cp>Jensen framed this shift with a concept worth understanding: the AI data center as a token factory.\u003C/p>\u003Cul>\u003Cli>Every data center has a fixed power ceiling; once built, that constraint doesn't change.\u003C/li>\u003Cli>The relevant metric for an AI operator is therefore \u003Cstrong>tokens per watt\u003C/strong> or how much useful AI output you can generate from your available power budget.\u003C/li>\u003Cli>A second axis matters equally: \u003Cstrong>token speed\u003C/strong> (latency per inference), which determines what tiers of service you can offer and at what price points.\u003C/li>\u003Cli>Higher token speed enables larger models, longer context windows, and more reasoning, all of which command higher pricing.\u003C/li>\u003C/ul>\u003Cp>
341This framing has direct implications for infrastructure decisions. The architecture you deploy determines your token throughput and token speed at a fixed power envelope which maps directly to revenue potential. Jensen's point was that this is how AI infrastructure ROI should be evaluated going forward, not just raw compute specs.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Vera Rubin: NVIDIA's Next-Generation AI Platform","_metadata":{"uid":"csb6c3834384563797"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Vera Rubin: NVIDIA's Next-Generation AI Platform\u003C/h2>\u003Cp>Blackwell is currently in full production and shipping at scale, but NVIDIA used GTC to formally introduce Vera Rubin, the next-generation platform architected around the demands of agentic AI workloads. The first Vera Rubin rack is already operational at Microsoft Azure.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs30c022cfca8b566f"},"select":"image_block","images_optional":{"image":[{"image_enlargement":true,"_metadata":{"uid":"cs0ae6a44ae0b9bda6"},"image_header":"","image_link":"","image_file":{"uid":"bltcf1b72bbcc2110b9","_version":1,"parent_uid":"blt50d5c471d4007ad8","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-03-17T23:18:02.945Z","updated_at":"2026-03-17T23:18:02.945Z","content_type":"image/png","file_size":"1025145","filename":"EXX-Blog-NVDIA-GTC-2026-Recap-2.png","title":"EXX-Blog-NVDIA-GTC-2026-Recap-2.png","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-03-18T00:01:25.199Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltcf1b72bbcc2110b9/69b9e12a321ab50a356036fc/EXX-Blog-NVDIA-GTC-2026-Recap-2.png","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltcf1b72bbcc2110b9/69b9e12a321ab50a356036fc/EXX-Blog-NVDIA-GTC-2026-Recap-2.png","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltcf1b72bbcc2110b9/69b9e12a321ab50a356036fc/EXX-Blog-NVDIA-GTC-2026-Recap-2.webp","queryParams":{}},"image_alt_text":"NVIDIA Vera Rubin Generation","ad_link":"","image_caption_subheader":""}]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"csca90253d92a96f2a"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>NVIDIA Vera Rubin NVL72\u003C/h3>\u003Cp>Vera Rubin is designed as a full-system platform, not just a new GPU. Key specs:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>3.6 exaflops\u003C/strong> of compute per rack\u003C/li>\u003Cli>\u003Cstrong>260 TB/s\u003C/strong> all-to-all NVLink bandwidth\u003C/li>\u003Cli>\u003Cstrong>72 GPUs\u003C/strong> connected via sixth-generation NVLink\u003C/li>\u003Cli>Fully liquid-cooled, including NVLink switching infrastructure\u003C/li>\u003Cli>Hot water cooling at 45°C, reducing data center cooling overhead\u003C/li>\u003C/ul>\u003Ch3>NVIDIA Groq 3 LPX\u003C/h3>\u003Cp>One of the more significant announcements was NVIDIA's acquisition of the Groq chip team and integration of the Groq 3 LPX into the Vera Rubin platform. The two chips serve fundamentally different roles:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Vera Rubin GPU:\u003C/strong> High memory capacity (288GB per chip), optimized for throughput-heavy workloads and KV cache storage\u003C/li>\u003Cli>\u003Cstrong>Groq 3 LPX:\u003C/strong> Massive on-chip SRAM, statically compiled, deterministic data flow optimized for low-latency token generation (decode)\u003C/li>\u003C/ul>\u003Cp>NVIDIA's inference orchestration software, Dynamo, splits the workload between them: Vera Rubin handles the prefill and attention portions of inference, while Groq handles the token generation (feedforward decode) stage. The result is \u003Cstrong>35x more throughput per megawatt\u003C/strong> compared to running Vera Rubin alone for latency-sensitive workloads.\u003C/p>\u003Ch3>NVIDIA Rubin Ultra\u003C/h3>\u003Cp>Beyond the standard Vera Rubin configuration, NVIDIA also announced Rubin Ultra, a higher-density variant using a new rack design called Kyber:\u003C/p>\u003Cul>\u003Cli>Connects \u003Cstrong>144 GPUs\u003C/strong> in a single NVLink domain (vs. 72 in standard Vera Rubin)\u003C/li>\u003Cli>Compute nodes slide in vertically and connect via a midplane backplane rather than copper cabling\u003C/li>\u003Cli>Enables NVLink scaling beyond what copper interconnects can support\u003C/li>\u003C/ul>\u003Cp>These two metric, throughput and token speed, map directly to the revenue potential of a given infrastru
341cture deployment. NVIDIA presented generation-over-generation comparisons at a fixed 1 gigawatt power envelope:\u003C/p>\u003Cul>\u003Cli>Blackwell delivered a substantial improvement over Hopper, and Vera Rubin is projected to deliver approximately 5x more revenue potential per gigawatt over Blackwell\u003C/li>\u003Cli>The architecture you deploy determines your output capacity at a fixed power budget, which sets a ceiling on what you can generate from that 1 gigawatt limit.\u003C/li>\u003C/ul>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs62f483521585759c"},"select":"ad_block","images_optional":{"image":[]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[{"uid":"blt9e79a738382ac083","_content_type_uid":"blog_ad_block"}]},{"section_title":"Hardware Roadmap","_metadata":{"uid":"cs66a335220dacb3b0"},"select":"image_block","images_optional":{"image":[{"image_enlargement":true,"_metadata":{"uid":"cs3becb1ec8f457075"},"image_header":"","image_link":"","image_file":{"uid":"blt7a1366926599577a","_version":1,"parent_uid":"blt50d5c471d4007ad8","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-03-17T23:18:02.954Z","updated_at":"2026-03-17T23:18:02.954Z","content_type":"image/png","file_size":"1166068","filename":"EXX-Blog-NVDIA-GTC-2026-Recap-3.png","title":"EXX-Blog-NVDIA-GTC-2026-Recap-3.png","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-03-18T00:01:25.129Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt7a1366926599577a/69b9e12a699c06f5081da19e/EXX-Blog-NVDIA-GTC-2026-Recap-3.png","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt7a1366926599577a/69b9e12a699c06f5081da19e/EXX-Blog-NVDIA-GTC-2026-Recap-3.png","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt7a1366926599577a/69b9e12a699c06f5081da19e/EXX-Blog-NVDIA-GTC-2026-Recap-3.webp","queryParams":{}},"image_alt_text":"NVIDIA hardware roadmap including Blackwell, Vera Rubin, Feynman","ad_link":"","image_caption_subheader":""}]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"csd70d55364d74bd47"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>Vera Rubin (current/near-term)\u003C/h3>\u003Cp>Two configurations will be available:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Standard Vera Rubin (Kyber rack):\u003C/strong> NVLink 144 with copper Scale-up\u003C/li>\u003Cli>\u003Cstrong>Vera Rubin with Oberon:\u003C/strong> NVLink 72 plus optical Scale-up, expandable to NVLink 576 via co-packaged optics and Spectrum 6 switching\u003C/li>\u003C/ul>\u003Cp>Both are in production. NVIDIA also noted the Vera CPU is already selling as a standalone product with high interest.\u003C/p>\u003Ch3>Rubin Ultra (next generation)\u003C/h3>\u003Cul>\u003Cli>New Kyber rack design supporting 144 GPUs in a single NVLink domain\u003C/li>\u003Cli>Introduces the Groq LP 35 chip, which will incorporate NVIDIA's MVRP compute structure for additional performance gains\u003C/li>\u003Cli>Co-packaged optics available for both Scale-up and Scale-out networking\u003C/li>\u003C/ul>\u003Ch3>Feynman (future generation)\u003C/h3>\u003Cul>\u003Cli>New GPU architecture paired with the Groq LP 40\u003C/li>\u003Cli>New CPU called Rosa (Rosalind), paired with BlueField 5 and ConnectX-10\u003C/li>\u003Cli>Both copper and co-packaged optical Scale-up supported\u003C/li>\u003Cli>Continues the annual architecture cadence NVIDIA has committed to\u003C/li>\u003C/ul>\u003Ch3>Key components shipping across generations\u003C/h3>\u003Cul>\u003Cli>\u003Cstrong>Vera CPU:\u003C/strong> Designed for high single-threaded performance and data processing workloads, using LPDDR5 memory for efficiency. Targeted at agentic orchestration and tool-use workloads\u003C/li>\u003Cli>\u003Cstrong>BlueField 4:\u003C/strong> New storage and networking DPU, with 100% of major storage vendors committed to the platform\u003C/li>\u003Cli>\u003Cstrong>Spectrum X with co-packaged optics:\u003C/strong> World's first CPO Ethernet switch, in full production\u003C/li>\u003Cli>\u003Cstrong>ConnectX 9:\u003C/strong> Updated networking paired with Vera CPU\u003C/li>\u003C/ul>\u003Cp>NVIDIA's broader point on the roadmap is that the platform is designed for c
341ontinuity. Each generation maintains backward compatibility, and the shift from copper to optical interconnects will happen incrementally across both Scale-up and Scale-out rather than as a hard cutover.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"AI Factories and Industry Adoption","_metadata":{"uid":"cs4ffc20d49be0ea67"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>AI Factories and Industry Adoption\u003C/h2>\u003Cp>A recurring theme throughout the keynote was NVIDIA's expansion beyond chips and systems into the infrastructure layer that surrounds them. Two areas reflect this most clearly: the tooling NVIDIA has built for designing and operating AI factories, and the breadth of industries now building on NVIDIA platforms.\u003C/p>\u003Ch3>NVIDIA DSX: Digital Twin Platform for AI Factories\u003C/h3>\u003Cp>As AI factories grow in scale and complexity, NVIDIA introduced DSX, an Omniverse-based digital twin platform built for designing, commissioning, and operating large-scale AI infrastructure. The core idea is that the various technology vendors involved in building a data center, cooling, power, networking, compute, need a shared environment to co-design before anything is physically built.\u003C/p>\u003Cp>DSX covers the full facility lifecycle:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Design and simulation:\u003C/strong> Thermal, electrical, mechanical, and network simulation using tools from partners including Siemens, Cadence, and Dassault Systemes\u003C/li>\u003Cli>\u003Cstrong>Virtual commissioning:\u003C/strong> Facilities can be validated digitally through Procore before construction begins, reducing delays\u003C/li>\u003Cli>\u003Cstrong>Live operations:\u003C/strong> Once a facility is running, the digital twin becomes an operational tool. AI agents monitor cooling, electrical systems, and grid signals in real time, feeding into NVIDIA's Max-Q system which dynamically adjusts compute workloads to maximize token throughput within power constraints\u003C/li>\u003C/ul>\u003Cp>The underlying argument is that at gigawatt scale, inefficiencies in how a facility is designed and operated translate directly to lost token output and lost revenue. NVIDIA estimates there is roughly a factor of two in recoverable efficiency across a typical AI factory deployment.\u003C/p>\u003Ch3>Industry Verticals\u003C/h3>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs34215fcc0189270b"},"select":"image_block","images_optional":{"image":[{"image_enlargement":true,"_metadata":{"uid":"cs16ee1ff54a704a36"},"image_header":"","image_link":"","image_file":{"uid":"blt849462342c13c9b5","_version":1,"parent_uid":"blt50d5c471d4007ad8","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-03-17T23:18:02.870Z","updated_at":"2026-03-17T23:18:02.870Z","content_type":"image/png","file_size":"922384","filename":"EXX-Blog-NVDIA-GTC-2026-Recap-4.png","title":"EXX-Blog-NVDIA-GTC-2026-Recap-4.png","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-03-18T00:01:25.183Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt849462342c13c9b5/69b9e12aee827a6a312052a6/EXX-Blog-NVDIA-GTC-2026-Recap-4.png","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt849462342c13c9b5/69b9e12aee827a6a312052a6/EXX-Blog-NVDIA-GTC-2026-Recap-4.png","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt849462342c13c9b5/69b9e12aee827a6a312052a6/EXX-Blog-NVDIA-GTC-2026-Recap-4.webp","queryParams":{}},"image_alt_text":"NVIDIA Industry Presence: Automotive, Financial, Healthcare, Industrial, Quantum, Retail, Robotics, Telco","ad_link":"","image_caption_subheader":""}]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs011637c5c55ffedb"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Cp>Jensen covered the range of industries now building on NVIDIA infrastru
341cture, with each relying on domain-specific Cuda-X libraries rather than general-purpose compute alone:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Financial services:\u003C/strong> The largest represented industry at GTC this year. Algorithmic trading is shifting from classical quantitative methods to deep learning models that discover patterns across large datasets without human feature engineering\u003C/li>\u003Cli>\u003Cstrong>Healthcare:\u003C/strong> Drug discovery, diagnostic AI agents, and physical AI robotics for clinical settings. NVIDIA cited this as going through its \"ChatGPT moment\" in terms of adoption velocity\u003C/li>\u003Cli>\u003Cstrong>Automotive:\u003C/strong> Self-driving and robotaxi platforms, covered in more detail in the robotics section below\u003C/li>\u003Cli>\u003Cstrong>Telecom:\u003C/strong> NVIDIA's Aerial platform enables AI-RAN, converting traditional base stations into AI inference infrastructure at the edge. Active partnerships with Nokia and T-Mobile\u003C/li>\u003Cli>\u003Cstrong>Industrial and manufacturing:\u003C/strong> Simulation-driven robot deployment for factory automation, with integration across major industrial automation vendors\u003C/li>\u003Cli>\u003Cstrong>Media and entertainment:\u003C/strong> Real-time AI for broadcast, live translation, and gaming, built on NVIDIA's Holoscan platform\u003C/li>\u003Cli>\u003Cstrong>Retail and CPG:\u003C/strong> Supply chain optimization and AI agents for customer support across a $35 trillion industry\u003C/li>\u003Cli>\u003Cstrong>Quantum computing:\u003C/strong> 35 companies at GTC building hybrid GPU-quantum systems using NVIDIA's cuQuantum platform\u003C/li>\u003C/ul>\u003Cp>The breadth here reflects a consistent pattern in how NVIDIA approaches verticals. Rather than positioning GPUs as general-purpose accelerators, each domain gets purpose-built libraries, Cuda-X, that solve specific algorithmic problems in that field. Jensen described these libraries as the core of what differentiates NVIDIA's platform long term.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"OpenClaw and the Agentic AI Framework","_metadata":{"uid":"csc98f7dd9d6287b47"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>OpenClaw and the Agentic AI Framework\u003C/h2>\u003Cp>One of the more significant announcements at GTC was NVIDIA's support for OpenClaw, an open-source agentic AI framework that has seen unusually rapid adoption since its release. Jensen described it as the fastest adopted open-source project in history, surpassing Linux's 30-year install base within weeks.\u003C/p>\u003Ch3>What is OpenClaw?\u003C/h3>\u003Cp>OpenClaw functions as an operating system for AI agents. The components map closely to what a traditional OS provides:\u003C/p>\u003Cul>\u003Cli>Resource management across file systems, tools, and language models\u003C/li>\u003Cli>Task scheduling and cron job execution\u003C/li>\u003Cli>Problem decomposition: breaking a prompt into sequential steps\u003C/li>\u003Cli>Sub-agent spawning for parallel workstreams\u003C/li>\u003Cli>Multimodal I/O including text, voice, and messaging\u003C/li>\u003C/ul>\u003Cp>The practical result is that anyone can spin up a functioning AI agent by running a single command. The agent connects to a language model, receives a task, and executes it autonomously across tools and systems.\u003C/p>\u003Cp>The capability that makes OpenClaw useful also creates security risk in a corporate environment. An agentic system with access to internal infrastructure can read sensitive data, execute code, and communicate externally. NVIDIA worked with OpenClaw's author to address this with an enterprise-ready reference design called NeMo Claw, which adds:\u003C/p>\u003Cul>\u003Cli>A policy engine for governing agent behavior\u003C/li>\u003Cli>Network guardrails\u003C/li>\u003Cli>A privacy router to prevent unauthorized data exfiltration\u003C/li>\u003Cli>Integration hooks for existing enterprise SaaS policy systems\u003C/li>\u003C/ul>\u003Cp>NeMo Claw is available to download and deploy and is designed to connect with the policy engines that enterprise softw
341are vendors already maintain.\u003C/p>\u003Cp>Jensen's framing was that OpenClaw represents the same kind of inflection point for enterprise software that Linux, HTML, and Kubernetes each represented in prior computing eras. The argument is straightforward: just as every company eventually needed a Linux strategy, a web strategy, and a cloud-native strategy, every company now needs an agentic AI strategy.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Open Frontier Models for Every Industry","_metadata":{"uid":"csa4744a3c8278f2b2"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Open Frontier Models for Every Industry\u003C/h2>\u003Cp>Alongside its hardware and infrastructure announcements, NVIDIA outlined its position as a contributor to open AI models. Rather than a single general-purpose model, NVIDIA is developing and releasing six domain-specific model families, each targeting a different field.\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Nemotron:\u003C/strong> Language models covering reasoning, visual understanding, retrieval-augmented generation, safety, and speech. Nemotron-3 Ultra is positioned as a base model for fine-tuning and sovereign AI deployments. Nemotron-4 is in active development\u003C/li>\u003Cli>\u003Cstrong>Cosmos:\u003C/strong> World foundation models for physical AI, focused on generating and understanding synthetic environments for robotics and autonomous systems training\u003C/li>\u003Cli>\u003Cstrong>Alpamayo:\u003C/strong> Described as the world's first reasoning-capable autonomous vehicle AI. Handles real-time decision making, narration of driving actions, and instruction following\u003C/li>\u003Cli>\u003Cstrong>Groot:\u003C/strong> Foundation models for general-purpose humanoid and industrial robotics, covering whole-body control, manipulation, and policy generation\u003C/li>\u003Cli>\u003Cstrong>BioNeMo:\u003C/strong> Open models for biology, chemistry, and molecular design, targeting drug discovery and life sciences research\u003C/li>\u003Cli>\u003Cstrong>Earth2:\u003C/strong> Weather and climate forecasting models grounded in AI physics simulation\u003C/li>\u003C/ul>\u003Cp>Each family is being actively maintained and updated. NVIDIA framed the ongoing investment in these models, rather than the models themselves, as the core value proposition for organizations building on them.\u003C/p>\u003Cp>A thread running through this section of the keynote was sovereign AI, the ability for individual countries and organizations to build and run their own models rather than depending on a small number of large external providers. NVIDIA's open model initiative, combined with its on-premises infrastructure options, is positioned as the technical foundation for that capability.\u003C/p>\u003Ch3>Robotics and Physical AI\u003C/h3>\u003Cp>Jensen dedicated a significant portion of the keynote to robotics, framing it as the next major frontier for AI after language and reasoning models. The core argument is that the same capabilities that enabled agentic AI in software, perception, reasoning, and action, now need to be extended to systems operating in the physical world.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs12335e6c743399fd"},"select":"image_block","images_optional":{"image":[{"image_enlargement":true,"_metadata":{"uid":"csacd626856befb8c0"},"image_header":"","image_link":"","image_file":{"uid":"blt8f8b1652937c4b7b","_version":1,"parent_uid":"blt50d5c471d4007ad8","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-03-17T23:18:03.058Z","updated_at":"2026-03-17T23:18:03.058Z","content_type":"image/png","file_size":"2397923","filename":"EXX-Blog-NVDIA-GTC-2026-Recap-5.png","title":"EXX-Blog-NVDIA-GTC-2026-Recap-5.png","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-03-18T00:01:25.228Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt8f8b1652937c4b7b/69b9e12b67be9e069d70bdaa/EXX-Blog-NVDIA-GTC-2026-Recap-5.png","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt8f8b1652937c4b7b/69b9e12b67be9e069d70bdaa/EXX-Blog-NVDIA-GTC-2026-Recap-5.png","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt8f8b1652937c4b7b/69b9e12b67be9e069d70bdaa/EXX-Blog-NVDIA-GTC-2026-Recap-5.webp","queryParams":{}},"image_alt_text":"NVIDIA's range in Robotics and Automotive","ad_link":"","
341image_caption_subheader":""}]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs0a50e67ddfd9d901"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Cp>NVIDIA provides three distinct computers for robotics development:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Training computer:\u003C/strong> For developing and training robot AI models at scale via DGX Rubin\u003C/li>\u003Cli>\u003Cstrong>Simulation computer:\u003C/strong> For synthetic data generation using Isaac Lab, Newton physics simulation, and Cosmos world models via MGX and RTX PRO.\u003C/li>\u003Cli>\u003Cstrong>Edge computer:\u003C/strong> The onboard compute that runs inside the robot itself via Jetson and Thor.\u003C/li>\u003C/ul>\u003Cp>The simulation layer is particularly important because real-world data collection alone is insufficient to train robots across the full range of edge cases they will encounter in deployment.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"AI Infrastructure Planning for the Inference Era","_metadata":{"uid":"csdd2bb02f7000564d"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>AI Infrastructure Planning for the Inference Era\u003C/h2>\u003Cp>GTC 2026 reinforced that the shift from training to inference as the dominant AI workload has real implications for how infrastructure should be evaluated. Tokens per watt and token speed at a fixed power budget are increasingly the metrics that determine what a deployment can deliver. That framing should inform hardware decisions being made today.\u003C/p>\u003Cp>\u003Ca href=\"/category/NVIDIA-Blackwell\" target=\"_blank\">NVIDIA Blackwell\u003C/a> is the right platform to deploy now. It is in full production, the software stack is mature, and the ecosystem is well established. Vera Rubin is sampling well with broad availability ahead, and the Rubin Ultra to Feynman roadmap signals that NVIDIA's annual architecture cadence is not slowing down. Planning around that cadence, rather than waiting for it, is the more practical approach for most deployment timelines.\u003C/p>\u003Cp>As an NVIDIA Elite Partner and solution integrator, \u003Ca href=\"/\" target=\"_blank\">Exxact Corporation\u003C/a> works with organizations navigating these infrastructure decisions. From \u003Ca href=\"/category/Deep-Learning-Solutions\" target=\"_blank\">GPU servers and workstations\u003C/a> to full \u003Ca href=\"/category/Rack-Scale-HPC-Clusters\" target=\"_blank\">rack-scale deployments\u003C/a>, our team can help identify the right configuration for your workload and budget.\u003C/p>\u003Cp>&nbsp;\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]}]}},"blog_title":"NVIDIA GTC 2026 Recap - Focusing on The Era of Tokens & Inference ","created_at":"2026-03-18T00:00:52.433Z","created_by":"bltf69a05e284b3d18a","date":"2026-03-17T22:49:22.000Z","feature_image":{"uid":"bltb0df289c713c0450","_version":1,"parent_uid":"blt50d5c471d4007ad8","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-03-17T23:18:02.625Z","updated_at":"2026-03-17T23:18:02.625Z","content_type":"image/jpeg","file_size":"221971","filename":"EXX-Blog-NVDIA-GTC-2026-Recap.jpg","title":"EXX-Blog-NVDIA-GTC-2026-Recap.jpg","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-03-18T00:01:25.164Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltb0df289c713c0450/69b9e12a42c8c41bfa342b43/EXX-Blog-NVDIA-GTC-2026-Recap.jpg","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltb0df289c713c0450/69b9e12a42c8c41bfa342b43/EXX-Blog-NVDIA-GTC-2026-Recap.jpg","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltb0df289c713c0450/69b9e12a42c8c41bfa342b43/EXX-Blog-NVDIA-GTC-2026-Recap.webp","queryParams":{}},"seo_meta_data":{"title":"NVIDIA GTC 2026 Recap: Tokens & Inference | Exxact Blog","description":"Recap NVIDIA GTC 2026 with Vera Rubin/Groq, Era of Inference, AI factories, OpenClaw agents, and robotics.","keywords":"NVIDIA GTC 2026 recap, NVIDIA Vera Rubin, NVIDIA Rubin Ultra, AI inference economics, tokens per watt, token speed, GPU roadmap 2026, NVLink bandwidth, AI data center token factory, Dynamo inference orchestration, Groq LPX, AI factory digital twin DSX, OpenClaw agentic framework, NeMo Claw enterprise security, NVIDIA robotics and physical AI","og_title":"NVIDIA GTC 2026 Recap: Tokens & Inference | Exxact Blog","og_description":"Recap NVIDIA GTC 2026 with Vera Rubin/Groq, Era of Inference, AI factories, OpenClaw agents, and robotics.","og_image":"","collection":""},"tags":["nvidia","gtc","keynote"],"title":"NVIDIA GTC 2026 Recap - Focusing on The Era of Tokens & Inference","unique_pageviews":"","unique_quarterly_views":0,"updated_at":"2026-03-27T00:29:09.673Z","updated_by":"blt24f42d8cf20d5d45","url":"/blog/news/nvidia-gtc-2026-recap-era-of-tokens-and-inference","publish_details":{"time":"2026-03-27T00:29:29.303Z","user":"blt24f42d8cf20d5d45","environment":"bltfe7355d60efbec6e","locale":"en-us"},"routerLink":"/blog/news/nvidia-gtc-2026-recap-era-of-tokens-and-inference","queryParams":{}},{"uid":"bltfc9c98bfe5255080","locale":"en-us","_version":16,"ACL":{},"_in_progress":false,"ad_blocks_side":[],"article":"","blog_category":[{"_version":7,"locale":"en-us","uid":"blt30782cc2c304a058","ACL":{}
341,"_in_progress":false,"created_at":"2020-08-31T20:22:17.797Z","created_by":"blt13a2948a46a0ad84","description":"","tags":[],"title":"News","updated_at":"2021-08-20T18:18:52.183Z","updated_by":"bltc816d579f2d0d71a","url":"/blog/News","_content_type_uid":"exx_blog_category","publish_details":{"environment":"bltfe7355d60efbec6e","locale":"en-us","time":"2021-08-20T18:18:53.082Z","user":"bltc816d579f2d0d71a"},"routerLink":"/blog/News","queryParams":{}}],"blog_content_v3":{"new_or_old_blog":"new_blog","blog_title":"Exxact Validates First Air-Cooled 4x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation","enable_table_of_contents":true,"feature_image":{"feature_image_link":"","feature_image_file":{"uid":"blt0b012a0e61fa9adb","_version":1,"parent_uid":"blt39100be1a8d8480c","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2025-08-14T16:58:07.684Z","updated_at":"2025-08-14T16:58:07.684Z","content_type":"image/jpeg","file_size":"201022","filename":"Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.jpg","title":"Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.jpg","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2025-08-15T17:53:53.938Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0b012a0e61fa9adb/689e159fa2135501d24fd83c/Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.jpg","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0b012a0e61fa9adb/689e159fa2135501d24fd83c/Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.jpg","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0b012a0e61fa9adb/689e159fa2135501d24fd83c/Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.webp","queryParams":{}},"feature_image_alt":"First Validated 4x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation"},"section":{"group":[{"section_title":"A Problem Worth Solving","_metadata":{"uid":"cs3d11eafa13d7d757"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch5>3/12/2026: Update with Stress Test Graph\u003Cbr />8/18/2025: Initial Blog Upload\u003C/h5>\u003Ch2>A Problem Worth Solving\u003C/h2>\u003Cp>The NVIDIA® RTX PRO™ 6000 Blackwell Max-Q Workstation Edition GPU with 96GB of VRAM has made waves across all industries that value GPU acceleration and high VRAM: AI, Engineering, 3D Content Creation, Life Science, and more. With multi-GPU workstations, the deployment flexibility and performance capabilities are second to none.\u003C/p>\u003Cp>With multi-GPUs workstations, we have a long-standing validation process that every Exxact solution has to clear to ensure proper cooling of the high heat components like multiple GPUs, memory modules, processors, and other components.\u003C/p>\u003Cp>Validating four NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition GPUs in a workstation has been challenging due to a higher heat output than the previous generation. Three NVIDIA RTX PRO 6000 Max-Q were considered the thermal limit for air-cooled configurations, with other integrators resorting to liquid cooling. As of today, March 11, 2026, we still have yet to see another solution integrator offer an air-cooled 4x Max-Q configuration.\u003C/p>\u003Cp>However, at Exxact, we thought otherwise. NVIDIA had designed the NVIDIA RTX PRO 6000 Blackwell Max-Q to 
341scale to 4x GPUs in a workstation. We saw the opportunity as a challenge to offer an air-cooled 4x NVIDIA RTX PRO 6000 Blackwell Max-Q workstation. We are the first to proudly say, “We did it.”\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"csc06b69effccb4454"},"select":"image_block","images_optional":{"image":[{"image_enlargement":false,"_metadata":{"uid":"cs7ae78aa118d38366"},"image_header":"","image_link":"","image_file":{"uid":"blt092593b024415a9d","_version":1,"title":"EXX-blog-4-max-q-system (1).jpg","parent_uid":"blt39100be1a8d8480c","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2025-08-15T22:35:52.962Z","updated_at":"2025-08-15T22:35:52.962Z","content_type":"image/jpeg","file_size":"194188","filename":"EXX-blog-4-max-q-system_(1).jpg","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2025-08-15T22:36:01.374Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt092593b024415a9d/689fb64825099b894b6a0e22/EXX-blog-4-max-q-system_(1).jpg","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt092593b024415a9d/689fb64825099b894b6a0e22/EXX-blog-4-max-q-system_(1).jpg","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt092593b024415a9d/689fb64825099b894b6a0e22/EXX-blog-4-max-q-system_(1).webp","queryParams":{}},"image_alt_text":"Exxact workstation with four NVIDIA RTX PRO 6000 Blackwell Max-Q","ad_link":"","image_caption_subheader":""}]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"The First Quad NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation by Exxact","_metadata":{"uid":"csf0a8495ffdf8ca35"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>The First 4x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation by Exxact\u003C/h2>\u003Cp>Exxact Corporation is the first to deliver a 4x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition in a workstation without liquid cooling the GPUs. With careful chassis selection, airflow design, and thermal monitoring adjustments, we observed GPU stability peaking 85°C over a 3 hour OCCT stress test.\u003C/p>\u003Cp>Introducing the Exxact Valence 4x Max-Q Workstation. This testing was performed on an AMD Threadripper PRO 7000WX platform with all 8 channels of memory populated. Here are the specifications of the system tested in a fixed 24°C/75°F environment with the side panels on:\u003C/p>\u003Ctable>\u003Ctbody>\u003Ctr>\u003Ctd>Exxact Platform\u003C/td>\u003Ctd>\u003Ca href=\"/Exxact-VWS-135223847-E135223847\" target=\"_blank\">VWS-135223847\u003C/a>\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>CPU\u003C/td>\u003Ctd>AMD Threadripper PRO 9995WX on ASUS WRX90-SAGE SE\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>GPU\u003C/td>\u003Ctd>4x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Memory\u003C/td>\u003Ctd>8x DDR5 ECC 128GB DIMMs (total 1TB)\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Power Supply\u003C/td>\u003Ctd>2500W\u003C/td>\u003C/tr>\u003C/tbody>\u003C/table>\u003Cp>In a stock configuration without the Exxact cooling solution, we also observed four NVIDIA RTX PRO 6000 Blackwell Max-Qs thermal throttling, undervolting, and leaving valuable performance (and money) on the table. However, with our custom cooling solution, each of our GPUs pulled the maximum 300W TDP with a temperature below 90°C on all four cards. This goes without saying; deploying your system in a hotter environment or restricting front, side, or rear panel airflow can adversely affect performance.\u003C/p>\u003Cp>We are extremely proud of our engineering team for their ability to deliver the first validated 4x-GPU workstation featuring the extremely popular NVIDIA RTX PRO 6000 Blackwell Max-Q GPU. We ran these same tests on with great success:\u003C/p>\u003Cul>\u003Cli>AMD Threadripper PRO 9000WX\u003C/li>\u003Cli>Intel Xeon W-3500\u003C/li>\u003Cli>Dual Intel Xeon Scalable 5th Gen\u003C/li>\u003C/ul>\u003Cp>We are currently running tests on the new Intel Xeon 600 workstation processor platform.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs6fccc0020702075b"},"select":"image_block","images_optional":{"image":[{"image_enlargement":true,"_metadata":{"uid":"cs002e4103fcae8ac0"}
341,"image_header":"","image_link":"","image_file":{"uid":"blt5fed868cc4262ff4","_version":1,"title":"OCCT.jpg","parent_uid":"blt39100be1a8d8480c","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-03-13T18:49:02.450Z","updated_at":"2026-03-13T18:49:02.450Z","content_type":"image/jpeg","file_size":"554216","filename":"OCCT.jpg","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-03-13T19:15:52.917Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt5fed868cc4262ff4/69b45c1e7aa66b1fd8df4664/OCCT.jpg","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt5fed868cc4262ff4/69b45c1e7aa66b1fd8df4664/OCCT.jpg","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt5fed868cc4262ff4/69b45c1e7aa66b1fd8df4664/OCCT.webp","queryParams":{}},"image_alt_text":"Exxact Validates 4x Max-Q Air-Cooled Workstation OCCT Stress Test","ad_link":"","image_caption_subheader":""}]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cse956a9ae82266c48"},"select":"ad_block","images_optional":{"image":[]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[{"uid":"bltb09a7bf16d360553","_content_type_uid":"blog_ad_block"}]},{"section_title":"Why Air-Cooled versus Liquid-Cooled?","_metadata":{"uid":"cs30fbd86c79b3a25f"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Why Air-Cooled versus Liquid-Cooled?\u003C/h2>\u003Cp>Air cooling is still the most practical choice for many workstation users because:\u003C/p>\u003Cul>\u003Cli>It is \u003Cstrong>simpler to deploy\u003C/strong>, with fewer components and less setup.\u003C/li>\u003Cli>It is \u003Cstrong>easier to service\u003C/strong>, which matters for IT teams supporting multi-GPU fleets.\u003C/li>\u003Cli>It is \u003Cstrong>more predictable over time\u003C/strong>, with no pumps to fail, no coolant to maintain, and no risk of leaks.\u003C/li>\u003Cli>It reduces operational variables, which helps teams deploy, and transport the system faster.\u003C/li>\u003C/ul>\u003Cp>With today’s high-power GPUs, the tradeoff is that air-cooled systems must be engineered correctly. A quad-GPU build needs a chassis that supports high static pressure airflow, a fan and ducting strategy that feeds each card with cool intake air, and a thermal budget that keeps hotspots under control during sustained load.\u003C/p>\u003Cp>Liquid cooling can be a good fit when you are constrained by acoustic targets and need lower fan speeds, when you need maximum thermal headroom in warmer ambient environments, or when you are comfortable with additional maintenance and component complexity.\u003C/p>\u003Cp>For our validation, the goal was clear: \u003Cstrong>prove 4x RTX PRO 6000 Blackwell Max-Q stability in a real workstation, with the side panels on, using air cooling\u003C/strong>. By combining chassis selection, airflow optimization, and continuous thermal monitoring, we achieved stable performance at full 300W TDP per GPU without relying on liquid cooling.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Why the NVIDIA RTX 6000 PRO Blackwell Max-Q is Your Next GPU","_metadata":{"uid":"cs7c30fac2362a9de0"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Why the NVIDIA RTX 6000 PRO Blackwell Max-Q is Your Next GPU\u003C/h2>\u003Cp>The NVIDIA RTX PRO 6000 Blackwell Max-Q is designed with deployment flexibility and scalability in mind. It is a dual-slot workstation-class GPU design for multi-GPU deployments and can be fitted in a typical workstation and a multi-GPU server.\u003C/p>\u003Cp>It features the largest VRAM capacity in a workstation-class GPU with 96GB of GDDR7 per card; when deployed in a 4x GPU workstation configuration, 384GB of GPU memory can power almost any workload. Coupled with the new GDDR7, the memory bandwidth was increased, resulting in significantly better GPU-to-GPU and GPU-to-component communication.\u003C/p>\u003Cp>At Exxact, our customers are extremely excited to get their hands on this GPU. Here are some popular use cases we see that can leverage our Tensor Workstation featuring four NVIDIA RTX PRO 6000 Blackwell Max-Q:\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Multi-GPU for AI Training and Inference","_metadata":{"uid":"csb6f89cb75a135a18"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>Multi-GPU for AI Development and Local Inference\u003C/h3>\u003Cp>A \u003Cstrong>4x Max-Q GPU\u003C/strong> setup is ideal when you need high VRAM per desk for development, testing, and secure on-prem deployment. Example 4x Max-Q GPUs use cases:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Local LLM serving (RAG):\u003C/strong> 96–192GB aggregate VRAM serves 70B–180B parameter models (e.g., Llama 3.1 70B, Mixtral 8x22B) fully on-prem without quantization compromises.\u003C/li>\u003Cli>\u003Cstrong>Fine-tuning:\u003C/strong> Enables full-parameter fine-tuning of 7B–13B models or LoRA runs on 70B models, cutting per-epoch time by 3–4x versus single-GPU.\u003C/li>\u003Cli>\u003Cstrong>Throughput-focused inference:\u003C/strong> Multi-GPU parallelism delivers 2–4x the token throughput of a single GPU for sustained multi-user workloads.\u003C/li>\u003Cli>\u003Cstrong>Multi-modal workloads:\u003C/strong> Run vision-language models like LLaVA-34B or InternVL2-40B locally without sending data offsite.\u003C/li>\u003C/ul>\u003Cp>
341Our Valence 4x Max-Q Workstation handles previously enterprise-only AI tasks. It can't train foundational LLMs (those require hundreds to thousands of GPUs), but it's a strong fit for serving 5–50 concurrent internal users or iterating on sub-100B parameter models without server infrastructure.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Multi-GPU for Engineering and MPD","_metadata":{"uid":"csadbd94a28ba93670"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>Multi-GPU for Engineering Simulation &amp; MPD\u003C/h3>\u003Cp>\u003Cstrong>4x Max-Q GPUs\u003C/strong> help when models are too large for a single GPU, or when teams want to run multiple design iterations in parallel from a desk-side workstation. Example 4x GPU workstation use cases:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>CFD and thermal:\u003C/strong> Handle mesh sizes of 50M–500M+ cells in solvers like Ansys Fluent — cases that would otherwise require cluster time.\u003C/li>\u003Cli>\u003Cstrong>FEA for large assemblies:\u003C/strong> Faster nonlinear and contact-heavy runs on assemblies exceeding 1M+ DOF, where GPU memory is the primary bottleneck.\u003C/li>\u003Cli>\u003Cstrong>Parameter sweeps and digital twins:\u003C/strong> Run 4 independent design variants simultaneously, compressing a day of sequential iteration into hours.\u003C/li>\u003Cli>\u003Cstrong>Pre/post visualization:\u003C/strong> Interactively review 10–100GB datasets locally without streaming to a remote node.\u003C/li>\u003C/ul>\u003Cp>High-performance GPUs increase productivity and iteration count, with typical simulation time reductions of 40–70% versus CPU-only workflows.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Multi-GPU for Life Science & Healthcare","_metadata":{"uid":"csa72b735cbe2fb137"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>Multi-GPU for Engineering Simulation &amp; MPD\u003C/h3>\u003Cp>\u003Cstrong>4x Max-Q GPUs\u003C/strong> help when models are too large for a single GPU, or when teams want to run multiple design iterations in parallel from a desk-side workstation. Example 4x GPU workstation use cases:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>CFD and thermal:\u003C/strong> Handle mesh sizes of 50M–500M+ cells in solvers like Ansys Fluent — cases that would otherwise require cluster time.\u003C/li>\u003Cli>\u003Cstrong>FEA for large assemblies:\u003C/strong> Faster nonlinear and contact-heavy runs on assemblies exceeding 1M+ DOF, where GPU memory is the primary bottleneck.\u003C/li>\u003Cli>\u003Cstrong>Parameter sweeps and digital twins:\u003C/strong> Run 4 independent design variants simultaneously, compressing a day of sequential iteration into hours.\u003C/li>\u003Cli>\u003Cstrong>Pre/post visualization:\u003C/strong> Interactively review 10–100GB datasets locally without streaming to a remote node.\u003C/li>\u003C/ul>\u003Cp>High-performance GPUs increase productivity and iteration count, with typical simulation time reductions of 40–70% versus CPU-only workflows.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Multi-GPU for 3D Content Creation & Rendering","_metadata":{"uid":"cs273377b137d6c2c6"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>Multi-GPU for 3D Design, Rendering, and Visualization\u003C/h3>\u003Cp>\u003Cstrong>4x Max-Q GPUs\u003C/strong> boost throughput for creators who need faster iteration and bigger scenes. Example 4x GPU workstation use cases:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>GPU rendering:\u003C/strong> Near-linear scaling in Octane, Redshift, and V-Ray GPU — 4 GPUs deliver roughly 3–3.8x the throughput of one card on complex production scenes.\u003C/li>\u003Cli>\u003Cstrong>Virtual production and real-time 3D:\u003C/strong> Maintain 120+ FPS headroom in Unreal Engine 5 with Lumen and Nanite enabled.\u003C/li>\u003Cli>\u003Cstrong>Large scene assembly:\u003C/strong> Handle geometry exceeding 500M–1B polygons and texture sets in the 50–200GB range without constant cache eviction.\u003C/li>\u003Cli>\u003Cstrong>AI-assisted workflows:\u003C/strong> Sub-second latency on upscaling, denoising, and generative passes that would stall on a single GPU.\u003C/li>\u003C/ul>\u003Cp>For studios and creators, multi-GPU shortens render queues and speeds up heavy GPU-accelerated workflows while keeping interactive work local for faster reviews and iteration.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Conclusion","_metadata":{"uid":"csa1e053d7beb07dc4"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Conclusion\u003C/h2>\u003Cp>At Exxact, we validate every configuration to ensure optimal performance in the most strenuous use case. Our goal is to deliver the most capable system that works right out of the box. Each system is designed to support a wide range of computing environments and workloads without compromise.\u003C/p>\u003Cp>The AMD Threadripper PRO 9995WX paired with 4x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition is the most performant workstation configuration for handling all types of workloads in AI, Engineering, Life Science, and 3D content. Want to configure this Exxact platform or any other 4x Max-Q workstations? Contact us today!\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs1d575584527d2f67"},"select":"ad_block","images_optional":{"image":[]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[{"uid":"blt7c3d4b068546a907","_content_type_uid":"blog_ad_block"}]}]}},"blog_title":"Exxact Validates First Air-Cooled 4x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation","created_at":"2025-08-14T18:04:37.579Z","created_by":"bltf69a05e284b3d18a","date":"2026-03-12T19:18:31.000Z","feature_image":{"uid":"blt0b012a0e61fa9adb","_version":1,"parent_uid":"blt39100be1a8d8480c","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2025-08-14T16:58:07.684Z","updated_at":"2025-08-14T16:58:07.684Z","content_type":"image/jpeg","file_size":"201022","filename":"Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.jpg","title":"Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.jpg","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2025-08-15T17:53:53.938Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0b012a0e61fa9adb/689e159fa2135501d24fd83c/Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.jpg","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0b012a0e61fa9adb/689e159fa2135501d24fd83c/Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.jpg","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0b012a0e61fa9adb/689e159fa2135501d24fd83c/Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.webp","queryParams":{}},"seo_meta_data":{"title":"Exxact Validates First Air-Cooled 4x RTX PRO 6000 Blackwell Max-Q | E
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341ompared to dual‑socket servers while still supporting substantial parallel workloads. Exxact is looking forward to offering configurable Intel Xeon 600 for AI, scientific computing, and engineering simulation workloads.\u003C/p>\u003Cp>We will go over the specifications of the Intel Xeon 600, set to be officially released later this year, with a late March 2026 launch.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Intel Xeon 600 vs Intel Xeon W-3500/2500 Architectural Differences","_metadata":{"uid":"cs970f8848ebcdd9a0"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Intel Xeon 600 vs Intel Xeon W-3500/2500 Architectural Differences\u003C/h2>\u003Cp>Intel Xeon 600 utilizes the chiplet-based Granite Rapids architecture borrowed from their Xeon 6000 server processors. These new workstation processors will be supported on their new W890 motherboard and chipset beginning late March. Exxact is particularly excited to showcase the Intel Xeon 600 platform at \u003Ca href=\"/News-Events\" target=\"_blank\">NVIDIA GTC 2026;\u003C/a> come visit and say hello!\u003C/p>\u003Cp>Compared to the W-3500/2500 last-generation processors, the Intel Xeon 600 has\u003C/p>\u003Cul>\u003Cli>Up to 86 full-fat performance cores only\u003C/li>\u003Cli>Increased L2 and L3 cache\u003C/li>\u003Cli>Up to 128 PCIe 5.0 lanes\u003C/li>\u003Cli>9% increase in single-threaded performance\u003C/li>\u003Cli>61% increase in multi-threaded performance\u003C/li>\u003C/ul>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Intel Xeon 600 Specifications","_metadata":{"uid":"cs888216edf47d07a9"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Intel Xeon 600 Specifications\u003C/h2>\u003Cp>Intel Xeon 600 Series processors are workstation processors targeting professional workloads that benefit from many performance cores and wide memory and I/O subsystems.\u003C/p>\u003Ctable>\u003Ctbody>\u003Ctr>\u003Ctd>Model\u003C/td>\u003Ctd>Cores / Threads\u003C/td>\u003Ctd>Max Turbo Clock Speed\u003C/td>\u003Ctd>All Core Turbo Clock Speed\u003C/td>\u003Ctd>Base Clock Speed\u003C/td>\u003Ctd>PCIe Lanes\u003C/td>\u003Ctd>Cache\u003C/td>\u003Ctd>TDP\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 698X\u003C/td>\u003Ctd>86 / 172\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>3.0 GHz\u003C/td>\u003Ctd>2.0 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>336 MB\u003C/td>\u003Ctd>350W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 696X\u003C/td>\u003Ctd>64 / 128\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>3.5 GHz\u003C/td>\u003Ctd>2.4 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>336 MB\u003C/td>\u003Ctd>350W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 678X\u003C/td>\u003Ctd>48 / 96\u003C/td>\u003Ctd>4.9 GHz\u003C/td>\u003Ctd>3.8 GHz\u003C/td>\u003Ctd>2.4 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>192 MB\u003C/td>\u003Ctd>300W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 676X\u003C/td>\u003Ctd>32 / 64\u003C/td>\u003Ctd>4.9 GHz\u003C/td>\u003Ctd>4.3 GHz\u003C/td>\u003Ctd>2.8 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>144 MB\u003C/td>\u003Ctd>275W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 674X\u003C/td>\u003Ctd>28 / 56\u003C/td>\u003Ctd>4.9 GHz\u003C/td>\u003Ctd>4.3 GHz\u003C/td>\u003Ctd>3.0 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>144 MB\u003C/td>\u003Ctd>270W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 658X\u003C/td>\u003Ctd>24 / 48\u003C/td>\u003Ctd>4.9 GHz\u003C/td>\u003Ctd>4.3 GHz\u003C/td>\u003Ctd>3.0 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>144 MB\u003C/td>\u003Ctd>250W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 656\u003C/td>\u003Ctd>20 / 40\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>4.5 GHz\u003C/td>\u003Ctd>2.9 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>72 MB\u003C/td>\u003Ctd>210W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 654\u003C/td>\u003Ctd>18 / 36\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>4.5 GHz\u003C/td>\u003Ctd>3.1 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>72 MB\u003C/td>\u003Ctd>200W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 638\u003C/td>\u003Ctd>16 / 32\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>4.5 GHz\u003C/td>\u003Ctd>3.2 GHz\u003C/td>\u003Ctd>80\u003C/td>\u003Ctd>72 MB\u003C/td>\u003Ctd>180W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 636\u003C/td>\u003Ctd>12 / 24\u003C/td>\u003Ctd>4.7 GHz\u003C/td>\u003Ctd>4.5 GHz\u003C/td>\u003Ctd>3.5 GHz\u003C/td>\u003Ctd>80\u003C/td>\u003Ctd>48 MB\u003C/td>\u003Ctd>170W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 634\u003C/td>\u003Ctd>12 / 24\u003C/td>\u003Ctd>4.6 GHz\u003C/td>\u003Ctd>3.9 GHz\u003C/td>\u003Ctd>2.7 GHz\u003C/td>\u003Ctd>80\u003C/td>\u003Ctd>48 MB\u003C/td>\u003Ctd>150W\u003C/td>\u003C/tr>\u003C/tbody>\u003C/table>\u003Cp>As usual, high core count models have lower all-core turbo clock speeds, while lower core count models can clock higher. Understanding whether your workload prioritizes clock speed or core count will help you select the optimal processor. At Exx
341act, we can help configure the right system for your unique workload!\u003C/p>\u003Cp>&nbsp;\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs3b863f0a5a28c69c"},"select":"ad_block","images_optional":{"image":[]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[{"uid":"blt7c3d4b068546a907","_content_type_uid":"blog_ad_block"}]},{"section_title":"","_metadata":{"uid":"cs5f8b57e855366c01"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>Memory, PCIe 5.0, and CXL 2.0 Support\u003C/h3>\u003Cp>In the SKU stack, Intel Xeon 600 has split the 18-cores and higher and the 16-cores and lower options that differ in memory channels and PCIe lanes.\u003C/p>\u003Cp>Across the range, Xeon 600 supports DDR5 6400 MT/s RDIMM and 8000 MT/s MRDIMM, and plenty of PCIe 5.0 lanes.\u003C/p>\u003Cul>\u003Cli>Channels: 8 channels (on 18-cores-and-up SKUs), 4 channels on (16-cores-and-below SKUs)\u003C/li>\u003Cli>5.0 PCIe Lanes: 128 lanes (on 18-cores-and-up SKUs), 80 lanes (on 16-cores-and-below SKUs)\u003C/li>\u003C/ul>\u003Cp>The platform also provides substantial expansion headroom. Intel Xeon 600 processors for workstations feature 128 PCIe 5.0 lanes on the majority of SKUs, with 80 PCIe 5.0 lanes on lower core count models (16 cores and below).\u003C/p>\u003Cp>In addition to supporting more GPUs, storage devices, and networking, these PCIe lanes can also be used for added memory via CXL 2.0.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Intel Xeon 600 Workstations for AI/ML, Scientific Computing, and Engineering Simulation","_metadata":{"uid":"cs72537773f7226015"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Intel Xeon 600 Workstations for AI/ML, Scientific Computing, and Engineering Simulation\u003C/h2>\u003Cp>Intel Xeon 600 workstations can be tailored to a range of compute‑heavy workloads. Across AI/ML, scientific research, and engineering simulation, the most important build variables tend to be CPU core count, memory capacity and bandwidth, and PCIe expansion for GPUs, storage, and networking.\u003C/p>\u003Cp>Key considerations by workload:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>AI and Machine Learning\u003C/strong>\u003Cul>\u003Cli>Higher‑core Xeon 600 SKUs for data preprocessing, orchestration, and mixed CPU/GPU pipelines.\u003C/li>\u003Cli>Large DDR5 memory footprints to keep datasets and intermediate results in memory.\u003C/li>\u003Cli>Enough PCIe 5.0 lanes for dual or quad high‑end GPUs, plus NVMe storage and network adapters.\u003C/li>\u003C/ul>\u003C/li>\u003Cli>\u003Cstrong>Scientific Computing Research\u003C/strong>\u003Cul>\u003Cli>Strong multi‑core performance for molecular dynamics, cryo‑EM processing, and materials simulations.\u003C/li>\u003Cli>Multi‑channel DDR5 configurations tuned for bandwidth and total capacity.\u003C/li>\u003Cli>Expansion room for accelerator cards and high‑performance storage for large datasets and output files.\u003C/li>\u003C/ul>\u003C/li>\u003Cli>\u003Cstrong>Engineering Simulation\u003C/strong>\u003Cul>\u003Cli>Mid‑ to high‑core SKUs to support parallel solvers (CFD, FEA, particle methods).\u003C/li>\u003Cli>Memory sized to keep meshes and particle sets in RAM during computation.\u003C/li>\u003Cli>PCIe resources reserved for GPU accelerators (when supported) and fast NVMe scratch/results storage.\u003C/li>\u003C/ul>\u003C/li>\u003C/ul>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Conclusion: Matching Xeon 600 to Real Requirements","_metadata":{"uid":"cs7ba6d55d0116fdf5"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Conclusion: Matching Xeon 600 to Real Requirements\u003C/h2>\u003Cp>The Intel Xeon 600 Processors for workstations will be another CPU option alongside the AMD Threadripper PRO for high-performance computing.\u003C/p>\u003Cp>As a solutions integrator, our role is to translate platform capabilities into concrete system configurations. We look at core count, memory channels and capacity, GPU count, storage layout, and network requirements, then align those with the actual models, simulations, and datasets you plan to run.\u003C/p>\u003Cp>A workstation can power the majority of the workloads for most individuals in AI research, scientific computing, and engineering simulation. Intel Xeon 600\u003C/p>\u003Cp>
341Talk to our engineering team about your code, typical problem sizes, and preferred software stack, and our team at Exxact is more than happy to \u003Ca href=\"/Exxact-VWS-130523500-E130523500\" target=\"_blank\">build your ideal Xeon 600 configuration\u003C/a> that is sized appropriately and fits your budget.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs439e36aa3052513c"},"select":"ad_block","images_optional":{"image":[]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[{"uid":"blt6e3ffc8dccd20ef8","_content_type_uid":"blog_ad_block"}]}]}},"blog_title":"Intel’s Long-Awaited Xeon 600 Processors for 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Long Awaited Xeon 600 Workstation Processors | Exxact Blog","description":"Intel Xeon 600 processors bring up to 86 cores, 128 PCIe 5.0 lanes, and DDR5-8000 support to single-socket workstations for AI, HPC, and simulation workloads.","keywords":"Intel Xeon 600, workstation processors, single-socket workstation, Granite Rapids architecture, W890 chipset, PCIe 5.0, DDR5 memory, AI workloads, scientific computing, engineering simulation, high core count processors, CXL 2.0, machine learning workstations, HPC workstations, multi-threaded performance","og_title":"Intel's Long Awaited Xeon 600 Workstation Processors | Exxact Blog","og_description":"Intel Xeon 600 processors bring up to 86 cores, 128 PCIe 5.0 lanes, and DDR5-8000 support to single-socket workstations for AI, HPC, and simulation workloads.","og_image":"","collection":""},"tags":["cpu","intel","workstation"],"title":"Intel’s Long-Awaited Xeon 600 Processors for Workstations","unique_pageviews":"","unique_quarterly_views":0,"updated_at":"2026-03-31T23:01:53.568Z","updated_by":"bltf69a05e284b3d18a","url":"/blog/news/intel-xeon-600-processors-for-workstations","publish_details":{"time":"2026-03-31T23:01:58.040Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"routerLink":"/blog/news/intel-xeon-600-processors-for-workstations","queryParams":{}}]},"h":{},"s":200,"st":"OK","u":"https://cdn.contentstack.io/v3/content_types/exx_blog_post/entries?environment=prod-exx&locale=en-us&query={\"blog_category\":{\"$in_query\":{\"title\":{\"$regex\":\"News\",\"$options\":\"i\"}}}}&limit=4&desc=date&include[]=blog_category","rt":"json"},"2610020335":{"b":{"entries":[{"uid":"bltc9ed1484626ba1d5","_version":4,"locale":"en-us","ACL":{},"_in_progress":false,"ad_blocks_side":[],"article":"","blog_category":[{"_version":7,"locale":"en-us","uid":"blt30782cc2c304a058","ACL":{},"_in_progress":false,"created_at":"2020-08-31T20:22:17.797Z","created_by":"blt13a2948a46a0ad84","description":"","tags":[],"title":"News","updated_at":"2021-08-20T18:18:52.183Z","updated_by":"bltc816d579f2d0d71a","url":"/blog/News","_content_type_uid":"exx_blog_category","publish_details":{"environment":"bltfe7355d60efbec6e","locale":"en-us","time":"2021-08-20T18:18:53.082Z","user":"bltc816d579f2d0d71a"},"routerLink":"/blog/News","queryParams":{}}],"blog_content_v3":{"new_or_old_blog":"new_blog","blog_title":"Intel’s Long-Awaited Xeon 600 Processors for Workstation","enable_table_of_contents":true,"feature_image":{"feature_image_link":"","feature_image_file":{"uid":"blt0f9828746f6fac41","_version":1,"title":"EXX-Blog-Intel-xeon-600-cpu.jpg","parent_uid":"blta8d1ca46d4b9b43b","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-02-20T01:40:51.009Z","updated_at":"2026-02-20T01:40:51.009Z","content_type":"image/jpeg","file_size":"189556","filename":"EXX-Blog-Intel-xeon-600-cpu.jpg","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-02-20T01:41:15.992Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0f9828746f6fac41/6997bba399c
341ddc000822b11f/EXX-Blog-Intel-xeon-600-cpu.jpg","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0f9828746f6fac41/6997bba399cddc000822b11f/EXX-Blog-Intel-xeon-600-cpu.jpg","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0f9828746f6fac41/6997bba399cddc000822b11f/EXX-Blog-Intel-xeon-600-cpu.webp","queryParams":{}},"feature_image_alt":"Intel’s Long Awaited Xeon 600 Processors for Workstation | Exxact Blog"},"section":{"group":[{"section_title":"Introduction","_metadata":{"uid":"cs63b463d5fb74557f"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch5>\u003Ca href=\"/Exxact-VWS-130523500-E130523500\" target=\"_blank\">Configure an Exxact Valence Workstation featuring Intel Xeon 600 processors ›\u003C/a>\u003C/h5>\u003Ch2>Introduction\u003C/h2>\u003Cp>After 3 years after the Intel Xeon W-3500 and about 1.5 years after the release of the Intel Xeon 6 Server in 2024, Intel’s new Xeon 600 processors bring server‑class compute capabilities into single‑socket workstations with up to 86 cores.\u003C/p>\u003Cp>By focusing on a single‑socket workstation platform, Xeon 600 simplifies deployment compared to dual‑socket servers while still supporting substantial parallel workloads. Exxact is looking forward to offering configurable Intel Xeon 600 for AI, scientific computing, and engineering simulation workloads.\u003C/p>\u003Cp>We will go over the specifications of the Intel Xeon 600, set to be officially released later this year, with a late March 2026 launch.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Intel Xeon 600 vs Intel Xeon W-3500/2500 Architectural Differences","_metadata":{"uid":"cs970f8848ebcdd9a0"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Intel Xeon 600 vs Intel Xeon W-3500/2500 Architectural Differences\u003C/h2>\u003Cp>Intel Xeon 600 utilizes the chiplet-based Granite Rapids architecture borrowed from their Xeon 6000 server processors. These new workstation processors will be supported on their new W890 motherboard and chipset beginning late March. Exxact is particularly excited to showcase the Intel Xeon 600 platform at \u003Ca href=\"/News-Events\" target=\"_blank\">NVIDIA GTC 2026;\u003C/a> come visit and say hello!\u003C/p>\u003Cp>Compared to the W-3500/2500 last-generation processors, the Intel Xeon 600 has\u003C/p>\u003Cul>\u003Cli>Up to 86 full-fat performance cores only\u003C/li>\u003Cli>Increased L2 and L3 cache\u003C/li>\u003Cli>Up to 128 PCIe 5.0 lanes\u003C/li>\u003Cli>9% increase in single-threaded performance\u003C/li>\u003Cli>61% increase in multi-threaded performance\u003C/li>\u003C/ul>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Intel Xeon 600 Specifications","_metadata":{"uid":"cs888216edf47d07a9"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Intel Xeon 600 Specifications\u003C/h2>\u003Cp>Intel Xeon 600 Series processors are workstation processors targeting professional workloads that benefit from many performance cores and wide memory and I/O subsystems.\u003C/p>\u003Ctable>\u003Ctbody>\u003Ctr>\u003Ctd>Model\u003C/td>\u003Ctd>Cores / Threads\u003C/td>\u003Ctd>Max Turbo Clock Speed\u003C/td>\u003Ctd>All Core Turbo Clock Speed\u003C/td>\u003Ctd>Base Clock Speed\u003C/td>\u003Ctd>PCIe Lanes\u003C/td>\u003Ctd>Cache\u003C/td>\u003Ctd>TDP\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 698X\u003C/td>\u003Ctd>86 / 172\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>3.0 GHz\u003C/td>\u003Ctd>2.0 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>336 MB\u003C/td>\u003Ctd>350W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 696X\u003C/td>\u003Ctd>64 / 128\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>3.5 GHz\u003C/td>\u003Ctd>2.4 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>336 MB\u003C/td>\u003Ctd>350W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 678X\u003C/td>\u003Ctd>48 / 96\u003C/td>\u003Ctd>4.9 GHz\u003C/td>\u003Ctd>3.8 GHz\u003C/td>\u003Ctd>2.4 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>192 MB\u003C/td>\u003Ctd>300W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 676X\u003C/td>\u003Ctd>32 / 64\u003C/td>\u003Ctd>4.9 GHz\u003C/td>\u003Ctd>4.3 GHz\u003C/td>\u003Ctd>2.8 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>144 MB\u003C/td>\u003Ctd>275W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 674X\u003C/td>\u003Ctd>28 / 56\u003C/td>\u003Ctd>4.9 GHz\u003C/td>\u003Ctd>4.3 GHz\u003C/td>\u003Ctd>3.0 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>144 MB\u003C/td>\u003Ctd>270W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 658X\u003C/td>\u003Ctd>24 / 48\u003C/td>\u003Ctd>4.9 GHz\u003C/td>\u003Ctd>4.3 GHz\u003C/td>\u003Ctd>3.0 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>144 MB\u003C/td>\u003Ctd>250W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 656\u003C/td>\u003Ctd>20 / 40\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>4.5 GHz\u003C/td>\u003Ctd>2.9 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>72 MB\u003C/td>\u003Ctd>210W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 654\u003C/td>\u003Ctd>18 / 36\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>4.5 GHz\u003C/td>\u003Ctd>3.1 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>72 MB\u003C/td>\u003Ctd>200W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 638\u003C/td>\u003Ctd>16 / 32\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>4.5 GHz\u003C/td>\u003Ctd>3.2 GHz\u003C/td>\u003Ctd>80\u003C/td>\u003Ctd>72 MB\u003C/td>\u003Ctd>180W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 636\u003C/td>\u003Ctd>12 / 24\u003C/td>\u003Ctd>4.7 GHz\u003C/td>\u003Ctd>4.5 GHz\u003C/td>\u003Ctd>3.5 GHz\u003C/td>\u003Ctd>80\u003C/td>\u003Ctd>48 MB\u003C/td>\u003Ctd>170W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 634\u003C/td>\u003Ctd>12 / 24\u003C/td>\u003Ctd>4.6 GHz\u003C/td>\u003Ctd>3.9 GHz\u003C/td>\u003Ctd>2.7 GHz\u003C/td>\u003Ctd>80\u003C/td>\u003Ctd>48 MB\u003C/td>\u003Ctd>150W\u003C/td>\u003C/tr>\u003C/tbody>\u003C/table>\u003Cp>As usual, high core count models have lower all-core turbo clock speeds, while lower core count models can clock higher. Understanding whether your workload prioritizes clock speed or core count will help you select the optimal processor. At Exx
341act, we can help configure the right system for your unique workload!\u003C/p>\u003Cp>&nbsp;\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs3b863f0a5a28c69c"},"select":"ad_block","images_optional":{"image":[]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[{"_content_type_uid":"blog_ad_block","uid":"blt7c3d4b068546a907","_version":6,"locale":"en-us","ACL":{},"_in_progress":false,"ad_banner_copy":"We extensively stock the latest CPUs and most powerful GPUs; accelerate your workloads with a workstation optimized to your deployment, budget, and desired performance.","ad_banner_title":"Accelerate Your Unique Workloads with the Latest Hardware","ad_url":"/category/Standard-Workstations","banner_image_file":{"uid":"blt0adfffce84eb3abb","_version":1,"parent_uid":"blteb775942f7ac40b1","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2024-02-13T19:04:51.957Z","updated_at":"2024-02-13T19:04:51.957Z","content_type":"image/png","file_size":"188396","filename":"Background-Pattern.png","title":"Background-Pattern.png","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2024-02-13T19:05:18.320Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0adfffce84eb3abb/65cbbd5323dbef12a7ffeb92/Background-Pattern.png","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0adfffce84eb3abb/65cbbd5323dbef12a7ffeb92/Background-Pattern.png","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0adfffce84eb3abb/65cbbd5323dbef12a7ffeb92/Background-Pattern.webp","queryParams":{}},"created_at":"2024-08-19T22:32:11.449Z","created_by":"bltf69a05e284b3d18a","image_alt_text":"exxact workstation","link_anchor_text":"Configure Now","product_image_file":{"uid":"bltb941e0955dfc0d16","_version":2,"is_dir":false,"ACL":{},"content_type":"image/png","created_at":"2025-04-02T18:58:11.344Z","created_by":"bltf69a05e284b3d18a","description":"","file_size":"76578","filename":"exx-vws-ph.png","parent_uid":"blteb775942f7ac40b1","tags":[],"title":"exx-vws-ph.png","updated_at":"2025-04-02T19:24:43.914Z","updated_by":"bltf69a05e284b3d18a","publish_details":{"time":"2025-04-02T19:24:46.737Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltb941e0955dfc0d16/67ed8efbb41d38c45677ff9e/exx-vws-ph.png","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltb941e0955dfc0d16/67ed8efbb41d38c45677ff9e/exx-vws-ph.png","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltb941e0955dfc0d16/67ed8efbb41d38c45677ff9e/exx-vws-ph.webp","queryParams":{}},"tags":[],"title":"WS > Workstation > Exponentially Accelerate Workloads with the Latest GPU","updated_at":"2025-04-04T00:38:51.601Z","updated_by":"bltf69a05e284b3d18a","publish_details":{"time":"2025-04-04T00:38:55.363Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"}}]},{"section_title":"","_metadata":{"uid":"cs5f8b57e855366c01"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>Memory, PCIe 5.0, and CXL 2.0 Support\u003C/h3>\u003Cp>In the SKU stack, Intel Xeon 600 has split the 18-cores and higher and the 16-cores and lower options that differ in memory channels and PCIe lanes.\u003C/p>\u003Cp>Across the range, Xeon 600 supports DDR5 6400 MT/s RDIMM and 8000 MT/s MRDIMM, and plenty of PCIe 5.0 lanes.\u003C/p>\u003Cul>\u003Cli>Channels: 8 channels (on 18-cores-and-up SKUs), 4 channels on (16-cores-and-below SKUs)\u003C/li>\u003Cli>5.0 PCIe Lanes: 128 lanes (on 18-cores-and-up SKUs), 80 lanes (on 16-cores-and-below SKUs)\u003C/li>\u003C/ul>\u003Cp>The platform also provides substantial expansion headroom. Intel Xeon 600 processors for workstations feature 128 PCIe 5.0 lanes on the majority of SKUs, with 80 PCIe 5.0 lanes on lower core count models (16 cores and below).\u003C/p>\u003Cp>In addition to supporting more GPUs, storage devices, and networking, these PCIe lanes can also be used for added memory via CXL 2.0.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Intel Xeon 600 Workstations for AI/ML, Scientific Computing, and Engineering Simulation","_metadata":{"uid":"cs72537773f7226015"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Intel Xeon 600 Workstations for AI/ML, Scientific Computing, and Engineering Simulation\u003C/h2>\u003Cp>Intel Xeon 600 workstations can be tailored to a range of compute‑heavy workloads. Across AI/ML, scientific research, and engineering simulation, the most important build variables tend to be CPU core count, memory capacity and bandwidth, and PCIe expansion for GPUs, storage, and networking.\u003C/p>\u003Cp>
341Key considerations by workload:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>AI and Machine Learning\u003C/strong>\u003Cul>\u003Cli>Higher‑core Xeon 600 SKUs for data preprocessing, orchestration, and mixed CPU/GPU pipelines.\u003C/li>\u003Cli>Large DDR5 memory footprints to keep datasets and intermediate results in memory.\u003C/li>\u003Cli>Enough PCIe 5.0 lanes for dual or quad high‑end GPUs, plus NVMe storage and network adapters.\u003C/li>\u003C/ul>\u003C/li>\u003Cli>\u003Cstrong>Scientific Computing Research\u003C/strong>\u003Cul>\u003Cli>Strong multi‑core performance for molecular dynamics, cryo‑EM processing, and materials simulations.\u003C/li>\u003Cli>Multi‑channel DDR5 configurations tuned for bandwidth and total capacity.\u003C/li>\u003Cli>Expansion room for accelerator cards and high‑performance storage for large datasets and output files.\u003C/li>\u003C/ul>\u003C/li>\u003Cli>\u003Cstrong>Engineering Simulation\u003C/strong>\u003Cul>\u003Cli>Mid‑ to high‑core SKUs to support parallel solvers (CFD, FEA, particle methods).\u003C/li>\u003Cli>Memory sized to keep meshes and particle sets in RAM during computation.\u003C/li>\u003Cli>PCIe resources reserved for GPU accelerators (when supported) and fast NVMe scratch/results storage.\u003C/li>\u003C/ul>\u003C/li>\u003C/ul>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Conclusion: Matching Xeon 600 to Real Requirements","_metadata":{"uid":"cs7ba6d55d0116fdf5"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Conclusion: Matching Xeon 600 to Real Requirements\u003C/h2>\u003Cp>The Intel Xeon 600 Processors for workstations will be another CPU option alongside the AMD Threadripper PRO for high-performance computing.\u003C/p>\u003Cp>As a solutions integrator, our role is to translate platform capabilities into concrete system configurations. We look at core count, memory channels and capacity, GPU count, storage layout, and network requirements, then align those with the actual models, simulations, and datasets you plan to run.\u003C/p>\u003Cp>A workstation can power the majority of the workloads for most individuals in AI research, scientific computing, and engineering simulation. Intel Xeon 600\u003C/p>\u003Cp>Talk to our engineering team about your code, typical problem sizes, and preferred software stack, and our team at Exxact is more than happy to \u003Ca href=\"/Exxact-VWS-130523500-E130523500\" target=\"_blank\">build your ideal Xeon 600 configuration\u003C/a> that is sized appropriately and fits your budget.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs439e36aa3052513c"},"select":"ad_block","images_optional":{"image":[]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[{"_content_type_uid":"blog_ad_block","uid":"blt6e3ffc8dccd20ef8","_version":4,"locale":"en-us","ACL":{},"_in_progress":false,"ad_banner_copy":"With access to the highest performing hardware, at Exxact, we can offer the platform optimized for your deployment, budget, and desired performance so you can make an impact with your research! ","ad_banner_title":"We're Here to Deliver the Tools to Power Your Research ","ad_url":"/Contact-Us","banner_image_file":{"uid":"blt0adfffce84eb3abb","_version":1,"parent_uid":"blteb775942f7ac40b1","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2024-02-13T19:04:51.957Z","updated_at":"2024-02-13T19:04:51.957Z","content_type":"image/png","file_size":"188396","filename":"Background-Pattern.png","title":"Background-Pattern.png","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2024-02-13T19:05:18.320Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0adfffce84eb3abb/65cbbd5323dbef12a7ffeb92/Background-Pattern.png","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0adfffce84eb3abb/65cbbd5323dbef12a7ffeb92/Backgroun
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341ompared to dual‑socket servers while still supporting substantial parallel workloads. Exxact is looking forward to offering configurable Intel Xeon 600 for AI, scientific computing, and engineering simulation workloads.\u003C/p>\u003Cp>We will go over the specifications of the Intel Xeon 600, set to be officially released later this year, with a late March 2026 launch.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Intel Xeon 600 vs Intel Xeon W-3500/2500 Architectural Differences","_metadata":{"uid":"cs970f8848ebcdd9a0"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Intel Xeon 600 vs Intel Xeon W-3500/2500 Architectural Differences\u003C/h2>\u003Cp>Intel Xeon 600 utilizes the chiplet-based Granite Rapids architecture borrowed from their Xeon 6000 server processors. These new workstation processors will be supported on their new W890 motherboard and chipset beginning late March. Exxact is particularly excited to showcase the Intel Xeon 600 platform at \u003Ca href=\"/News-Events\" target=\"_blank\">NVIDIA GTC 2026;\u003C/a> come visit and say hello!\u003C/p>\u003Cp>Compared to the W-3500/2500 last-generation processors, the Intel Xeon 600 has\u003C/p>\u003Cul>\u003Cli>Up to 86 full-fat performance cores only\u003C/li>\u003Cli>Increased L2 and L3 cache\u003C/li>\u003Cli>Up to 128 PCIe 5.0 lanes\u003C/li>\u003Cli>9% increase in single-threaded performance\u003C/li>\u003Cli>61% increase in multi-threaded performance\u003C/li>\u003C/ul>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Intel Xeon 600 Specifications","_metadata":{"uid":"cs888216edf47d07a9"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Intel Xeon 600 Specifications\u003C/h2>\u003Cp>Intel Xeon 600 Series processors are workstation processors targeting professional workloads that benefit from many performance cores and wide memory and I/O subsystems.\u003C/p>\u003Ctable>\u003Ctbody>\u003Ctr>\u003Ctd>Model\u003C/td>\u003Ctd>Cores / Threads\u003C/td>\u003Ctd>Max Turbo Clock Speed\u003C/td>\u003Ctd>All Core Turbo Clock Speed\u003C/td>\u003Ctd>Base Clock Speed\u003C/td>\u003Ctd>PCIe Lanes\u003C/td>\u003Ctd>Cache\u003C/td>\u003Ctd>TDP\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 698X\u003C/td>\u003Ctd>86 / 172\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>3.0 GHz\u003C/td>\u003Ctd>2.0 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>336 MB\u003C/td>\u003Ctd>350W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 696X\u003C/td>\u003Ctd>64 / 128\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>3.5 GHz\u003C/td>\u003Ctd>2.4 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>336 MB\u003C/td>\u003Ctd>350W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 678X\u003C/td>\u003Ctd>48 / 96\u003C/td>\u003Ctd>4.9 GHz\u003C/td>\u003Ctd>3.8 GHz\u003C/td>\u003Ctd>2.4 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>192 MB\u003C/td>\u003Ctd>300W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 676X\u003C/td>\u003Ctd>32 / 64\u003C/td>\u003Ctd>4.9 GHz\u003C/td>\u003Ctd>4.3 GHz\u003C/td>\u003Ctd>2.8 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>144 MB\u003C/td>\u003Ctd>275W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 674X\u003C/td>\u003Ctd>28 / 56\u003C/td>\u003Ctd>4.9 GHz\u003C/td>\u003Ctd>4.3 GHz\u003C/td>\u003Ctd>3.0 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>144 MB\u003C/td>\u003Ctd>270W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 658X\u003C/td>\u003Ctd>24 / 48\u003C/td>\u003Ctd>4.9 GHz\u003C/td>\u003Ctd>4.3 GHz\u003C/td>\u003Ctd>3.0 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>144 MB\u003C/td>\u003Ctd>250W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 656\u003C/td>\u003Ctd>20 / 40\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>4.5 GHz\u003C/td>\u003Ctd>2.9 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>72 MB\u003C/td>\u003Ctd>210W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 654\u003C/td>\u003Ctd>18 / 36\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>4.5 GHz\u003C/td>\u003Ctd>3.1 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>72 MB\u003C/td>\u003Ctd>200W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 638\u003C/td>\u003Ctd>16 / 32\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>4.5 GHz\u003C/td>\u003Ctd>3.2 GHz\u003C/td>\u003Ctd>80\u003C/td>\u003Ctd>72 MB\u003C/td>\u003Ctd>180W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 636\u003C/td>\u003Ctd>12 / 24\u003C/td>\u003Ctd>4.7 GHz\u003C/td>\u003Ctd>4.5 GHz\u003C/td>\u003Ctd>3.5 GHz\u003C/td>\u003Ctd>80\u003C/td>\u003Ctd>48 MB\u003C/td>\u003Ctd>170W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 634\u003C/td>\u003Ctd>12 / 24\u003C/td>\u003Ctd>4.6 GHz\u003C/td>\u003Ctd>3.9 GHz\u003C/td>\u003Ctd>2.7 GHz\u003C/td>\u003Ctd>80\u003C/td>\u003Ctd>48 MB\u003C/td>\u003Ctd>150W\u003C/td>\u003C/tr>\u003C/tbody>\u003C/table>\u003Cp>As usual, high core count models have lower all-core turbo clock speeds, while lower core count models can clock higher. Understanding whether your workload prioritizes clock speed or core count will help you select the optimal processor. At Exx
341act, we can help configure the right system for your unique workload!\u003C/p>\u003Cp>&nbsp;\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs3b863f0a5a28c69c"},"select":"ad_block","images_optional":{"image":[]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[{"_content_type_uid":"blog_ad_block","uid":"blt7c3d4b068546a907","_version":6,"locale":"en-us","ACL":{},"_in_progress":false,"ad_banner_copy":"We extensively stock the latest CPUs and most powerful GPUs; accelerate your workloads with a workstation optimized to your deployment, budget, and desired performance.","ad_banner_title":"Accelerate Your Unique Workloads with the Latest Hardware","ad_url":"/category/Standard-Workstations","banner_image_file":{"uid":"blt0adfffce84eb3abb","_version":1,"parent_uid":"blteb775942f7ac40b1","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2024-02-13T19:04:51.957Z","updated_at":"2024-02-13T19:04:51.957Z","content_type":"image/png","file_size":"188396","filename":"Background-Pattern.png","title":"Background-Pattern.png","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2024-02-13T19:05:18.320Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0adfffce84eb3abb/65cbbd5323dbef12a7ffeb92/Background-Pattern.png","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0adfffce84eb3abb/65cbbd5323dbef12a7ffeb92/Background-Pattern.png","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0adfffce84eb3abb/65cbbd5323dbef12a7ffeb92/Background-Pattern.webp","queryParams":{}},"created_at":"2024-08-19T22:32:11.449Z","created_by":"bltf69a05e284b3d18a","image_alt_text":"exxact workstation","link_anchor_text":"Configure Now","product_image_file":{"uid":"bltb941e0955dfc0d16","_version":2,"is_dir":false,"ACL":{},"content_type":"image/png","created_at":"2025-04-02T18:58:11.344Z","created_by":"bltf69a05e284b3d18a","description":"","file_size":"76578","filename":"exx-vws-ph.png","parent_uid":"blteb775942f7ac40b1","tags":[],"title":"exx-vws-ph.png","updated_at":"2025-04-02T19:24:43.914Z","updated_by":"bltf69a05e284b3d18a","publish_details":{"time":"2025-04-02T19:24:46.737Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltb941e0955dfc0d16/67ed8efbb41d38c45677ff9e/exx-vws-ph.png","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltb941e0955dfc0d16/67ed8efbb41d38c45677ff9e/exx-vws-ph.png","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltb941e0955dfc0d16/67ed8efbb41d38c45677ff9e/exx-vws-ph.webp","queryParams":{}},"tags":[],"title":"WS > Workstation > Exponentially Accelerate Workloads with the Latest GPU","updated_at":"2025-04-04T00:38:51.601Z","updated_by":"bltf69a05e284b3d18a","publish_details":{"time":"2025-04-04T00:38:55.363Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"}}]},{"section_title":"","_metadata":{"uid":"cs5f8b57e855366c01"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>Memory, PCIe 5.0, and CXL 2.0 Support\u003C/h3>\u003Cp>In the SKU stack, Intel Xeon 600 has split the 18-cores and higher and the 16-cores and lower options that differ in memory channels and PCIe lanes.\u003C/p>\u003Cp>Across the range, Xeon 600 supports DDR5 6400 MT/s RDIMM and 8000 MT/s MRDIMM, and plenty of PCIe 5.0 lanes.\u003C/p>\u003Cul>\u003Cli>Channels: 8 channels (on 18-cores-and-up SKUs), 4 channels on (16-cores-and-below SKUs)\u003C/li>\u003Cli>5.0 PCIe Lanes: 128 lanes (on 18-cores-and-up SKUs), 80 lanes (on 16-cores-and-below SKUs)\u003C/li>\u003C/ul>\u003Cp>The platform also provides substantial expansion headroom. Intel Xeon 600 processors for workstations feature 128 PCIe 5.0 lanes on the majority of SKUs, with 80 PCIe 5.0 lanes on lower core count models (16 cores and below).\u003C/p>\u003Cp>In addition to supporting more GPUs, storage devices, and networking, these PCIe lanes can also be used for added memory via CXL 2.0.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Intel Xeon 600 Workstations for AI/ML, Scientific Computing, and Engineering Simulation","_metadata":{"uid":"cs72537773f7226015"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Intel Xeon 600 Workstations for AI/ML, Scientific Computing, and Engineering Simulation\u003C/h2>\u003Cp>Intel Xeon 600 workstations can be tailored to a range of compute‑heavy workloads. Across AI/ML, scientific research, and engineering simulation, the most important build variables tend to be CPU core count, memory capacity and bandwidth, and PCIe expansion for GPUs, storage, and networking.\u003C/p>\u003Cp>
341Key considerations by workload:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>AI and Machine Learning\u003C/strong>\u003Cul>\u003Cli>Higher‑core Xeon 600 SKUs for data preprocessing, orchestration, and mixed CPU/GPU pipelines.\u003C/li>\u003Cli>Large DDR5 memory footprints to keep datasets and intermediate results in memory.\u003C/li>\u003Cli>Enough PCIe 5.0 lanes for dual or quad high‑end GPUs, plus NVMe storage and network adapters.\u003C/li>\u003C/ul>\u003C/li>\u003Cli>\u003Cstrong>Scientific Computing Research\u003C/strong>\u003Cul>\u003Cli>Strong multi‑core performance for molecular dynamics, cryo‑EM processing, and materials simulations.\u003C/li>\u003Cli>Multi‑channel DDR5 configurations tuned for bandwidth and total capacity.\u003C/li>\u003Cli>Expansion room for accelerator cards and high‑performance storage for large datasets and output files.\u003C/li>\u003C/ul>\u003C/li>\u003Cli>\u003Cstrong>Engineering Simulation\u003C/strong>\u003Cul>\u003Cli>Mid‑ to high‑core SKUs to support parallel solvers (CFD, FEA, particle methods).\u003C/li>\u003Cli>Memory sized to keep meshes and particle sets in RAM during computation.\u003C/li>\u003Cli>PCIe resources reserved for GPU accelerators (when supported) and fast NVMe scratch/results storage.\u003C/li>\u003C/ul>\u003C/li>\u003C/ul>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Conclusion: Matching Xeon 600 to Real Requirements","_metadata":{"uid":"cs7ba6d55d0116fdf5"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Conclusion: Matching Xeon 600 to Real Requirements\u003C/h2>\u003Cp>The Intel Xeon 600 Processors for workstations will be another CPU option alongside the AMD Threadripper PRO for high-performance computing.\u003C/p>\u003Cp>As a solutions integrator, our role is to translate platform capabilities into concrete system configurations. We look at core count, memory channels and capacity, GPU count, storage layout, and network requirements, then align those with the actual models, simulations, and datasets you plan to run.\u003C/p>\u003Cp>A workstation can power the majority of the workloads for most individuals in AI research, scientific computing, and engineering simulation. Intel Xeon 600\u003C/p>\u003Cp>Talk to our engineering team about your code, typical problem sizes, and preferred software stack, and our team at Exxact is more than happy to \u003Ca href=\"/Exxact-VWS-130523500-E130523500\" target=\"_blank\">build your ideal Xeon 600 configuration\u003C/a> that is sized appropriately and fits your budget.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs439e36aa3052513c"},"select":"ad_block","images_optional":{"image":[]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[{"_content_type_uid":"blog_ad_block","uid":"blt6e3ffc8dccd20ef8","_version":4,"locale":"en-us","ACL":{},"_in_progress":false,"ad_banner_copy":"With access to the highest performing hardware, at Exxact, we can offer the platform optimized for your deployment, budget, and desired performance so you can make an impact with your research! ","ad_banner_title":"We're Here to Deliver the Tools to Power Your Research ","ad_url":"/Contact-Us","banner_image_file":{"uid":"blt0adfffce84eb3abb","_version":1,"parent_uid":"blteb775942f7ac40b1","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2024-02-13T19:04:51.957Z","updated_at":"2024-02-13T19:04:51.957Z","content_type":"image/png","file_size":"188396","filename":"Background-Pattern.png","title":"Background-Pattern.png","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2024-02-13T19:05:18.320Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0adfffce84eb3abb/65cbbd5323dbef12a7ffeb92/Background-Pattern.png","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0adfffce84eb3abb/65cbbd5323dbef12a7ffeb92/Backgroun
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341inference, but either cannot high thermals and power density of OAM and SXM based nodes or cannot.\u003C/p>\u003Cp>If you are looking for AMD Instinct MI350P data center GPUs, talk to our sales engineers today for more information! \u003Ca href=\"/category/AMD-Instinct-Solutions\" target=\"_self\">Configure an Exxact TensorEX server with AMD Instinct GPUs today.\u003C/a>\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"csbe57c9a156e62c89"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>AMD Instinct MI350P Specifications\u003C/h2>\u003Cp>AMD is essentially taking one of its MI350X accelerators and cutting it in half, resulting in a card with half as many compute units and half as much memory at just over half the power consumption.\u003C/p>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>\u003Cstrong>GPU\u003C/strong>\u003C/th>\u003Cth>\u003Cstrong>MI350P\u003C/strong>\u003C/th>\u003Cth>\u003Cstrong>MI350X\u003C/strong>\u003C/th>\u003C/tr>\u003C/thead>\u003Ctbody>\u003Ctr>\u003Ctd>\u003Cstrong>Compute Units\u003C/strong>\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>256\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>\u003Cstrong>Matrix Cores\u003C/strong>\u003C/td>\u003Ctd>512\u003C/td>\u003Ctd>1024\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>\u003Cstrong>Peak Engine Clock\u003C/strong>\u003C/td>\u003Ctd>2200MHz\u003C/td>\u003Ctd>2200MHz\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>\u003Cstrong>Memory\u003C/strong>\u003C/td>\u003Ctd>144GB HBM3E\u003C/td>\u003Ctd>288GB HBM3E\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>\u003Cstrong>Memory Bandwidth\u003C/strong>\u003C/td>\u003Ctd>4TB/sec (8Gbps x 4096-bits)\u003C/td>\u003Ctd>8TB/sec (8Gbps x 8192-bits)\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>\u003Cstrong>Matrix Perf (MXFP8)\u003C/strong>\u003C/td>\u003Ctd>2.3 PFLOPS\u003C/td>\u003Ctd>4.6 PFLOPS\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>\u003Cstrong>I/O\u003C/strong>\u003C/td>\u003Ctd>PCIe Gen5 x16\u003C/td>\u003Ctd>
341PCIe Gen5 x167x Infinity Fabric (x16)\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>\u003Cstrong>TBP\u003C/strong>\u003C/td>\u003Ctd>600W (Optional: 450W)\u003C/td>\u003Ctd>1000W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>\u003Cstrong>Form Factor\u003C/strong>\u003C/td>\u003Ctd>PCIe FHFL Double-Wide\u003C/td>\u003Ctd>OAM\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>\u003Cstrong>Architecture\u003C/strong>\u003C/td>\u003Ctd>CDNA 4\u003C/td>\u003Ctd>CDNA 4\u003C/td>\u003C/tr>\u003C/tbody>\u003C/table>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Why Use a PCIe-Based Data Center GPU?","_metadata":{"uid":"csea65d236296986ff"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>Why Use a PCIe-Based Data Center GPU?\u003C/h3>\u003Cp>The majority of AI accelerator demand has centered on modular OAM and SXM-based GPUs designed for high-density compute nodes. These solutions deliver the best scale-up and scale-out performance through proprietary GPU interconnects. But they come with significant infrastructure requirements:\u003C/p>\u003Cul>\u003Cli>11kW per node\u003C/li>\u003Cli>Proprietary server design\u003C/li>\u003Cli>Low upgradability.\u003C/li>\u003C/ul>\u003Cp>This has left a gap in the market for customers who need serious AI hardware without the overhead of high-density non-modular systems. GPU vendors have attempted to address this gap with workstation-class products repurposed for the data center. While capable, these graphics-based GPUs fall short in key areas:\u003C/p>\u003Cul>\u003Cli>Lower peak performance compared to flagship server GPUs\u003C/li>\u003Cli>No HBM memory, limiting memory capacity and bandwidth\u003C/li>\u003Cli>Not a fully validated replacement for data center-class AI inference workloads\u003C/li>\u003C/ul>\u003Cp>To properly fill this gap, AMD is releasing the Instinct MI350P — its first Instinct PCIe accelerator built on its current-generation CDNA 4 architecture since the MI210 in 2022. The goal is straightforward: deliver flagship-class AI hardware to traditional server and data center customers in a standard, readily replaceable PCIe form factor that can plug into existing server designs.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs0613e7dbffcb8c9f"},"select":"image_block","images_optional":{"image":[{"image_enlargement":false,"_metadata":{"uid":"csff4c9fb00014e30b"},"image_header":"","image_link":"","image_file":{"uid":"blt18796290f632f310","_version":1,"title":"EXX-Blog-amd-instinct-mi350i-2.jpg","parent_uid":"bltca9c53775898e821","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-07-08T22:30:08.783Z","updated_at":"2026-07-08T22:30:08.783Z","content_type":"image/jpeg","file_size":"71398","filename":"EXX-Blog-amd-instinct-mi350i-2.jpg","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-07-08T23:39:33.489Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt18796290f632f310/6a4ecf70c3e19c411e3353f6/EXX-Blog-amd-instinct-mi350i-2.jpg","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt18796290f632f310/6a4ecf70c3e19c411e3353f6/EXX-Blog-amd-instinct-mi350i-2.jpg","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt18796290f632f310/6a4ecf70c3e19c411e3353f6/EXX-Blog-amd-instinct-mi350i-2.webp","queryParams":{}},"image_alt_text":"AMD Instinct MI350P front image","ad_link":"","image_caption_subheader":""}]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Instinct MI350P: Half a MI350X At Half the Power","_metadata":{"uid":"cs78ef5b7cb0fa25fd"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Instinct MI350P: Half a MI350X At Half the Power\u003C/h2>\u003Cp>The MI350P is effectively a precision half-slice of AMD’s flagship MI350X. Where the MI350X uses two I/O dies (IODs), each stacked with four accelerator complex dies (XCDs), for eight total XCDs, the MI350P uses a single IOD with four XCDs clocked identically to the MI350X. The result is roughly half the compute performance in a PCIe GPU.\u003C/p>\u003Cp>The die reduction scales evenly across memory as well:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Memory:\u003C/strong> 144GB HBM3e (half the XCDs mean half the 288GB on the MI350X)\u003C/li>\u003Cli>\u003Cstrong>Memory Bandwidth:\u003C/strong> 4TB/s (half of the MI350X’s 8TB/s)\u003C/li>\u003Cli>\u003Cstrong>Peak Matrix Performance (MXFP8):\u003C/strong> 2.3 PFLOPS vs. 4.6 PFLOPS\u003C/li>\u003C/ul>\u003Cp>Physically, the MI350P is a standard full-height, full-length (FHFL) dual-slot PCIe card available in two TBP configurations: 600W standard and 450W optional. It is designed for air cooling and targets mainstream server form factors.\u003C/p>\u003Cp>AMD did not include Infinity Fabric GPU-to-GPU interconnect support on the MI350P. Multi-GPU setups are limited to PCIe Gen5 x16 communication between cards, which caps usable AI model sizes to what fits within the 144GB of VRAM. This is the biggest difference from its MI350X OAM counterpart.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"FAQ on AMD Instinct MI350P","_metadata":{"uid":"csb602c3aeaa840536"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>FAQ on AMD Instinct MI350P\u003C/h3>\u003Ch4>What is the AMD Instinct MI350P?\u003C/h4>\u003Cp>The AMD Instinct MI350P is a data center GPU built in a standard PCIe add-in card form factor. It brings AMD’s CDNA 4 architecture, 144GB of HBM3E memory, and 2.3 PFLOPS of MXFP8 matrix performance to mainstream server platforms without requiring an OAM-based accelerator node.\u003C/p>\u003Ch4>What workloads is the AMD Instinct MI350P designed for?\u003C/h4>\u003Cp>The AMD Instinct MI350P is designed for enterprise AI inference, generative AI, retrieval-augmented generation (RAG), small and large language model deployment, and other accelerator-driven data center workloads. Its 144GB of HBM3E memory gives it enough capacity for many on-premises inference deployments while staying within a standard PCIe server design.\u003C/p>\u003Ch4>How much power does the AMD Instinct MI350P use?\u003C/h4>\u003Cp>The AMD Instinct MI350P has a 600W total board power configuration, with an optional 450W mode for systems with tighter power or thermal requirements. It is a passive, air-cooled full-height, full-length dual-slot PCIe card 
341intended for compatible server platforms.\u003C/p>\u003Ch4>Who should consider using the AMD Instinct MI350P?\u003C/h4>\u003Cp>The AMD Instinct MI350P is a strong fit for organizations that want to deploy on-premises AI infrastructure but do not need, or cannot support, the density and power requirements of OAM or SXM GPU systems. It is especially relevant for teams looking to add high-capacity AI acceleration to existing PCIe-based server environments.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Conclusion","_metadata":{"uid":"csf004b030eb80c330"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Conclusion\u003C/h2>\u003Cp>AMD has made significant strides in the data center GPU market, and with the Instinct MI350P, the company is finally returning to a long-underserved segment: the standard PCIe form factor. This is AMD's first Instinct PCIe accelerator built on its current-generation architecture in nearly five years, arriving at a time when demand for accessible, high-performance AI hardware is growing rapidly.\u003C/p>\u003Cp>The MI350P is a purpose-built product, not a compromise. It won't match the raw throughput of the MI350X, but it delivers the highest performance AMD can package into a standard PCIe card — complete with CDNA 4 architecture, HBM3E memory, and data center-class capabilities — in a form factor that fits the infrastructure most organizations already have.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs933e24e874c330bc"},"select":"ad_block","images_optional":{"image":[]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[{"uid":"blt74922f1792f95854","_content_type_uid":"blog_ad_block"}]}]}},"blog_title":"AMD Instinct MI350P - Why PCIe Data Center GPUs Still Matter","created_at":"2026-07-08T22:58:48.461Z","created_by":"bltf69a05e284b3d18a","date":"2026-07-08T21:53:41.000Z","feature_image":{"uid":"bltd7341aaba39a9a6a","_version":1,"title":"EXX-Blog-amd-instinct-mi350i.jpg","parent_uid":"bltca9c53775898e821","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-07-08T21:55:03.987Z","updated_at":"2026-07-08T21:55:03.987Z","content_type":"image/jpeg","file_size":"143067","filename":"EXX-Blog-amd-instinct-mi350i.jpg","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-07-08T23:39:33.454Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltd7341aaba39a9a6a/6a4ec737fdb55694141042a1/EXX-Blog-amd-instinct-mi350i.jpg","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltd7341aaba39a9a6a/6a4ec737fdb55694141042a1/EXX-Blog-amd-instinct-mi350i.jpg","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltd7341aaba39a9a6a/6a4ec737fdb55694141042a1/EXX-Blog-amd-instinct-mi350i.webp","queryParams":{}},"seo_meta_data":{"title":"AMD Instinct MI350P - Why PCIe Data Center GPUs Still Matter | Exxact Blog","description":"AMD Instinct MI350P brings CDNA 4 performance to standard PCIe servers for on-prem AI inference without OAM infrastructure.","keywords":"AMD Instinct MI350P, MI350P PCIe GPU, AMD CDNA 4, data center PCIe accelerator, HBM3E GPU, AI inference on-prem, PCIe Gen5 x16 GPU, MI350X comparison, enterprise AI deployment, RAG inference hardware, GPU memory bandwidth, 600W PCIe GPU, passive air-cooled GPU, traditional server AI, Exxact TensorEX","og_title":"AMD Instinct MI350P - Why PCIe Data Center GPUs Still Matter | Exxact Blog","og_description":"AMD Instinct MI350P brings CDNA 4 performance to standard PCIe servers for on-prem AI inference without OAM infrastructure.","og_image":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltd7341aaba39a9a6a/6a4ec737fdb55694141042a1/EXX-Blog-amd-instinct-mi350i.jpg","collection":""},"tags":["amd","gpu","ai","instinct"],"title":"AMD Instinct MI350P - Why PCIe Data Center GPUs Still Matter","unique_pageviews":"","unique_quarterly_views":0,"updated_at":"2026-07-08T23:39:43.665Z","updated_by":"bltf69a05e284b3d18a","url":"/blog/news/amd-instinct-mi350p","publish_details":{"time":"2026-07-08T23:39:48.052Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"routerLink":"/blog/news/amd-instinct-mi350p","queryParams":{}},{"uid":"blt14b600f7b0f27dc9","locale":"en-us","_version":7,"ACL":{},"_in_progress":false,"ad_blocks_side":[],"article":"","blog_category":[{"_version":7,"locale":"en-us","uid":"blt30782cc2c304a058","ACL":{}
341,"_in_progress":false,"created_at":"2020-08-31T20:22:17.797Z","created_by":"blt13a2948a46a0ad84","description":"","tags":[],"title":"News","updated_at":"2021-08-20T18:18:52.183Z","updated_by":"bltc816d579f2d0d71a","url":"/blog/News","_content_type_uid":"exx_blog_category","publish_details":{"environment":"bltfe7355d60efbec6e","locale":"en-us","time":"2021-08-20T18:18:53.082Z","user":"bltc816d579f2d0d71a"},"routerLink":"/blog/News","queryParams":{}}],"blog_content_v3":{"new_or_old_blog":"new_blog","blog_title":"NVIDIA GTC 2026 Recap - Focusing on The Era of Tokens & Inference ","enable_table_of_contents":true,"feature_image":{"feature_image_link":"","feature_image_file":{"uid":"bltb0df289c713c0450","_version":1,"parent_uid":"blt50d5c471d4007ad8","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-03-17T23:18:02.625Z","updated_at":"2026-03-17T23:18:02.625Z","content_type":"image/jpeg","file_size":"221971","filename":"EXX-Blog-NVDIA-GTC-2026-Recap.jpg","title":"EXX-Blog-NVDIA-GTC-2026-Recap.jpg","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-03-18T00:01:25.164Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltb0df289c713c0450/69b9e12a42c8c41bfa342b43/EXX-Blog-NVDIA-GTC-2026-Recap.jpg","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltb0df289c713c0450/69b9e12a42c8c41bfa342b43/EXX-Blog-NVDIA-GTC-2026-Recap.jpg","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltb0df289c713c0450/69b9e12a42c8c41bfa342b43/EXX-Blog-NVDIA-GTC-2026-Recap.webp","queryParams":{}},"feature_image_alt":"NVIDIA GTC 2026 Recap - Focusing on The Era of Tokens & Inference "},"section":{"group":[{"section_title":"NVIDIA GTC 2026 Recap Introduction","_metadata":{"uid":"cs435ab4ce15a581b8"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>NVIDIA GTC 2026 Recap Introduction\u003C/h2>\u003Cp>NVIDIA's GPU Technology Conference returned to San Jose this year with the energy of a company that knows it's at the center of one of the biggest technological shifts in history. Jensen Huang took the stage to cover everything from chip architecture and inference economics to open-source agentic frameworks and physical robotics — a lot of ground for one keynote.\u003C/p>\u003Cp>At Exxact, we pay close attention to GTC every year because what NVIDIA announces directly shapes the AI infrastructure landscape our customers are building on. This recap breaks down the highlights and what they mean for organizations investing in AI today.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"CUDA Turns 20. The Foundation Behind NVIDIA's Platform","_metadata":{"uid":"cs98cadaa35ae98d8b"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>CUDA Turns 20. The Foundation Behind NVIDIA's Platform\u003C/h3>\u003Cp>CUDA, NVIDIA's parallel computing platform, turned 20 this year. Jensen used the anniversary as a framing device for the rest of the keynote, most of what NVIDIA has built, and continues to build, traces back to this foundation.\u003C/p>\u003Cp>A few things worth noting about where CUDA stands today:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Installed base:\u003C/strong> Hundreds of millions of CUDA-capable GPUs are deployed across every major cloud provider, OEM, and industry vertical.\u003C/li>\u003Cli>\u003Cstrong>Backward compatibility:\u003C/strong> NVIDIA continues to optimize its software stack across older architectures, meaning deployed hardware gains performance improvements over time without replacement.\u003C/li>\u003Cli>\u003Cstrong>Ecosystem scale:\u003C/strong> Thousands of libraries, frameworks, and tools are built on CUDA, with hundreds of thousands of public projects dependent on it.\u003C/li>\u003Cli>\u003Cstrong>Library downloads\u003C/strong> are growing faster than ever, reflecting the expanding range of workloads CUDA now supports, from traditional HPC to AI training, inference, and data processing.\u003C/li>\u003C/ul>\u003Cp>The core point Jensen made is that the value of NVIDIA's platform isn't just the hardware, it's the compounding effect of two decades of software, tooling, and developer adoption built on top of it. That installed base is why workloads running on older NVIDIA hardware, like Ampere, are still seeing rising cloud pricing despite being several generations old.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"The Inference Inflection Point","_metadata":{"uid":"cs63de6390d3a1f616"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>The Inference Inflection Point\u003C/h3>\u003Cp>For most of NVIDIA's history, training the model was the dominant and focus in AI workload. As of recently, that has shifted. Jensen made the case that inference is now the primary driver of compute demand and that the economics of AI businesses are increasingly defined by how efficie
341ntly they can generate tokens. The more tokens you can hold in memory, the faster you can process these tokens, and the faster you can generate tokens, the smarter the AI.\u003C/p>\u003Cp>Three developments over the past two years drove this shift:\u003C/p>\u003Cul>\u003Cli>\u003Ca href=\"/blog/deep-learning/generative-ai-use-cases\" target=\"_blank\">\u003Cstrong>Generative AI (2022–2023):\u003C/strong>\u003C/a> Models moved from perception and classification to content generation, fundamentally changing how compute is consumed.\u003C/li>\u003Cli>\u003Cstrong>Reasoning models (2024):\u003C/strong> Models like OpenAI's o1 and o3 introduced chain-of-thought processing — the model \"thinks\" before responding, which significantly increases both input and output token usage per query.\u003C/li>\u003Cli>\u003Ca href=\"/blog/deep-learning/agentic-ai-ai-that-reasons\" target=\"_blank\">\u003Cstrong>Agentic AI (2024–2025):\u003C/strong>\u003C/a> Tools like Claude Code enabled models to autonomously read files, write and test code, and iterate on tasks. This moved AI from answering questions to completing multi-step work, multiplying compute demand further.\u003C/li>\u003C/ul>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs5399f547ea5cba7c"},"select":"image_block","images_optional":{"image":[{"image_enlargement":true,"_metadata":{"uid":"cs1b252c9a6bd3ad7f"},"image_header":"","image_link":"","image_file":{"uid":"blt999e42dfcd3ce4c2","_version":1,"parent_uid":"blt50d5c471d4007ad8","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-03-17T23:18:02.939Z","updated_at":"2026-03-17T23:18:02.939Z","content_type":"image/png","file_size":"1007061","filename":"EXX-Blog-NVDIA-GTC-2026-Recap-1.png","title":"EXX-Blog-NVDIA-GTC-2026-Recap-1.png","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-03-18T00:01:25.147Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt999e42dfcd3ce4c2/69b9e12aee827a62c22052aa/EXX-Blog-NVDIA-GTC-2026-Recap-1.png","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt999e42dfcd3ce4c2/69b9e12aee827a62c22052aa/EXX-Blog-NVDIA-GTC-2026-Recap-1.png","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt999e42dfcd3ce4c2/69b9e12aee827a62c22052aa/EXX-Blog-NVDIA-GTC-2026-Recap-1.webp","queryParams":{}},"image_alt_text":"NVIDIA graph showing Tokens per Watt and Performance against Cost","ad_link":"","image_caption_subheader":"Source: NVIDIA"}]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs64ad85c2b029f292"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Cp>The cumulative effect: Jensen estimated that compute demand per AI workload has increased roughly 10,000x over two years, while overall usage has grown approximately 100x. This implying total compute demand on the order of 1 million times greater than two years ago.\u003C/p>\u003Cp>Jensen framed this shift with a concept worth understanding: the AI data center as a token factory.\u003C/p>\u003Cul>\u003Cli>Every data center has a fixed power ceiling; once built, that constraint doesn't change.\u003C/li>\u003Cli>The relevant metric for an AI operator is therefore \u003Cstrong>tokens per watt\u003C/strong> or how much useful AI output you can generate from your available power budget.\u003C/li>\u003Cli>A second axis matters equally: \u003Cstrong>token speed\u003C/strong> (latency per inference), which determines what tiers of service you can offer and at what price points.\u003C/li>\u003Cli>Higher token speed enables larger models, longer context windows, and more reasoning, all of which command higher pricing.\u003C/li>\u003C/ul>\u003Cp>
341This framing has direct implications for infrastructure decisions. The architecture you deploy determines your token throughput and token speed at a fixed power envelope which maps directly to revenue potential. Jensen's point was that this is how AI infrastructure ROI should be evaluated going forward, not just raw compute specs.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Vera Rubin: NVIDIA's Next-Generation AI Platform","_metadata":{"uid":"csb6c3834384563797"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Vera Rubin: NVIDIA's Next-Generation AI Platform\u003C/h2>\u003Cp>Blackwell is currently in full production and shipping at scale, but NVIDIA used GTC to formally introduce Vera Rubin, the next-generation platform architected around the demands of agentic AI workloads. The first Vera Rubin rack is already operational at Microsoft Azure.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs30c022cfca8b566f"},"select":"image_block","images_optional":{"image":[{"image_enlargement":true,"_metadata":{"uid":"cs0ae6a44ae0b9bda6"},"image_header":"","image_link":"","image_file":{"uid":"bltcf1b72bbcc2110b9","_version":1,"parent_uid":"blt50d5c471d4007ad8","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-03-17T23:18:02.945Z","updated_at":"2026-03-17T23:18:02.945Z","content_type":"image/png","file_size":"1025145","filename":"EXX-Blog-NVDIA-GTC-2026-Recap-2.png","title":"EXX-Blog-NVDIA-GTC-2026-Recap-2.png","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-03-18T00:01:25.199Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltcf1b72bbcc2110b9/69b9e12a321ab50a356036fc/EXX-Blog-NVDIA-GTC-2026-Recap-2.png","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltcf1b72bbcc2110b9/69b9e12a321ab50a356036fc/EXX-Blog-NVDIA-GTC-2026-Recap-2.png","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltcf1b72bbcc2110b9/69b9e12a321ab50a356036fc/EXX-Blog-NVDIA-GTC-2026-Recap-2.webp","queryParams":{}},"image_alt_text":"NVIDIA Vera Rubin Generation","ad_link":"","image_caption_subheader":""}]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"csca90253d92a96f2a"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>NVIDIA Vera Rubin NVL72\u003C/h3>\u003Cp>Vera Rubin is designed as a full-system platform, not just a new GPU. Key specs:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>3.6 exaflops\u003C/strong> of compute per rack\u003C/li>\u003Cli>\u003Cstrong>260 TB/s\u003C/strong> all-to-all NVLink bandwidth\u003C/li>\u003Cli>\u003Cstrong>72 GPUs\u003C/strong> connected via sixth-generation NVLink\u003C/li>\u003Cli>Fully liquid-cooled, including NVLink switching infrastructure\u003C/li>\u003Cli>Hot water cooling at 45°C, reducing data center cooling overhead\u003C/li>\u003C/ul>\u003Ch3>NVIDIA Groq 3 LPX\u003C/h3>\u003Cp>One of the more significant announcements was NVIDIA's acquisition of the Groq chip team and integration of the Groq 3 LPX into the Vera Rubin platform. The two chips serve fundamentally different roles:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Vera Rubin GPU:\u003C/strong> High memory capacity (288GB per chip), optimized for throughput-heavy workloads and KV cache storage\u003C/li>\u003Cli>\u003Cstrong>Groq 3 LPX:\u003C/strong> Massive on-chip SRAM, statically compiled, deterministic data flow optimized for low-latency token generation (decode)\u003C/li>\u003C/ul>\u003Cp>NVIDIA's inference orchestration software, Dynamo, splits the workload between them: Vera Rubin handles the prefill and attention portions of inference, while Groq handles the token generation (feedforward decode) stage. The result is \u003Cstrong>35x more throughput per megawatt\u003C/strong> compared to running Vera Rubin alone for latency-sensitive workloads.\u003C/p>\u003Ch3>NVIDIA Rubin Ultra\u003C/h3>\u003Cp>Beyond the standard Vera Rubin configuration, NVIDIA also announced Rubin Ultra, a higher-density variant using a new rack design called Kyber:\u003C/p>\u003Cul>\u003Cli>Connects \u003Cstrong>144 GPUs\u003C/strong> in a single NVLink domain (vs. 72 in standard Vera Rubin)\u003C/li>\u003Cli>Compute nodes slide in vertically and connect via a midplane backplane rather than copper cabling\u003C/li>\u003Cli>Enables NVLink scaling beyond what copper interconnects can support\u003C/li>\u003C/ul>\u003Cp>These two metric, throughput and token speed, map directly to the revenue potential of a given infrastru
341cture deployment. NVIDIA presented generation-over-generation comparisons at a fixed 1 gigawatt power envelope:\u003C/p>\u003Cul>\u003Cli>Blackwell delivered a substantial improvement over Hopper, and Vera Rubin is projected to deliver approximately 5x more revenue potential per gigawatt over Blackwell\u003C/li>\u003Cli>The architecture you deploy determines your output capacity at a fixed power budget, which sets a ceiling on what you can generate from that 1 gigawatt limit.\u003C/li>\u003C/ul>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs62f483521585759c"},"select":"ad_block","images_optional":{"image":[]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[{"uid":"blt9e79a738382ac083","_content_type_uid":"blog_ad_block"}]},{"section_title":"Hardware Roadmap","_metadata":{"uid":"cs66a335220dacb3b0"},"select":"image_block","images_optional":{"image":[{"image_enlargement":true,"_metadata":{"uid":"cs3becb1ec8f457075"},"image_header":"","image_link":"","image_file":{"uid":"blt7a1366926599577a","_version":1,"parent_uid":"blt50d5c471d4007ad8","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-03-17T23:18:02.954Z","updated_at":"2026-03-17T23:18:02.954Z","content_type":"image/png","file_size":"1166068","filename":"EXX-Blog-NVDIA-GTC-2026-Recap-3.png","title":"EXX-Blog-NVDIA-GTC-2026-Recap-3.png","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-03-18T00:01:25.129Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt7a1366926599577a/69b9e12a699c06f5081da19e/EXX-Blog-NVDIA-GTC-2026-Recap-3.png","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt7a1366926599577a/69b9e12a699c06f5081da19e/EXX-Blog-NVDIA-GTC-2026-Recap-3.png","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt7a1366926599577a/69b9e12a699c06f5081da19e/EXX-Blog-NVDIA-GTC-2026-Recap-3.webp","queryParams":{}},"image_alt_text":"NVIDIA hardware roadmap including Blackwell, Vera Rubin, Feynman","ad_link":"","image_caption_subheader":""}]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"csd70d55364d74bd47"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>Vera Rubin (current/near-term)\u003C/h3>\u003Cp>Two configurations will be available:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Standard Vera Rubin (Kyber rack):\u003C/strong> NVLink 144 with copper Scale-up\u003C/li>\u003Cli>\u003Cstrong>Vera Rubin with Oberon:\u003C/strong> NVLink 72 plus optical Scale-up, expandable to NVLink 576 via co-packaged optics and Spectrum 6 switching\u003C/li>\u003C/ul>\u003Cp>Both are in production. NVIDIA also noted the Vera CPU is already selling as a standalone product with high interest.\u003C/p>\u003Ch3>Rubin Ultra (next generation)\u003C/h3>\u003Cul>\u003Cli>New Kyber rack design supporting 144 GPUs in a single NVLink domain\u003C/li>\u003Cli>Introduces the Groq LP 35 chip, which will incorporate NVIDIA's MVRP compute structure for additional performance gains\u003C/li>\u003Cli>Co-packaged optics available for both Scale-up and Scale-out networking\u003C/li>\u003C/ul>\u003Ch3>Feynman (future generation)\u003C/h3>\u003Cul>\u003Cli>New GPU architecture paired with the Groq LP 40\u003C/li>\u003Cli>New CPU called Rosa (Rosalind), paired with BlueField 5 and ConnectX-10\u003C/li>\u003Cli>Both copper and co-packaged optical Scale-up supported\u003C/li>\u003Cli>Continues the annual architecture cadence NVIDIA has committed to\u003C/li>\u003C/ul>\u003Ch3>Key components shipping across generations\u003C/h3>\u003Cul>\u003Cli>\u003Cstrong>Vera CPU:\u003C/strong> Designed for high single-threaded performance and data processing workloads, using LPDDR5 memory for efficiency. Targeted at agentic orchestration and tool-use workloads\u003C/li>\u003Cli>\u003Cstrong>BlueField 4:\u003C/strong> New storage and networking DPU, with 100% of major storage vendors committed to the platform\u003C/li>\u003Cli>\u003Cstrong>Spectrum X with co-packaged optics:\u003C/strong> World's first CPO Ethernet switch, in full production\u003C/li>\u003Cli>\u003Cstrong>ConnectX 9:\u003C/strong> Updated networking paired with Vera CPU\u003C/li>\u003C/ul>\u003Cp>NVIDIA's broader point on the roadmap is that the platform is designed for c
341ontinuity. Each generation maintains backward compatibility, and the shift from copper to optical interconnects will happen incrementally across both Scale-up and Scale-out rather than as a hard cutover.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"AI Factories and Industry Adoption","_metadata":{"uid":"cs4ffc20d49be0ea67"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>AI Factories and Industry Adoption\u003C/h2>\u003Cp>A recurring theme throughout the keynote was NVIDIA's expansion beyond chips and systems into the infrastructure layer that surrounds them. Two areas reflect this most clearly: the tooling NVIDIA has built for designing and operating AI factories, and the breadth of industries now building on NVIDIA platforms.\u003C/p>\u003Ch3>NVIDIA DSX: Digital Twin Platform for AI Factories\u003C/h3>\u003Cp>As AI factories grow in scale and complexity, NVIDIA introduced DSX, an Omniverse-based digital twin platform built for designing, commissioning, and operating large-scale AI infrastructure. The core idea is that the various technology vendors involved in building a data center, cooling, power, networking, compute, need a shared environment to co-design before anything is physically built.\u003C/p>\u003Cp>DSX covers the full facility lifecycle:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Design and simulation:\u003C/strong> Thermal, electrical, mechanical, and network simulation using tools from partners including Siemens, Cadence, and Dassault Systemes\u003C/li>\u003Cli>\u003Cstrong>Virtual commissioning:\u003C/strong> Facilities can be validated digitally through Procore before construction begins, reducing delays\u003C/li>\u003Cli>\u003Cstrong>Live operations:\u003C/strong> Once a facility is running, the digital twin becomes an operational tool. AI agents monitor cooling, electrical systems, and grid signals in real time, feeding into NVIDIA's Max-Q system which dynamically adjusts compute workloads to maximize token throughput within power constraints\u003C/li>\u003C/ul>\u003Cp>The underlying argument is that at gigawatt scale, inefficiencies in how a facility is designed and operated translate directly to lost token output and lost revenue. NVIDIA estimates there is roughly a factor of two in recoverable efficiency across a typical AI factory deployment.\u003C/p>\u003Ch3>Industry Verticals\u003C/h3>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs34215fcc0189270b"},"select":"image_block","images_optional":{"image":[{"image_enlargement":true,"_metadata":{"uid":"cs16ee1ff54a704a36"},"image_header":"","image_link":"","image_file":{"uid":"blt849462342c13c9b5","_version":1,"parent_uid":"blt50d5c471d4007ad8","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-03-17T23:18:02.870Z","updated_at":"2026-03-17T23:18:02.870Z","content_type":"image/png","file_size":"922384","filename":"EXX-Blog-NVDIA-GTC-2026-Recap-4.png","title":"EXX-Blog-NVDIA-GTC-2026-Recap-4.png","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-03-18T00:01:25.183Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt849462342c13c9b5/69b9e12aee827a6a312052a6/EXX-Blog-NVDIA-GTC-2026-Recap-4.png","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt849462342c13c9b5/69b9e12aee827a6a312052a6/EXX-Blog-NVDIA-GTC-2026-Recap-4.png","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt849462342c13c9b5/69b9e12aee827a6a312052a6/EXX-Blog-NVDIA-GTC-2026-Recap-4.webp","queryParams":{}},"image_alt_text":"NVIDIA Industry Presence: Automotive, Financial, Healthcare, Industrial, Quantum, Retail, Robotics, Telco","ad_link":"","image_caption_subheader":""}]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs011637c5c55ffedb"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Cp>Jensen covered the range of industries now building on NVIDIA infrastru
341cture, with each relying on domain-specific Cuda-X libraries rather than general-purpose compute alone:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Financial services:\u003C/strong> The largest represented industry at GTC this year. Algorithmic trading is shifting from classical quantitative methods to deep learning models that discover patterns across large datasets without human feature engineering\u003C/li>\u003Cli>\u003Cstrong>Healthcare:\u003C/strong> Drug discovery, diagnostic AI agents, and physical AI robotics for clinical settings. NVIDIA cited this as going through its \"ChatGPT moment\" in terms of adoption velocity\u003C/li>\u003Cli>\u003Cstrong>Automotive:\u003C/strong> Self-driving and robotaxi platforms, covered in more detail in the robotics section below\u003C/li>\u003Cli>\u003Cstrong>Telecom:\u003C/strong> NVIDIA's Aerial platform enables AI-RAN, converting traditional base stations into AI inference infrastructure at the edge. Active partnerships with Nokia and T-Mobile\u003C/li>\u003Cli>\u003Cstrong>Industrial and manufacturing:\u003C/strong> Simulation-driven robot deployment for factory automation, with integration across major industrial automation vendors\u003C/li>\u003Cli>\u003Cstrong>Media and entertainment:\u003C/strong> Real-time AI for broadcast, live translation, and gaming, built on NVIDIA's Holoscan platform\u003C/li>\u003Cli>\u003Cstrong>Retail and CPG:\u003C/strong> Supply chain optimization and AI agents for customer support across a $35 trillion industry\u003C/li>\u003Cli>\u003Cstrong>Quantum computing:\u003C/strong> 35 companies at GTC building hybrid GPU-quantum systems using NVIDIA's cuQuantum platform\u003C/li>\u003C/ul>\u003Cp>The breadth here reflects a consistent pattern in how NVIDIA approaches verticals. Rather than positioning GPUs as general-purpose accelerators, each domain gets purpose-built libraries, Cuda-X, that solve specific algorithmic problems in that field. Jensen described these libraries as the core of what differentiates NVIDIA's platform long term.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"OpenClaw and the Agentic AI Framework","_metadata":{"uid":"csc98f7dd9d6287b47"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>OpenClaw and the Agentic AI Framework\u003C/h2>\u003Cp>One of the more significant announcements at GTC was NVIDIA's support for OpenClaw, an open-source agentic AI framework that has seen unusually rapid adoption since its release. Jensen described it as the fastest adopted open-source project in history, surpassing Linux's 30-year install base within weeks.\u003C/p>\u003Ch3>What is OpenClaw?\u003C/h3>\u003Cp>OpenClaw functions as an operating system for AI agents. The components map closely to what a traditional OS provides:\u003C/p>\u003Cul>\u003Cli>Resource management across file systems, tools, and language models\u003C/li>\u003Cli>Task scheduling and cron job execution\u003C/li>\u003Cli>Problem decomposition: breaking a prompt into sequential steps\u003C/li>\u003Cli>Sub-agent spawning for parallel workstreams\u003C/li>\u003Cli>Multimodal I/O including text, voice, and messaging\u003C/li>\u003C/ul>\u003Cp>The practical result is that anyone can spin up a functioning AI agent by running a single command. The agent connects to a language model, receives a task, and executes it autonomously across tools and systems.\u003C/p>\u003Cp>The capability that makes OpenClaw useful also creates security risk in a corporate environment. An agentic system with access to internal infrastructure can read sensitive data, execute code, and communicate externally. NVIDIA worked with OpenClaw's author to address this with an enterprise-ready reference design called NeMo Claw, which adds:\u003C/p>\u003Cul>\u003Cli>A policy engine for governing agent behavior\u003C/li>\u003Cli>Network guardrails\u003C/li>\u003Cli>A privacy router to prevent unauthorized data exfiltration\u003C/li>\u003Cli>Integration hooks for existing enterprise SaaS policy systems\u003C/li>\u003C/ul>\u003Cp>NeMo Claw is available to download and deploy and is designed to connect with the policy engines that enterprise softw
341are vendors already maintain.\u003C/p>\u003Cp>Jensen's framing was that OpenClaw represents the same kind of inflection point for enterprise software that Linux, HTML, and Kubernetes each represented in prior computing eras. The argument is straightforward: just as every company eventually needed a Linux strategy, a web strategy, and a cloud-native strategy, every company now needs an agentic AI strategy.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Open Frontier Models for Every Industry","_metadata":{"uid":"csa4744a3c8278f2b2"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Open Frontier Models for Every Industry\u003C/h2>\u003Cp>Alongside its hardware and infrastructure announcements, NVIDIA outlined its position as a contributor to open AI models. Rather than a single general-purpose model, NVIDIA is developing and releasing six domain-specific model families, each targeting a different field.\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Nemotron:\u003C/strong> Language models covering reasoning, visual understanding, retrieval-augmented generation, safety, and speech. Nemotron-3 Ultra is positioned as a base model for fine-tuning and sovereign AI deployments. Nemotron-4 is in active development\u003C/li>\u003Cli>\u003Cstrong>Cosmos:\u003C/strong> World foundation models for physical AI, focused on generating and understanding synthetic environments for robotics and autonomous systems training\u003C/li>\u003Cli>\u003Cstrong>Alpamayo:\u003C/strong> Described as the world's first reasoning-capable autonomous vehicle AI. Handles real-time decision making, narration of driving actions, and instruction following\u003C/li>\u003Cli>\u003Cstrong>Groot:\u003C/strong> Foundation models for general-purpose humanoid and industrial robotics, covering whole-body control, manipulation, and policy generation\u003C/li>\u003Cli>\u003Cstrong>BioNeMo:\u003C/strong> Open models for biology, chemistry, and molecular design, targeting drug discovery and life sciences research\u003C/li>\u003Cli>\u003Cstrong>Earth2:\u003C/strong> Weather and climate forecasting models grounded in AI physics simulation\u003C/li>\u003C/ul>\u003Cp>Each family is being actively maintained and updated. NVIDIA framed the ongoing investment in these models, rather than the models themselves, as the core value proposition for organizations building on them.\u003C/p>\u003Cp>A thread running through this section of the keynote was sovereign AI, the ability for individual countries and organizations to build and run their own models rather than depending on a small number of large external providers. NVIDIA's open model initiative, combined with its on-premises infrastructure options, is positioned as the technical foundation for that capability.\u003C/p>\u003Ch3>Robotics and Physical AI\u003C/h3>\u003Cp>Jensen dedicated a significant portion of the keynote to robotics, framing it as the next major frontier for AI after language and reasoning models. The core argument is that the same capabilities that enabled agentic AI in software, perception, reasoning, and action, now need to be extended to systems operating in the physical world.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs12335e6c743399fd"},"select":"image_block","images_optional":{"image":[{"image_enlargement":true,"_metadata":{"uid":"csacd626856befb8c0"},"image_header":"","image_link":"","image_file":{"uid":"blt8f8b1652937c4b7b","_version":1,"parent_uid":"blt50d5c471d4007ad8","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-03-17T23:18:03.058Z","updated_at":"2026-03-17T23:18:03.058Z","content_type":"image/png","file_size":"2397923","filename":"EXX-Blog-NVDIA-GTC-2026-Recap-5.png","title":"EXX-Blog-NVDIA-GTC-2026-Recap-5.png","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-03-18T00:01:25.228Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt8f8b1652937c4b7b/69b9e12b67be9e069d70bdaa/EXX-Blog-NVDIA-GTC-2026-Recap-5.png","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt8f8b1652937c4b7b/69b9e12b67be9e069d70bdaa/EXX-Blog-NVDIA-GTC-2026-Recap-5.png","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt8f8b1652937c4b7b/69b9e12b67be9e069d70bdaa/EXX-Blog-NVDIA-GTC-2026-Recap-5.webp","queryParams":{}},"image_alt_text":"NVIDIA's range in Robotics and Automotive","ad_link":"","
341image_caption_subheader":""}]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs0a50e67ddfd9d901"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Cp>NVIDIA provides three distinct computers for robotics development:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Training computer:\u003C/strong> For developing and training robot AI models at scale via DGX Rubin\u003C/li>\u003Cli>\u003Cstrong>Simulation computer:\u003C/strong> For synthetic data generation using Isaac Lab, Newton physics simulation, and Cosmos world models via MGX and RTX PRO.\u003C/li>\u003Cli>\u003Cstrong>Edge computer:\u003C/strong> The onboard compute that runs inside the robot itself via Jetson and Thor.\u003C/li>\u003C/ul>\u003Cp>The simulation layer is particularly important because real-world data collection alone is insufficient to train robots across the full range of edge cases they will encounter in deployment.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"AI Infrastructure Planning for the Inference Era","_metadata":{"uid":"csdd2bb02f7000564d"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>AI Infrastructure Planning for the Inference Era\u003C/h2>\u003Cp>GTC 2026 reinforced that the shift from training to inference as the dominant AI workload has real implications for how infrastructure should be evaluated. Tokens per watt and token speed at a fixed power budget are increasingly the metrics that determine what a deployment can deliver. That framing should inform hardware decisions being made today.\u003C/p>\u003Cp>\u003Ca href=\"/category/NVIDIA-Blackwell\" target=\"_blank\">NVIDIA Blackwell\u003C/a> is the right platform to deploy now. It is in full production, the software stack is mature, and the ecosystem is well established. Vera Rubin is sampling well with broad availability ahead, and the Rubin Ultra to Feynman roadmap signals that NVIDIA's annual architecture cadence is not slowing down. Planning around that cadence, rather than waiting for it, is the more practical approach for most deployment timelines.\u003C/p>\u003Cp>As an NVIDIA Elite Partner and solution integrator, \u003Ca href=\"/\" target=\"_blank\">Exxact Corporation\u003C/a> works with organizations navigating these infrastructure decisions. From \u003Ca href=\"/category/Deep-Learning-Solutions\" target=\"_blank\">GPU servers and workstations\u003C/a> to full \u003Ca href=\"/category/Rack-Scale-HPC-Clusters\" target=\"_blank\">rack-scale deployments\u003C/a>, our team can help identify the right configuration for your workload and budget.\u003C/p>\u003Cp>&nbsp;\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]}]}},"blog_title":"NVIDIA GTC 2026 Recap - Focusing on The Era of Tokens & Inference ","created_at":"2026-03-18T00:00:52.433Z","created_by":"bltf69a05e284b3d18a","date":"2026-03-17T22:49:22.000Z","feature_image":{"uid":"bltb0df289c713c0450","_version":1,"parent_uid":"blt50d5c471d4007ad8","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-03-17T23:18:02.625Z","updated_at":"2026-03-17T23:18:02.625Z","content_type":"image/jpeg","file_size":"221971","filename":"EXX-Blog-NVDIA-GTC-2026-Recap.jpg","title":"EXX-Blog-NVDIA-GTC-2026-Recap.jpg","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-03-18T00:01:25.164Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltb0df289c713c0450/69b9e12a42c8c41bfa342b43/EXX-Blog-NVDIA-GTC-2026-Recap.jpg","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltb0df289c713c0450/69b9e12a42c8c41bfa342b43/EXX-Blog-NVDIA-GTC-2026-Recap.jpg","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/bltb0df289c713c0450/69b9e12a42c8c41bfa342b43/EXX-Blog-NVDIA-GTC-2026-Recap.webp","queryParams":{}},"seo_meta_data":{"title":"NVIDIA GTC 2026 Recap: Tokens & Inference | Exxact Blog","description":"Recap NVIDIA GTC 2026 with Vera Rubin/Groq, Era of Inference, AI factories, OpenClaw agents, and robotics.","keywords":"NVIDIA GTC 2026 recap, NVIDIA Vera Rubin, NVIDIA Rubin Ultra, AI inference economics, tokens per watt, token speed, GPU roadmap 2026, NVLink bandwidth, AI data center token factory, Dynamo inference orchestration, Groq LPX, AI factory digital twin DSX, OpenClaw agentic framework, NeMo Claw enterprise security, NVIDIA robotics and physical AI","og_title":"NVIDIA GTC 2026 Recap: Tokens & Inference | Exxact Blog","og_description":"Recap NVIDIA GTC 2026 with Vera Rubin/Groq, Era of Inference, AI factories, OpenClaw agents, and robotics.","og_image":"","collection":""},"tags":["nvidia","gtc","keynote"],"title":"NVIDIA GTC 2026 Recap - Focusing on The Era of Tokens & Inference","unique_pageviews":"","unique_quarterly_views":0,"updated_at":"2026-03-27T00:29:09.673Z","updated_by":"blt24f42d8cf20d5d45","url":"/blog/news/nvidia-gtc-2026-recap-era-of-tokens-and-inference","publish_details":{"time":"2026-03-27T00:29:29.303Z","user":"blt24f42d8cf20d5d45","environment":"bltfe7355d60efbec6e","locale":"en-us"},"routerLink":"/blog/news/nvidia-gtc-2026-recap-era-of-tokens-and-inference","queryParams":{}},{"uid":"bltfc9c98bfe5255080","locale":"en-us","_version":16,"ACL":{},"_in_progress":false,"ad_blocks_side":[],"article":"","blog_category":[{"_version":7,"locale":"en-us","uid":"blt30782cc2c304a058","ACL":{}
341,"_in_progress":false,"created_at":"2020-08-31T20:22:17.797Z","created_by":"blt13a2948a46a0ad84","description":"","tags":[],"title":"News","updated_at":"2021-08-20T18:18:52.183Z","updated_by":"bltc816d579f2d0d71a","url":"/blog/News","_content_type_uid":"exx_blog_category","publish_details":{"environment":"bltfe7355d60efbec6e","locale":"en-us","time":"2021-08-20T18:18:53.082Z","user":"bltc816d579f2d0d71a"},"routerLink":"/blog/News","queryParams":{}}],"blog_content_v3":{"new_or_old_blog":"new_blog","blog_title":"Exxact Validates First Air-Cooled 4x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation","enable_table_of_contents":true,"feature_image":{"feature_image_link":"","feature_image_file":{"uid":"blt0b012a0e61fa9adb","_version":1,"parent_uid":"blt39100be1a8d8480c","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2025-08-14T16:58:07.684Z","updated_at":"2025-08-14T16:58:07.684Z","content_type":"image/jpeg","file_size":"201022","filename":"Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.jpg","title":"Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.jpg","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2025-08-15T17:53:53.938Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0b012a0e61fa9adb/689e159fa2135501d24fd83c/Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.jpg","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0b012a0e61fa9adb/689e159fa2135501d24fd83c/Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.jpg","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0b012a0e61fa9adb/689e159fa2135501d24fd83c/Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.webp","queryParams":{}},"feature_image_alt":"First Validated 4x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation"},"section":{"group":[{"section_title":"A Problem Worth Solving","_metadata":{"uid":"cs3d11eafa13d7d757"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch5>3/12/2026: Update with Stress Test Graph\u003Cbr />8/18/2025: Initial Blog Upload\u003C/h5>\u003Ch2>A Problem Worth Solving\u003C/h2>\u003Cp>The NVIDIA® RTX PRO™ 6000 Blackwell Max-Q Workstation Edition GPU with 96GB of VRAM has made waves across all industries that value GPU acceleration and high VRAM: AI, Engineering, 3D Content Creation, Life Science, and more. With multi-GPU workstations, the deployment flexibility and performance capabilities are second to none.\u003C/p>\u003Cp>With multi-GPUs workstations, we have a long-standing validation process that every Exxact solution has to clear to ensure proper cooling of the high heat components like multiple GPUs, memory modules, processors, and other components.\u003C/p>\u003Cp>Validating four NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition GPUs in a workstation has been challenging due to a higher heat output than the previous generation. Three NVIDIA RTX PRO 6000 Max-Q were considered the thermal limit for air-cooled configurations, with other integrators resorting to liquid cooling. As of today, March 11, 2026, we still have yet to see another solution integrator offer an air-cooled 4x Max-Q configuration.\u003C/p>\u003Cp>However, at Exxact, we thought otherwise. NVIDIA had designed the NVIDIA RTX PRO 6000 Blackwell Max-Q to 
341scale to 4x GPUs in a workstation. We saw the opportunity as a challenge to offer an air-cooled 4x NVIDIA RTX PRO 6000 Blackwell Max-Q workstation. We are the first to proudly say, “We did it.”\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"csc06b69effccb4454"},"select":"image_block","images_optional":{"image":[{"image_enlargement":false,"_metadata":{"uid":"cs7ae78aa118d38366"},"image_header":"","image_link":"","image_file":{"uid":"blt092593b024415a9d","_version":1,"title":"EXX-blog-4-max-q-system (1).jpg","parent_uid":"blt39100be1a8d8480c","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2025-08-15T22:35:52.962Z","updated_at":"2025-08-15T22:35:52.962Z","content_type":"image/jpeg","file_size":"194188","filename":"EXX-blog-4-max-q-system_(1).jpg","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2025-08-15T22:36:01.374Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt092593b024415a9d/689fb64825099b894b6a0e22/EXX-blog-4-max-q-system_(1).jpg","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt092593b024415a9d/689fb64825099b894b6a0e22/EXX-blog-4-max-q-system_(1).jpg","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt092593b024415a9d/689fb64825099b894b6a0e22/EXX-blog-4-max-q-system_(1).webp","queryParams":{}},"image_alt_text":"Exxact workstation with four NVIDIA RTX PRO 6000 Blackwell Max-Q","ad_link":"","image_caption_subheader":""}]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"The First Quad NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation by Exxact","_metadata":{"uid":"csf0a8495ffdf8ca35"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>The First 4x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation by Exxact\u003C/h2>\u003Cp>Exxact Corporation is the first to deliver a 4x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition in a workstation without liquid cooling the GPUs. With careful chassis selection, airflow design, and thermal monitoring adjustments, we observed GPU stability peaking 85°C over a 3 hour OCCT stress test.\u003C/p>\u003Cp>Introducing the Exxact Valence 4x Max-Q Workstation. This testing was performed on an AMD Threadripper PRO 7000WX platform with all 8 channels of memory populated. Here are the specifications of the system tested in a fixed 24°C/75°F environment with the side panels on:\u003C/p>\u003Ctable>\u003Ctbody>\u003Ctr>\u003Ctd>Exxact Platform\u003C/td>\u003Ctd>\u003Ca href=\"/Exxact-VWS-135223847-E135223847\" target=\"_blank\">VWS-135223847\u003C/a>\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>CPU\u003C/td>\u003Ctd>AMD Threadripper PRO 9995WX on ASUS WRX90-SAGE SE\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>GPU\u003C/td>\u003Ctd>4x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Memory\u003C/td>\u003Ctd>8x DDR5 ECC 128GB DIMMs (total 1TB)\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Power Supply\u003C/td>\u003Ctd>2500W\u003C/td>\u003C/tr>\u003C/tbody>\u003C/table>\u003Cp>In a stock configuration without the Exxact cooling solution, we also observed four NVIDIA RTX PRO 6000 Blackwell Max-Qs thermal throttling, undervolting, and leaving valuable performance (and money) on the table. However, with our custom cooling solution, each of our GPUs pulled the maximum 300W TDP with a temperature below 90°C on all four cards. This goes without saying; deploying your system in a hotter environment or restricting front, side, or rear panel airflow can adversely affect performance.\u003C/p>\u003Cp>We are extremely proud of our engineering team for their ability to deliver the first validated 4x-GPU workstation featuring the extremely popular NVIDIA RTX PRO 6000 Blackwell Max-Q GPU. We ran these same tests on with great success:\u003C/p>\u003Cul>\u003Cli>AMD Threadripper PRO 9000WX\u003C/li>\u003Cli>Intel Xeon W-3500\u003C/li>\u003Cli>Dual Intel Xeon Scalable 5th Gen\u003C/li>\u003C/ul>\u003Cp>We are currently running tests on the new Intel Xeon 600 workstation processor platform.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs6fccc0020702075b"},"select":"image_block","images_optional":{"image":[{"image_enlargement":true,"_metadata":{"uid":"cs002e4103fcae8ac0"}
341,"image_header":"","image_link":"","image_file":{"uid":"blt5fed868cc4262ff4","_version":1,"title":"OCCT.jpg","parent_uid":"blt39100be1a8d8480c","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-03-13T18:49:02.450Z","updated_at":"2026-03-13T18:49:02.450Z","content_type":"image/jpeg","file_size":"554216","filename":"OCCT.jpg","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-03-13T19:15:52.917Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt5fed868cc4262ff4/69b45c1e7aa66b1fd8df4664/OCCT.jpg","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt5fed868cc4262ff4/69b45c1e7aa66b1fd8df4664/OCCT.jpg","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt5fed868cc4262ff4/69b45c1e7aa66b1fd8df4664/OCCT.webp","queryParams":{}},"image_alt_text":"Exxact Validates 4x Max-Q Air-Cooled Workstation OCCT Stress Test","ad_link":"","image_caption_subheader":""}]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cse956a9ae82266c48"},"select":"ad_block","images_optional":{"image":[]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[{"uid":"bltb09a7bf16d360553","_content_type_uid":"blog_ad_block"}]},{"section_title":"Why Air-Cooled versus Liquid-Cooled?","_metadata":{"uid":"cs30fbd86c79b3a25f"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Why Air-Cooled versus Liquid-Cooled?\u003C/h2>\u003Cp>Air cooling is still the most practical choice for many workstation users because:\u003C/p>\u003Cul>\u003Cli>It is \u003Cstrong>simpler to deploy\u003C/strong>, with fewer components and less setup.\u003C/li>\u003Cli>It is \u003Cstrong>easier to service\u003C/strong>, which matters for IT teams supporting multi-GPU fleets.\u003C/li>\u003Cli>It is \u003Cstrong>more predictable over time\u003C/strong>, with no pumps to fail, no coolant to maintain, and no risk of leaks.\u003C/li>\u003Cli>It reduces operational variables, which helps teams deploy, and transport the system faster.\u003C/li>\u003C/ul>\u003Cp>With today’s high-power GPUs, the tradeoff is that air-cooled systems must be engineered correctly. A quad-GPU build needs a chassis that supports high static pressure airflow, a fan and ducting strategy that feeds each card with cool intake air, and a thermal budget that keeps hotspots under control during sustained load.\u003C/p>\u003Cp>Liquid cooling can be a good fit when you are constrained by acoustic targets and need lower fan speeds, when you need maximum thermal headroom in warmer ambient environments, or when you are comfortable with additional maintenance and component complexity.\u003C/p>\u003Cp>For our validation, the goal was clear: \u003Cstrong>prove 4x RTX PRO 6000 Blackwell Max-Q stability in a real workstation, with the side panels on, using air cooling\u003C/strong>. By combining chassis selection, airflow optimization, and continuous thermal monitoring, we achieved stable performance at full 300W TDP per GPU without relying on liquid cooling.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Why the NVIDIA RTX 6000 PRO Blackwell Max-Q is Your Next GPU","_metadata":{"uid":"cs7c30fac2362a9de0"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Why the NVIDIA RTX 6000 PRO Blackwell Max-Q is Your Next GPU\u003C/h2>\u003Cp>The NVIDIA RTX PRO 6000 Blackwell Max-Q is designed with deployment flexibility and scalability in mind. It is a dual-slot workstation-class GPU design for multi-GPU deployments and can be fitted in a typical workstation and a multi-GPU server.\u003C/p>\u003Cp>It features the largest VRAM capacity in a workstation-class GPU with 96GB of GDDR7 per card; when deployed in a 4x GPU workstation configuration, 384GB of GPU memory can power almost any workload. Coupled with the new GDDR7, the memory bandwidth was increased, resulting in significantly better GPU-to-GPU and GPU-to-component communication.\u003C/p>\u003Cp>At Exxact, our customers are extremely excited to get their hands on this GPU. Here are some popular use cases we see that can leverage our Tensor Workstation featuring four NVIDIA RTX PRO 6000 Blackwell Max-Q:\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Multi-GPU for AI Training and Inference","_metadata":{"uid":"csb6f89cb75a135a18"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>Multi-GPU for AI Development and Local Inference\u003C/h3>\u003Cp>A \u003Cstrong>4x Max-Q GPU\u003C/strong> setup is ideal when you need high VRAM per desk for development, testing, and secure on-prem deployment. Example 4x Max-Q GPUs use cases:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>Local LLM serving (RAG):\u003C/strong> 96–192GB aggregate VRAM serves 70B–180B parameter models (e.g., Llama 3.1 70B, Mixtral 8x22B) fully on-prem without quantization compromises.\u003C/li>\u003Cli>\u003Cstrong>Fine-tuning:\u003C/strong> Enables full-parameter fine-tuning of 7B–13B models or LoRA runs on 70B models, cutting per-epoch time by 3–4x versus single-GPU.\u003C/li>\u003Cli>\u003Cstrong>Throughput-focused inference:\u003C/strong> Multi-GPU parallelism delivers 2–4x the token throughput of a single GPU for sustained multi-user workloads.\u003C/li>\u003Cli>\u003Cstrong>Multi-modal workloads:\u003C/strong> Run vision-language models like LLaVA-34B or InternVL2-40B locally without sending data offsite.\u003C/li>\u003C/ul>\u003Cp>
341Our Valence 4x Max-Q Workstation handles previously enterprise-only AI tasks. It can't train foundational LLMs (those require hundreds to thousands of GPUs), but it's a strong fit for serving 5–50 concurrent internal users or iterating on sub-100B parameter models without server infrastructure.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Multi-GPU for Engineering and MPD","_metadata":{"uid":"csadbd94a28ba93670"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>Multi-GPU for Engineering Simulation &amp; MPD\u003C/h3>\u003Cp>\u003Cstrong>4x Max-Q GPUs\u003C/strong> help when models are too large for a single GPU, or when teams want to run multiple design iterations in parallel from a desk-side workstation. Example 4x GPU workstation use cases:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>CFD and thermal:\u003C/strong> Handle mesh sizes of 50M–500M+ cells in solvers like Ansys Fluent — cases that would otherwise require cluster time.\u003C/li>\u003Cli>\u003Cstrong>FEA for large assemblies:\u003C/strong> Faster nonlinear and contact-heavy runs on assemblies exceeding 1M+ DOF, where GPU memory is the primary bottleneck.\u003C/li>\u003Cli>\u003Cstrong>Parameter sweeps and digital twins:\u003C/strong> Run 4 independent design variants simultaneously, compressing a day of sequential iteration into hours.\u003C/li>\u003Cli>\u003Cstrong>Pre/post visualization:\u003C/strong> Interactively review 10–100GB datasets locally without streaming to a remote node.\u003C/li>\u003C/ul>\u003Cp>High-performance GPUs increase productivity and iteration count, with typical simulation time reductions of 40–70% versus CPU-only workflows.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Multi-GPU for Life Science & Healthcare","_metadata":{"uid":"csa72b735cbe2fb137"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>Multi-GPU for Engineering Simulation &amp; MPD\u003C/h3>\u003Cp>\u003Cstrong>4x Max-Q GPUs\u003C/strong> help when models are too large for a single GPU, or when teams want to run multiple design iterations in parallel from a desk-side workstation. Example 4x GPU workstation use cases:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>CFD and thermal:\u003C/strong> Handle mesh sizes of 50M–500M+ cells in solvers like Ansys Fluent — cases that would otherwise require cluster time.\u003C/li>\u003Cli>\u003Cstrong>FEA for large assemblies:\u003C/strong> Faster nonlinear and contact-heavy runs on assemblies exceeding 1M+ DOF, where GPU memory is the primary bottleneck.\u003C/li>\u003Cli>\u003Cstrong>Parameter sweeps and digital twins:\u003C/strong> Run 4 independent design variants simultaneously, compressing a day of sequential iteration into hours.\u003C/li>\u003Cli>\u003Cstrong>Pre/post visualization:\u003C/strong> Interactively review 10–100GB datasets locally without streaming to a remote node.\u003C/li>\u003C/ul>\u003Cp>High-performance GPUs increase productivity and iteration count, with typical simulation time reductions of 40–70% versus CPU-only workflows.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Multi-GPU for 3D Content Creation & Rendering","_metadata":{"uid":"cs273377b137d6c2c6"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>Multi-GPU for 3D Design, Rendering, and Visualization\u003C/h3>\u003Cp>\u003Cstrong>4x Max-Q GPUs\u003C/strong> boost throughput for creators who need faster iteration and bigger scenes. Example 4x GPU workstation use cases:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>GPU rendering:\u003C/strong> Near-linear scaling in Octane, Redshift, and V-Ray GPU — 4 GPUs deliver roughly 3–3.8x the throughput of one card on complex production scenes.\u003C/li>\u003Cli>\u003Cstrong>Virtual production and real-time 3D:\u003C/strong> Maintain 120+ FPS headroom in Unreal Engine 5 with Lumen and Nanite enabled.\u003C/li>\u003Cli>\u003Cstrong>Large scene assembly:\u003C/strong> Handle geometry exceeding 500M–1B polygons and texture sets in the 50–200GB range without constant cache eviction.\u003C/li>\u003Cli>\u003Cstrong>AI-assisted workflows:\u003C/strong> Sub-second latency on upscaling, denoising, and generative passes that would stall on a single GPU.\u003C/li>\u003C/ul>\u003Cp>For studios and creators, multi-GPU shortens render queues and speeds up heavy GPU-accelerated workflows while keeping interactive work local for faster reviews and iteration.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Conclusion","_metadata":{"uid":"csa1e053d7beb07dc4"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Conclusion\u003C/h2>\u003Cp>At Exxact, we validate every configuration to ensure optimal performance in the most strenuous use case. Our goal is to deliver the most capable system that works right out of the box. Each system is designed to support a wide range of computing environments and workloads without compromise.\u003C/p>\u003Cp>The AMD Threadripper PRO 9995WX paired with 4x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition is the most performant workstation configuration for handling all types of workloads in AI, Engineering, Life Science, and 3D content. Want to configure this Exxact platform or any other 4x Max-Q workstations? Contact us today!\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs1d575584527d2f67"},"select":"ad_block","images_optional":{"image":[]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[{"uid":"blt7c3d4b068546a907","_content_type_uid":"blog_ad_block"}]}]}},"blog_title":"Exxact Validates First Air-Cooled 4x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation","created_at":"2025-08-14T18:04:37.579Z","created_by":"bltf69a05e284b3d18a","date":"2026-03-12T19:18:31.000Z","feature_image":{"uid":"blt0b012a0e61fa9adb","_version":1,"parent_uid":"blt39100be1a8d8480c","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2025-08-14T16:58:07.684Z","updated_at":"2025-08-14T16:58:07.684Z","content_type":"image/jpeg","file_size":"201022","filename":"Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.jpg","title":"Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.jpg","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2025-08-15T17:53:53.938Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0b012a0e61fa9adb/689e159fa2135501d24fd83c/Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.jpg","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0b012a0e61fa9adb/689e159fa2135501d24fd83c/Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.jpg","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0b012a0e61fa9adb/689e159fa2135501d24fd83c/Blog-Exxact-Validates-4x-NVIDIA-RTX-PRO-6000-Blackwell-Max-Q-Workstation.webp","queryParams":{}},"seo_meta_data":{"title":"Exxact Validates First Air-Cooled 4x RTX PRO 6000 Blackwell Max-Q | E
341xxact Blog","description":"Exxact successfully validated the first 4x NVIDIA RTX PRO 6000 Blackwell Max-Q workstation, delivering unprecedented GPU power for AI, engineering, and content creation workflows.","keywords":"NVIDIA RTX PRO 6000 Blackwell, Max-Q workstation, multi-GPU validation, 96GB VRAM GPU, 4x GPU workstation, GPU-accelerated computing, AI workstation, engineering simulation GPU, Blackwell architecture, professional GPU solution, Exxact Tensor Workstation, thermal optimization GPU, GDDR7 memory, workstation cooling solutions, high-performance computing","og_title":"Exxact Validates First Air-Cooled 4x RTX PRO 6000 Blackwell Max-Q | Exxact Blog","og_description":"Exxact successfully validated the first 4x NVIDIA RTX PRO 6000 Blackwell Max-Q workstation, delivering unprecedented GPU power for AI, engineering, and content creation workflows.","og_image":"","collection":""},"tags":[],"title":"Exxact Validates 4x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition GPUs","unique_pageviews":"","unique_quarterly_views":0,"updated_at":"2026-03-13T22:32:57.590Z","updated_by":"bltf69a05e284b3d18a","url":"/blog/news/exxact-validates-4x-nvidia-rtx-pro-6000-blackwell-max-q-in-a-workstation","publish_details":{"time":"2026-03-13T22:33:02.281Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"routerLink":"/blog/news/exxact-validates-4x-nvidia-rtx-pro-6000-blackwell-max-q-in-a-workstation","queryParams":{}},{"uid":"bltc9ed1484626ba1d5","_version":4,"locale":"en-us","ACL":{},"_in_progress":false,"ad_blocks_side":[],"article":"","blog_category":[{"_version":7,"locale":"en-us","uid":"blt30782cc2c304a058","ACL":{},"_in_progress":false,"created_at":"2020-08-31T20:22:17.797Z","created_by":"blt13a2948a46a0ad84","description":"","tags":[],"title":"News","updated_at":"2021-08-20T18:18:52.183Z","updated_by":"bltc816d579f2d0d71a","url":"/blog/News","_content_type_uid":"exx_blog_category","publish_details":{"environment":"bltfe7355d60efbec6e","locale":"en-us","time":"2021-08-20T18:18:53.082Z","user":"bltc816d579f2d0d71a"},"routerLink":"/blog/News","queryParams":{}}],"blog_content_v3":{"new_or_old_blog":"new_blog","blog_title":"Intel’s Long-Awaited Xeon 600 Processors for Workstation","enable_table_of_contents":true,"feature_image":{"feature_image_link":"","feature_image_file":{"uid":"blt0f9828746f6fac41","_version":1,"title":"EXX-Blog-Intel-xeon-600-cpu.jpg","parent_uid":"blta8d1ca46d4b9b43b","created_by":"bltf69a05e284b3d18a","updated_by":"bltf69a05e284b3d18a","created_at":"2026-02-20T01:40:51.009Z","updated_at":"2026-02-20T01:40:51.009Z","content_type":"image/jpeg","file_size":"189556","filename":"EXX-Blog-Intel-xeon-600-cpu.jpg","ACL":{},"is_dir":false,"tags":[],"publish_details":{"time":"2026-02-20T01:41:15.992Z","user":"bltf69a05e284b3d18a","environment":"bltfe7355d60efbec6e","locale":"en-us"},"url":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0f9828746f6fac41/6997bba399cddc000822b11f/EXX-Blog-Intel-xeon-600-cpu.jpg","routerLink":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0f9828746f6fac41/6997bba399cddc000822b11f/EXX-Blog-Intel-xeon-600-cpu.jpg","webp":"https://images.contentstack.io/v3/assets/blt71da4c740e00faaa/blt0f9828746f6fac41/6997bba399cddc000822b11f/EXX-Blog-Intel-xeon-600-cpu.webp","queryParams":{}},"feature_image_alt":"Intel’s Long Awaited Xeon 600 Processors for Workstation | Exxact Blog"},"section":{"group":[{"section_title":"Introduction","_metadata":{"uid":"cs63b463d5fb74557f"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch5>\u003Ca href=\"/Exxact-VWS-130523500-E130523500\" target=\"_blank\">Configure an Exxact Valence Workstation featuring Intel Xeon 600 processors ›\u003C/a>\u003C/h5>\u003Ch2>Introduction\u003C/h2>\u003Cp>After 3 years after the Intel Xeon W-3500 and about 1.5 years after the release of the Intel Xeon 6 Server in 2024, Intel’s new Xeon 600 processors bring server‑class compute capabilities into single‑socket workstations with up to 86 cores.\u003C/p>\u003Cp>By focusing on a single‑socket workstation platform, Xeon 600 simplifies deployment c
341ompared to dual‑socket servers while still supporting substantial parallel workloads. Exxact is looking forward to offering configurable Intel Xeon 600 for AI, scientific computing, and engineering simulation workloads.\u003C/p>\u003Cp>We will go over the specifications of the Intel Xeon 600, set to be officially released later this year, with a late March 2026 launch.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Intel Xeon 600 vs Intel Xeon W-3500/2500 Architectural Differences","_metadata":{"uid":"cs970f8848ebcdd9a0"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Intel Xeon 600 vs Intel Xeon W-3500/2500 Architectural Differences\u003C/h2>\u003Cp>Intel Xeon 600 utilizes the chiplet-based Granite Rapids architecture borrowed from their Xeon 6000 server processors. These new workstation processors will be supported on their new W890 motherboard and chipset beginning late March. Exxact is particularly excited to showcase the Intel Xeon 600 platform at \u003Ca href=\"/News-Events\" target=\"_blank\">NVIDIA GTC 2026;\u003C/a> come visit and say hello!\u003C/p>\u003Cp>Compared to the W-3500/2500 last-generation processors, the Intel Xeon 600 has\u003C/p>\u003Cul>\u003Cli>Up to 86 full-fat performance cores only\u003C/li>\u003Cli>Increased L2 and L3 cache\u003C/li>\u003Cli>Up to 128 PCIe 5.0 lanes\u003C/li>\u003Cli>9% increase in single-threaded performance\u003C/li>\u003Cli>61% increase in multi-threaded performance\u003C/li>\u003C/ul>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Intel Xeon 600 Specifications","_metadata":{"uid":"cs888216edf47d07a9"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Intel Xeon 600 Specifications\u003C/h2>\u003Cp>Intel Xeon 600 Series processors are workstation processors targeting professional workloads that benefit from many performance cores and wide memory and I/O subsystems.\u003C/p>\u003Ctable>\u003Ctbody>\u003Ctr>\u003Ctd>Model\u003C/td>\u003Ctd>Cores / Threads\u003C/td>\u003Ctd>Max Turbo Clock Speed\u003C/td>\u003Ctd>All Core Turbo Clock Speed\u003C/td>\u003Ctd>Base Clock Speed\u003C/td>\u003Ctd>PCIe Lanes\u003C/td>\u003Ctd>Cache\u003C/td>\u003Ctd>TDP\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 698X\u003C/td>\u003Ctd>86 / 172\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>3.0 GHz\u003C/td>\u003Ctd>2.0 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>336 MB\u003C/td>\u003Ctd>350W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 696X\u003C/td>\u003Ctd>64 / 128\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>3.5 GHz\u003C/td>\u003Ctd>2.4 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>336 MB\u003C/td>\u003Ctd>350W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 678X\u003C/td>\u003Ctd>48 / 96\u003C/td>\u003Ctd>4.9 GHz\u003C/td>\u003Ctd>3.8 GHz\u003C/td>\u003Ctd>2.4 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>192 MB\u003C/td>\u003Ctd>300W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 676X\u003C/td>\u003Ctd>32 / 64\u003C/td>\u003Ctd>4.9 GHz\u003C/td>\u003Ctd>4.3 GHz\u003C/td>\u003Ctd>2.8 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>144 MB\u003C/td>\u003Ctd>275W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 674X\u003C/td>\u003Ctd>28 / 56\u003C/td>\u003Ctd>4.9 GHz\u003C/td>\u003Ctd>4.3 GHz\u003C/td>\u003Ctd>3.0 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>144 MB\u003C/td>\u003Ctd>270W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 658X\u003C/td>\u003Ctd>24 / 48\u003C/td>\u003Ctd>4.9 GHz\u003C/td>\u003Ctd>4.3 GHz\u003C/td>\u003Ctd>3.0 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>144 MB\u003C/td>\u003Ctd>250W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 656\u003C/td>\u003Ctd>20 / 40\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>4.5 GHz\u003C/td>\u003Ctd>2.9 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>72 MB\u003C/td>\u003Ctd>210W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 654\u003C/td>\u003Ctd>18 / 36\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>4.5 GHz\u003C/td>\u003Ctd>3.1 GHz\u003C/td>\u003Ctd>128\u003C/td>\u003Ctd>72 MB\u003C/td>\u003Ctd>200W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 638\u003C/td>\u003Ctd>16 / 32\u003C/td>\u003Ctd>4.8 GHz\u003C/td>\u003Ctd>4.5 GHz\u003C/td>\u003Ctd>3.2 GHz\u003C/td>\u003Ctd>80\u003C/td>\u003Ctd>72 MB\u003C/td>\u003Ctd>180W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 636\u003C/td>\u003Ctd>12 / 24\u003C/td>\u003Ctd>4.7 GHz\u003C/td>\u003Ctd>4.5 GHz\u003C/td>\u003Ctd>3.5 GHz\u003C/td>\u003Ctd>80\u003C/td>\u003Ctd>48 MB\u003C/td>\u003Ctd>170W\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Intel Xeon 634\u003C/td>\u003Ctd>12 / 24\u003C/td>\u003Ctd>4.6 GHz\u003C/td>\u003Ctd>3.9 GHz\u003C/td>\u003Ctd>2.7 GHz\u003C/td>\u003Ctd>80\u003C/td>\u003Ctd>48 MB\u003C/td>\u003Ctd>150W\u003C/td>\u003C/tr>\u003C/tbody>\u003C/table>\u003Cp>As usual, high core count models have lower all-core turbo clock speeds, while lower core count models can clock higher. Understanding whether your workload prioritizes clock speed or core count will help you select the optimal processor. At Exx
341act, we can help configure the right system for your unique workload!\u003C/p>\u003Cp>&nbsp;\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs3b863f0a5a28c69c"},"select":"ad_block","images_optional":{"image":[]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[{"uid":"blt7c3d4b068546a907","_content_type_uid":"blog_ad_block"}]},{"section_title":"","_metadata":{"uid":"cs5f8b57e855366c01"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch3>Memory, PCIe 5.0, and CXL 2.0 Support\u003C/h3>\u003Cp>In the SKU stack, Intel Xeon 600 has split the 18-cores and higher and the 16-cores and lower options that differ in memory channels and PCIe lanes.\u003C/p>\u003Cp>Across the range, Xeon 600 supports DDR5 6400 MT/s RDIMM and 8000 MT/s MRDIMM, and plenty of PCIe 5.0 lanes.\u003C/p>\u003Cul>\u003Cli>Channels: 8 channels (on 18-cores-and-up SKUs), 4 channels on (16-cores-and-below SKUs)\u003C/li>\u003Cli>5.0 PCIe Lanes: 128 lanes (on 18-cores-and-up SKUs), 80 lanes (on 16-cores-and-below SKUs)\u003C/li>\u003C/ul>\u003Cp>The platform also provides substantial expansion headroom. Intel Xeon 600 processors for workstations feature 128 PCIe 5.0 lanes on the majority of SKUs, with 80 PCIe 5.0 lanes on lower core count models (16 cores and below).\u003C/p>\u003Cp>In addition to supporting more GPUs, storage devices, and networking, these PCIe lanes can also be used for added memory via CXL 2.0.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Intel Xeon 600 Workstations for AI/ML, Scientific Computing, and Engineering Simulation","_metadata":{"uid":"cs72537773f7226015"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Intel Xeon 600 Workstations for AI/ML, Scientific Computing, and Engineering Simulation\u003C/h2>\u003Cp>Intel Xeon 600 workstations can be tailored to a range of compute‑heavy workloads. Across AI/ML, scientific research, and engineering simulation, the most important build variables tend to be CPU core count, memory capacity and bandwidth, and PCIe expansion for GPUs, storage, and networking.\u003C/p>\u003Cp>Key considerations by workload:\u003C/p>\u003Cul>\u003Cli>\u003Cstrong>AI and Machine Learning\u003C/strong>\u003Cul>\u003Cli>Higher‑core Xeon 600 SKUs for data preprocessing, orchestration, and mixed CPU/GPU pipelines.\u003C/li>\u003Cli>Large DDR5 memory footprints to keep datasets and intermediate results in memory.\u003C/li>\u003Cli>Enough PCIe 5.0 lanes for dual or quad high‑end GPUs, plus NVMe storage and network adapters.\u003C/li>\u003C/ul>\u003C/li>\u003Cli>\u003Cstrong>Scientific Computing Research\u003C/strong>\u003Cul>\u003Cli>Strong multi‑core performance for molecular dynamics, cryo‑EM processing, and materials simulations.\u003C/li>\u003Cli>Multi‑channel DDR5 configurations tuned for bandwidth and total capacity.\u003C/li>\u003Cli>Expansion room for accelerator cards and high‑performance storage for large datasets and output files.\u003C/li>\u003C/ul>\u003C/li>\u003Cli>\u003Cstrong>Engineering Simulation\u003C/strong>\u003Cul>\u003Cli>Mid‑ to high‑core SKUs to support parallel solvers (CFD, FEA, particle methods).\u003C/li>\u003Cli>Memory sized to keep meshes and particle sets in RAM during computation.\u003C/li>\u003Cli>PCIe resources reserved for GPU accelerators (when supported) and fast NVMe scratch/results storage.\u003C/li>\u003C/ul>\u003C/li>\u003C/ul>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"Conclusion: Matching Xeon 600 to Real Requirements","_metadata":{"uid":"cs7ba6d55d0116fdf5"},"select":"text_block","images_optional":{"image":[]},"rich_text_editor":"\u003Ch2>Conclusion: Matching Xeon 600 to Real Requirements\u003C/h2>\u003Cp>The Intel Xeon 600 Processors for workstations will be another CPU option alongside the AMD Threadripper PRO for high-performance computing.\u003C/p>\u003Cp>As a solutions integrator, our role is to translate platform capabilities into concrete system configurations. We look at core count, memory channels and capacity, GPU count, storage layout, and network requirements, then align those with the actual models, simulations, and datasets you plan to run.\u003C/p>\u003Cp>A workstation can power the majority of the workloads for most individuals in AI research, scientific computing, and engineering simulation. Intel Xeon 600\u003C/p>\u003Cp>
341Talk to our engineering team about your code, typical problem sizes, and preferred software stack, and our team at Exxact is more than happy to \u003Ca href=\"/Exxact-VWS-130523500-E130523500\" target=\"_blank\">build your ideal Xeon 600 configuration\u003C/a> that is sized appropriately and fits your budget.\u003C/p>","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[]},{"section_title":"","_metadata":{"uid":"cs439e36aa3052513c"},"select":"ad_block","images_optional":{"image":[]},"rich_text_editor":"","tables_optional":{"table_reference":[],"table_custom":""},"ad_block_reference":[{"uid":"blt6e3ffc8dccd20ef8","_content_type_uid":"blog_ad_block"}]}]}},"blog_title":"Intel’s Long-Awaited Xeon 600 Processors for 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