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3separately through Sales."},{q:"What does Pro include that Basic does not?",a:"Pro unlocks full history on the GPU, RAM and token indices instead of 30 days, the complete Market Trends view with filters and charts, 36 months of Forward Curve with history date selection, and unlimited PriceIQ and SiteIQ runs. Basic keeps the same indices but caps history at 30 days, removes filters and charts, and meters the Application Library."},{q:"How much price history do I get?",a:"Basic includes 30 days of index history and six months of Forward Curve. Pro includes the full published history for every index and 36 months of Forward Curve, with the ability to select a history date. The indices update every business day, so a subscription covers both the historical series and ongoing daily values rather than a one-off extract. 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H100 and A100 are tracked across both segments, with each reading published separately so you can see the spread and how it moves."},{q:"Where can I access the Silicon Indices?",a:"Via the Silicon Data portal, direct API (e.g. ticker SDH100RT), and distribution partners including Kaiko."},{q:"How does Silicon Data collect pricing data?",a:"We aggregate real-time rental and private transaction data across neo-cloud providers, hyperscalers, colocation markets, and private rental platforms, then standardize it into a like-for-like benchmark."}]},cta:{headline:"Get access to Silicon Data",sub:"Full access to every Silicon Index, the complete portal, and historical pricing — distributed via web and API.",card:{title:"Silicon Data Portal Access",trial:"7-day trial",tagline:"Daily GPU and LLM market intelligence, built for institutional use.",features:["Silicon Index™ Dashboard — all GPU, CPU & accelerator indices","Market direction tracking","PriceIQ predictive modeling","Daily index values & 90-day historical pricing","NeoCloud vs Hyperscaler comparison","SiliconCarbon™"]},ctas:[{label:"Get started",href:"/pricing",variant:"primary"},{label:"Talk to sales",href:"/contact-us",variant:"secondary"}]}},m=[{slug:"h100",name:"H100",ticker:"SDH100RT"},{slug:"h200",name:"H200",ticker:"SDH200RT"},{slug:"a100",name:"A100",ticker:"SDA100RT"},{slug:"b200",name:"B200",ticker:"SDB200RT"},{slug:"b300",name:"B300",ticker:"--"},{slug:"mi300x",name:"MI300X",ticker:"SDMI3XRT"}],f=new Set(["mi300x","h200","b300"]);function g(e){let t=[...m.filter(t=>t.slug!==e.slug).map(e=>({name:e.name,ticker:f.has(e.slug)?"--":e.ticker,value:"$68",change:"2.1% (7D)",href:`/products/silicon-index/${e.slug}`})),{name:"LLM Token Expenditure",ticker:"SDLLMTK",value:"$68",change:"2.1% (7D)",href:"/products/llm-token-index"}];return{slug:e.slug,hero:{breadcrumb:["GPU Indices",e.name],eyebrow:`${e.name} Rental Price Index${f.has(e.slug)?"":` — ${e.ticker}`}`,headline:`Silicon Data ${e.name} Rental Price Index`,sub:e.dualSegment?`Daily benchmark for ${e.gpuName} hourly rental pricing, with separate neo-cloud and hyperscaler readings. ${f.has(e.slug)?"Published every day":`The Neo-Cloud index is published daily as ticker ${e.ticker}`}.`:`Daily benchmark for ${e.gpuName} hourly rental pricing — a single standardized reading across the GPU rental market. 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3 GPU rental pricing. It is published daily as a standardized, like-for-like hourly rate expressed in USD per GPU-hour, drawn from observations across neo-cloud providers, hyperscalers, colocation markets, and private rental platforms.${e.aboutLead?` ${e.aboutLead}`:""}`],e.dualSegment?["The index ",{b:"separates neo-cloud and hyperscaler pricing"},e.aboutMid??" into distinct readings so buyers can compare segments directly. Every observation entering the calculation is standardized for rental term length, cluster scale, and interconnect — the index reflects the underlying compute cost, not the headline rate on any single provider's website."]:["The index blends neo-cloud, hyperscaler, colocation, and private-market observations into a ",{b:"single standardized reading"},e.aboutMid??". Every observation entering the calculation is standardized for rental term length, cluster scale, and interconnect — the index reflects the underlying compute cost, not the headline rate on any single provider's website."],[e.aboutClose??`Investors and operators planning ${e.name} deployments pair index access with SiteIQ — the Silicon Data data center capacity-planning platform — to feed live ${e.name} prices into deployable-power, climate-adjusted PUE, and GPU payback models before committing capex.`]],stats:[{value:e.neoCloudPct??"95%",label:"Neo-cloud GPU providers tracked"},{value:"100%",label:"Major hyperscalers covered"}],footnote:e.coverageFootnote??"Together this represents more than 80% of the available global GPU rental market today."},regional:{eyebrow:"REGIONAL BREAKDOWN",headline:`${e.name} rental prices by region`,sub:"Regional averages reflect the same standardized methodology applied to providers operating in each region.",columns:["Region","Neo-cloud avg ($/GPU-hour)","Hyperscaler avg ($/GPU-hour)","Spread"],rows:e.regional??[],footnote:"Regional averages are updated on the same daily cadence as the global index."},drivers:{eyebrow:"PRICE DRIVERS",headline:`What drives ${e.name} rental prices`,body:e.driversBody??`${e.name} rental prices move with the broad balance of supply and demand for GPU capacity — new clusters and provider capacity coming online, the pace at which workloads shift toward newer accelerators, and the mix of on-demand versus reserved commitments. Because the index reports a standardized hourly rate, these forces show up as movements in a like-for-like benchmark rather than in any single provider's list price.`},methodology:{eyebrow:"Methodology",headline:`How the ${e.name} index is calculated`,sub:`The ${e.name} Silicon Index is calculated daily from observations across cloud providers, colocation markets, brokered cluster sales, and private rental platforms. Observations are standardized for machine specs, rental terms, platform performance, and geolocation, then filtered for outliers and independently validated before publication.`,cta:{label:"Read the full Silicon Data methodology",href:"/products/silicon-index#methodology"}},related:{eyebrow:"RELATED INDICES",headline:"Other Silicon Indices",cards:t},faq:{eyebrow:"",headline:"Frequently Asked Questions (FAQ)",items:[{q:`What is the current ${e.name} rental price?`,a:`The current ${e.name} rental price is ${e.price} per GPU-hour, based on the Silicon Data ${e.name} Rental Price Index${f.has(e.slug)?"":e.dualSegment?` (Neo-Cloud ticker ${e.ticker})`:` (ticker ${e.ticker})`}. The reading is a standardized, like-for-like rate across neo-cloud and hyperscaler providers${e.dualSegment?", and the two segment readings are published separately on this page":""}.`},...e.dualSegment?[{q:`Why does the ${e.name} price differ between neo-clouds and hyperscalers?`,a:"Hyperscaler on-demand capacity typically prices above neo-cloud and marketplace supply. The index publishes both segment readings so you can see the spread and how it moves."}]:[],{q:`What drives ${e.name} rental price changes?`,a:"New cluster availability and provider capacity additions, substitution pressure as other accelerators come online, and contract length (on-demand vs reserved). Every observation is standardized before it enters the index."},{q:`What does the ${e.name} Silicon Index track?`,a:`The realized, standardized hourly rental price of ${e.gpuName} capacity in USD per GPU-hour, drawn from neo-cloud providers, hyperscalers, colocation markets, and private rental platforms.`},...e.extraFaq??[],...f.has(e.slug)?[]:[{q:`How is the ${e.ticker} ticker calculated?`,a:`${e.ticker}

3 is published daily as a standardized, like-for-like hourly rate across tracked ${e.name} providers; observations are standardized for machine specs, rental terms, platform performance, and geography, statistical outliers removed, and each day's value independently validated.`}]]},cta:{headline:"Get access to Silicon Data",sub:"Full access to every Silicon Index, the complete portal, and historical pricing — distributed via web and API.",card:{title:"Silicon Data Portal Access",trial:"7-day trial",tagline:"Daily GPU and LLM market intelligence, built for institutional use.",features:["Silicon Index™ Dashboard — GPU, RAM & Token indices, full history","GPU Residual Value — market-calibrated hardware valuation","Forward Curve — 36 months forward","PriceIQ predictive modeling — unlimited","NeoCloud vs Hyperscaler comparison","SiliconMark™ Dashboard & SiliconCarbon™"]},ctas:[{label:"Get started",href:"/pricing",variant:"primary"},{label:"Talk to sales",href:"/contact-us",variant:"secondary"}]}}}g({slug:"h100",name:"H100",gpuName:"NVIDIA H100",ticker:"SDH100RT",price:"$2.53",dualSegment:!0,substitution:"newer accelerators such as the B200",regional:[["North America","$2.48","$3.12","+25.8%"],["Europe","$2.61","$3.34","+28.0%"],["Asia-Pacific","$2.72","$3.45","+26.8%"],["Middle East","$2.55","$3.28","+28.6%"]]}),g({slug:"h200",name:"H200",gpuName:"NVIDIA H200",ticker:"SDH200RT",price:"$3.10",substitution:"the B200 and newer Blackwell capacity",regional:[["North America","$2.95","$3.72","+26.1%"],["Europe","$3.10","$3.95","+27.4%"],["Asia-Pacific","$3.22","$4.08","+26.7%"],["Middle East","$3.04","$3.88","+27.6%"]],aboutLead:"The H200 pairs the Hopper compute of the H100 with 141GB of HBM3e memory and substantially higher bandwidth, making it the preferred choice for memory-bound inference and long-context serving.",coverageFootnote:"H200 shares the Hopper platform with H100, so provider coverage tracks closely as data centers upgrade memory-bound fleets.",driversBody:"H200 rental prices sit between H100 and B200 and move with the value of memory capacity and bandwidth. Its 141GB of HBM3e makes it the preferred choice for memory-bound inference and long-context serving, sustaining a premium over H100 wherever model size or KV-cache is the binding constraint. As Blackwell supply broadens, some frontier training demand migrates past H200 to B200, while inference demand keeps a durable floor under H200 rates. Because the index reports a standardized hourly rate, these forces show up as movements in a like-for-like benchmark rather than in any single provider's list price.",extraFaq:[{q:"How does H200 pricing compare to H100 and B200?",a:"H200 typically rents at a premium to H100 and below B200, reflecting its larger 141GB HBM3e memory and higher bandwidth on the same Hopper compute. The spread over H100 is widest for memory-bound inference and long-context workloads, while B200 commands more where raw training throughput is the priority. All indices are published on the same standardized, like-for-like basis, so the differences reflect real market pricing rather than methodology."}]}),g({slug:"a100",name:"A100",gpuName:"NVIDIA A100",ticker:"SDA100RT",price:"$1.29",dualSegment:!0,substitution:"the H100 and newer accelerators",regional:[["North America","$1.38","$1.79","+29.7%"],["Europe","$1.45","$1.92","+32.4%"],["Asia-Pacific","$1.51","$1.98","+31.1%"],["Middle East","$1.44","$1.90","+31.9%"]],aboutLead:"The A100 remains the workhorse GPU for inference pipelines, fine-tuning, and cost-sensitive training workloads.",aboutClose:"Operators sustaining A100 fleets for inference workloads use SiteIQ — the Silicon Data data center capacity-planning platform — to model deployable density, climate-adjusted PUE, and break-even utilization for ongoing capacity planning.",coverageFootnote:"A100 is the most widely distributed data-center GPU in the rental market. Older fleets remain in active service across inference-heavy providers.",driversBody:"A100 rental prices are pulled between three forces. Training workloads that can afford higher per-GPU throughput increasingly migrate up to H100 and B200, softening A100 demand at the high end, while established inference and fine-tuning pipelines stay on A100 for its unit economics — a durable price floor even as frontier training moves up the stack. At the same time, as large operators retire A100 clusters in favor of newer accelerators, that released capacity re-enters the on-demand market and compresses hourly rates. Because the index reports a standardized hourly rate, these forces show up as movements in a like-for-like benchmark rather than 

3in any single provider's list price.",extraFaq:[{q:"How does A100 pricing compare to H100 and B200?",a:"A100 rents well below H100 and B200 on a per-GPU-hour basis, reflecting its position as the prior-generation workhorse. The spread to H100 widens or narrows with how quickly training demand migrates to newer accelerators, while A100's floor is set by inference and fine-tuning workloads that stay on it for unit economics. All three indices are published on the same standardized, like-for-like basis, so the differences reflect real market pricing rather than methodology."}]}),g({slug:"b200",name:"B200",gpuName:"NVIDIA Blackwell B200",ticker:"SDB200RT",price:"$5.45",substitution:"additional B200 supply and H200 capacity",regional:[["North America","$3.95","$4.86","+23.0%"],["Europe","$4.18","$5.21","+24.6%"],["Asia-Pacific","$4.32","$5.38","+24.5%"],["Middle East","$4.06","$5.02","+23.6%"]],aboutLead:"The B200 is the successor to H100 — the flagship for frontier training workloads where per-GPU throughput gains on Blackwell justify a premium over Hopper.",aboutMid:". Supply remains constrained relative to demand, so expect wider bid-ask spreads than on older indices until broader provider rollout.",aboutClose:"Investors underwriting B200-based deployments use SiteIQ — the Silicon Data data center capacity-planning platform — to model the premium-cost economics, feeding live B200 prices into deployable power, climate-adjusted PUE, and payback timelines before committing capex.",coverageFootnote:"B200 coverage is expanding rapidly as more neo-cloud providers bring Blackwell capacity online.",driversBody:"B200 rental prices are set by a market where supply is still catching up with demand. Limited initial provider rollout keeps Blackwell capacity constrained relative to frontier training demand, and large AI labs locking in multi-year reserved capacity absorb much of that supply ahead of broader availability — both keeping on-demand rates elevated. Underpinning the premium is raw throughput: fewer B200s can do the work of substantially more H100s on modern training workloads, so per-GPU economics justify paying up. Because the index reports a standardized hourly rate, these forces show up as movements in a like-for-like benchmark rather than in any single provider's list price.",extraFaq:[{q:"How does B200 pricing compare to H100 and A100?",a:"B200 commands a clear premium over H100 and A100, reflecting Blackwell's throughput advantage and constrained early-rollout supply. The spread over H100 tracks how much of the market values per-GPU performance enough to pay up, and it is expected to compress as more providers bring Blackwell capacity online. All indices are published on the same standardized, like-for-like basis, so the differences reflect real market pricing rather than methodology."}]}),g({slug:"b300",name:"B300",gpuName:"NVIDIA B300",ticker:"--",price:"$6.95",substitution:"additional B300 supply and B200 capacity",aboutLead:"The B300 pairs the Blackwell compute of the B200 with 288GB of HBM3e memory and higher inference throughput, making it the preferred choice for frontier reasoning and long-context serving.",coverageFootnote:"B300 shares the Blackwell platform with B200, so provider coverage tracks closely as data centers bring Blackwell Ultra capacity online.",driversBody:"B300 rental prices sit above B200 and move with the value of memory capacity and inference throughput. Its 288GB of HBM3e makes it the preferred choice for frontier reasoning and long-context serving, sustaining a premium over B200 wherever model size or KV-cache is the binding constraint. As Blackwell Ultra supply broadens, that premium is expected to compress, while frontier training and inference demand keeps a durable floor under B300 rates. Because the index reports a standardized hourly rate, these forces show up as movements in a like-for-like benchmark rather than in any single provider's list price.",extraFaq:[{q:"How does B300 pricing compare to B200 and H200?",a:"B300 typically rents at a premium to B200 and well above H200, reflecting its larger 288GB HBM3e memory and higher inference throughput on the Blackwell platform. The spread over B200 is widest for frontier reasoning and long-context workloads, while H200 remains the value option for memory-bound Hopper deployments. All indices are published on the same standardized, like-for-like basis, so the differences reflect real market pricing rather than methodology."}]}),g({slug:"mi300x",name:"MI300X",gpuName:"AMD Instinct MI300X",ticker:"SDMI3XRT",price:"$2.12",substitution:"NVIDIA H100 and H200 capacity",regional:[["North America","$2.02","$2.61","+29.2%"],["Europe","$2.14","$2.79","+30.4%"],["Asia-Pacific","$2.21","$2.8

36","+29.4%"],["Middle East","$2.08","$2.71","+30.3%"]],aboutLead:"The MI300X is AMD's flagship data-center accelerator, configured with 192GB HBM3 memory — a configuration that outperforms alternatives on large-model inference and memory-bound training.",aboutMid:". The index moves on its own supply-demand cycle, largely independent of the NVIDIA indices.",aboutClose:"Teams evaluating MI300X for large-memory inference pair the index with SiteIQ — the Silicon Data data center capacity-planning platform — to model deployable density and hardware cooling profiles before committing capex.",coverageFootnote:"MI300X coverage grows as more neo-cloud providers add AMD-ready environments. Not every cloud currently offers MI300X — the index includes only those that do.",driversBody:"MI300X rental prices move on their own cycle, largely independent of NVIDIA supply dynamics. The pace of ROCm ecosystem maturity is the single largest driver — as more frameworks and inference engines mature on AMD, the substitution rate against NVIDIA GPUs grows. The 192GB HBM3 configuration sustains demand for large-model inference, particularly where fitting a full model on a single GPU removes tensor-parallel overhead. And AMD's share-capture pricing strategy means MI300X rates often move inversely to NVIDIA's rather than in lockstep. Because the index reports a standardized hourly rate, these forces show up as movements in a like-for-like benchmark rather than in any single provider's list price.",extraFaq:[{q:"How does MI300X pricing compare to H100 and B200?",a:"MI300X pricing moves on its own supply-demand cycle rather than tracking NVIDIA indices in lockstep — AMD's share-capture pricing often pushes rates inversely to Hopper and Blackwell. Where large-memory inference favors the 192GB HBM3 configuration, MI300X can undercut comparable NVIDIA capacity. All indices are published on the same standardized, like-for-like basis, so the differences reflect real market pricing rather than methodology."}]}),e.s(["aboutUs",0,{hero:{eyebrow:"ABOUT SILICON DATA",headline:"The data intelligence platform delivering systematic visibility and institutional benchmarks across the AI economy.",mediaLabel:"- Media Coverage -",mediaCoverage:[{name:"CNBC",logo:"cnbc",href:"https://www.cnbc.com/2026/05/12/new-futures-market-for-semiconductors-comes-as-ai-drives-costs-skyward.html",excerpt:"CNBC reports that traders will soon be able to bet on computer-chip prices as AI drives compute costs skyward — with CME Group building the first futures market on top of Silicon Data’s GPU rental benchmarks."},{name:"CME Group",logo:"cme-group",href:"https://www.cmegroup.com/media-room/press-releases/2026/5/12/cme_group_and_silicondatapartnertolaunchfirstcomputefutures.html",excerpt:"CME Group and Silicon Data partnered to launch the first compute futures, letting market participants hedge and price GPU rental costs using Silicon Data’s daily indices — a milestone in turning compute into a tradable asset class."},{name:"Bloomberg",logo:"media-0",href:"https://www.bloomberg.com/news/articles/2025-05-27/silicon-data-creates-first-of-its-kind-index-for-ai-chips?embedded-checkout=true",excerpt:"Silicon Data, a market-intelligence firm focused on cost transparency of graphics-processing units, has launched the world’s first daily index to track the specialized chips powering artificial intelligence. The flagship SDH100RT (also known as the H100 Re

3ntal Index) monitors the hourly cost of renting a top-tier GPU model."},{name:"The Wall Street Journal",logo:"media-wsj",href:"https://www.wsj.com/tech/ai/ai-needs-a-lot-of-computing-power-is-a-market-for-compute-the-next-big-thing-2302133c",excerpt:"As AI demand surges, computing power is becoming the world’s most valuable resource. Silicon Data provides the transparency layer for this new market — powering price discovery, benchmarking, and performance intelligence across the global GPU economy."},{name:"IEEE",logo:"media-1",href:"https://spectrum.ieee.org/gpu-prices",excerpt:"IEEE Spectrum highlights how Silicon Data has launched the world’s first daily rental-price index for high-end GPUs — tracking the hourly cost of renting a chip like the NVIDIA H100. The index draws on millions of data-points to bring pricing transparency to an otherwise opaque compute market."},{name:"Fortune",logo:"media-5",href:"https://fortune.com/2025/03/20/jd-vance-has-a-message-for-silicon-valley-the-trump-administration-is-working-with-you/",excerpt:"In an interview with Fortune, U.S. Vice President JD Vance emphasized the administration’s plans to work closely with technology and AI companies to strengthen domestic innovation and computing infrastructure. The piece highlights how AI firms and data-infrastructure players — including those building GPU-market transparency tools like Silicon Data — fit into broader national tech and industrial policy priorities."},{name:"Yahoo Finance",logo:"media-4",href:"https://finance.yahoo.com/news/silicon-data-secures-4-7m-120000230.html?guccounter=1",excerpt:"Silicon Data has secured $4.7 million in funding to accelerate its mission of bringing transparency and market structure to the global GPU economy. The round, backed by investors including DRW and Jump Trading Group, will support the expansion of Silicon Data’s benchmarking and pricing products such as SiliconMark and the H100 Re

3ntal Index."},{name:"AIM Research",logo:"media-6",href:"https://aimmediahouse.com/market-industry/silicon-datas-pitch-to-make-ai-costs-predictable",excerpt:"AIM Media House explores how Silicon Data is building the pricing and benchmarking layer for the AI infrastructure economy. By standardizing GPU performance metrics and introducing daily rental-price indices, the company aims to make compute costs more transparent and predictable for AI developers, data centers, and investors."}]},values:{heading:"WE WORK WITH HONESTY, OPENNESS, AND RESILIENCE",sub:"FINDING MEANING IN THE CHALLENGES AND JOY IN THE JOURNEY.",photo:"/assets/products/about-us/team-photo.png",items:[{icon:"integrity",name:"Integrity",body:"Speak truthfully, act with integrity, and give feedback with care."},{icon:"merit",name:"Merit",body:"Ideas win on merit. We reward impact, not credentials."},{icon:"open-minds",name:"Open Minds",body:"Value conviction, adapt quickly to better data and arguments."},{icon:"grit",name:"Grit",body:"Learn fast, bounce back stronger, thrive on challenge."},{icon:"fun",name:"Fun",body:"We take the work seriously not ourselves. Celebrate the journey."}]},leaders:[{name:"Donald Wilson",role:"Founder and CEO @ DRW",photo:"/assets/products/about-us/leader-don.png",photoSide:"left",photoAspect:"3 / 2",quote:"Silicon Data is at the leading edge of providing transparency into compute markets, I believe we’re in the early days of compute becoming the world's largest commodity, and market participants will need the infrastructure and risk management tools to efficiently navigate volatility and make informed decisions in this rapidly changing environment. Carmen and her team have a profound understanding of these dynamics, and we’re excited to support what they are building."},{name:"Carmen Li",role:"Founder and CEO @ Silicon Data",photo:"/assets/products/about-us/leader-carmen.png",photoSide:"right",photoAspect:"1 / 1",quote:"Silicon Data’s data aggregating capabilities would also make the development of compute market indices – which would track the price performance of a basket of GPU platforms in a similar fashion to a traditional financially settled commodity index – a very real possibility, further emphasizing how our product suite would secure widespread and democratic access to the buying and selling of compute products for all our customers."}],news:{heading:"NEWS ROOM",videoId:"Y_wM-_k3qTk",items:[{date:"May 12, 2026",location:"CHICAGO",title:"CME Group and Silicon Data Partner to Launch First Compute Futures",slug:"cme-group-and-silicon-data-partner-to-launch-first-compute-futures"},{date:"Apr 20, 2026",location:"New York",title:"Silicon Data Unveils First GPU Forward Curve, Signaling Transition of AI Compute into a Tradable Commodity",slug:"silicon-data-unveils-first-gpu-forward-curve"},{date:"Dec 5, 2025",location:"New York",title:"Silicon Data Announces Major Revision to A100 & H100 Rental Indices, Launches First-Ever B200 Index, and Introduces New Hyperscaler Rental Benchmarks",slug:"silicon-data-announces-major-revision-to-a100-h100-rental-indices-launches-first-ever-b200-index-and-introduces-new-hyperscaler-rental-benchmarks"},{date:"Oct 8, 2025",location:"New York",title:"Silicon Data Partners with dxFeed to Expand GPU Index Coverage on Refinitiv, Driving Vital Transparency in AI Infrastructure Markets",slug:"silicon-data-partners-with-dxfeed-to-expand-gpu-index-coverage-on-refinitiv-driving-vital-transparency-in-ai-infrastructure-markets"},{date:"May 20, 2025",location:"New York",title:"Silicon Data Secures $4.7M in Seed Funding and Expands Compute Industry Data Product Suite",slug:"silicon-data-secures-4-7m-in-seed-funding-and-expands-compute-industry-data-product-suite"},{date:"May 20, 2025",location:"New York",title:"Silicon Data Launches World's First Daily GPU Rental Index to Bring Transparency to the AI Infrastructure Economy",slug:"silicon-data-launches-world-s-first-daily-gpu-rental-index-to-bring-transparency-to-the-ai-infrastructure-economy-copy"}]},blog:{heading:"BLOG",cards:[{author:"Carmen Li",date:"May 1, 2026",title:"The GPU Forward Curve: Pricing the Curve, Not Just the Spot",excerpt:"Why a GPU forward curve is the missing primitive that turns compute rental into a tradeable, hedgeable asset class for financial institutions.",slug:"the-gpu-forward-curve-pricing-the-curve-not-just-the-spot"},{author:"Carmen Li",date:"Apr 27, 2026",title:"H100 Hyperscaler Index, April 2026: A Benchmark in Flat Mode",excerpt:"H100 Hyperscaler on-demand rates held flat from Mar 1–Apr 20, 2026, with a 0.47% coefficient of variation. Why that stability is the institutional signal.",slug:"h100-hyperscaler-index-april-2026"},{author:"Yang Gao",date:"Apr 2, 2026",title:"March 2026 RAM Price Update: DDR5 Spot Prices Dip While GDDR6 Stays Firm",excerpt:"DDR5 chip spot prices softened after the TurboQuant announcement while GDDR6 kept rising. Silicon Data’s RAM Index and March sample data explain why the late-March memory selloff was narrative-driven, not broad-based.",slug:"march-2026-ram-price-update"}]},finalCta:{headline:"Make better compute decisions today",ctas:[{label:"Getting Started",href:"/pricing",variant:"primary"},{label:"Talk to Sales",href:"/contact-us",variant:"secondary"}]}},"company",0,a,"forwardCurve",0,{hero:{eyebrow:"Forward Curve",headline:"GPU Term Rental Implied\nForward Rates",sub:"Time has a price. We publish it daily. GPU term rates, and the rental rates they imply for a future moment — forward rates. Out to 36 months, they become the term and forward curves.",ctas:[{label:"Get Started",href:"/pricing",variant:"primary"},{label:"Talk to sales",href:"/contact-us",variant:"secondary"}],image:"/assets/products/forward-curve/hero.png"},chart:{src:"https://portal.silicondata.com/forward-curve-chart",height:1100},segments:{eyebrow:"TARGET SEGMENT USE CASES",headline:"Built for Every Side of the Compute Market",sub:"From pricing strategy to derivatives origination — forward-looking intelligence for operators, buyers, and financiers.",cards:[{image:"/assets/products/forward-curve/card-gpu-providers.png",title:"GPU Providers & Data Centers",body:"Anchor your pricing strategy to market expectations. Benchmark rental rates against the term structure, identify contango or backwardation regimes, and structure long-term contracts with market-calibrated confidence."},{image:"/assets/products/forward-curve/card-ai-buyers.png",title:"AI Companies & Compute Buyers",body:"Time your procurement. Compare locked-in term rates against forward expectations to decide when to commit, when to wait, and when to negotiate. Validate every broker quote against the curve."},{image:"/assets/products/forward-curve/card-financial.png",title:"Financial Institutions & Risk",body:"The no-arbitrage forward rate is y

3our settlement reference. Price swaps, build structured products, underwrite GPU-collateralized loans, and design parametric insurance — all anchored to a published, methodology-backed forward curve."}]},methodology:{eyebrow:"METHODOLOGY & DATA SCHEMA",headline:"How the Term-Implied Forward Curve is built",items:[{icon:"data",title:"Real Market Data Collection",body:"We collect actual GPU rental transaction data and published rates across 95% of Neo-Cloud providers and 100% of major hyperscalers."},{icon:"structure",title:"Term Structure Construction",body:"Rental rates are organized by term length (1-month through 36-month) to build the term structure (the blue line)."},{icon:"formula",title:"No-Arbitrage Forward Derivation",body:"The implied forward rate (orange line) is mathematically derived from the term structure using established no-arbitrage pricing methodology."},{icon:"publish",title:"Daily Publication",body:"Both curves are recalculated every business day and can be accessed via the Silicon Data portal."}]},faq:{eyebrow:"FORWARD CURVE",headline:"Frequently Asked Questions (FAQ)",items:[{q:"What is the exact difference between the Term Structure Rate and the Implied Forward Rate?",a:"The Term Structure Rate (the blue line) represents the actual cost you would pay today to lock in a GPU rental for a specific duration (e.g., a 12-month contract). The Implied Forward Rate (the orange line) is a mathematical derivation that shows the market's expected spot rental price at a specific point in the future (e.g., what a 1-month rental will cost exactly 12 months from now)."},{q:"Are these just published “list prices” from cloud providers?",a:"No. The term structure is built from actual GPU rental transaction data and published rates collected daily across 95% of Neo-Cloud providers and 100% of major hyperscalers, then standardized per GPU-hour. The Implied Forward Rate is derived from that term structure using no-arbitrage pricing — it is a market-calibrated benchmark, not a single provider’s rate card."},{q:"Which GPU models are currently tracked on the Forward Curve?",a:"The Forward Curve currently tracks the Nvidia H100, B200 and A100 markets, as these possess the trading volume and liquidity required for robust forward pricing. Forward indices for other GPU models will be published as those markets mature."},{q:"Can the Forward Curve be used as a settlement reference for actual derivative contracts?",a:"Yes. Unlike static pricing aggregators, the Silicon Data Forward Curve is built specifically for financial application. By utilizing standard no-arbitrage derivation and publishing daily, the curve serves as a reliable, methodology-backed reference rate for pricing and settling compute futures, swaps, options, and parametric insurance products."},{q:"How do we ingest this data into our internal trading or FinOps models?",a:"Depending on your subscription tier, the Forward Curve data is accessible via direct REST API, the Silicon Data web portal, and directly through the Bloomberg Terminal (Ticker: SDH100RT)."},{q:"Is historical data available for backtesting?",a:"Yes. Full historical data archives of the forward curve are available to users on our Enterprise Tier, enabling quant desks and researchers to backtest models and analyze historical contango/backwardation regimes."}]},finalCta:{headline:"Get access to Silicon Data",sub:"Full access to every Silicon Index, the complete portal, and historical pricing — distributed via web and API.",card:{title:"Silicon Data Portal Access",trial:"7-day trial",tagline:"Daily GPU and LLM market intelligence, built for institutional use.",features:["Silicon Index™ Dashboard — all GPU, CPU & accelerator indices","Market direction tracking","PriceIQ predictive modeling","Daily index values & 90-day historical pricing","NeoCloud vs Hyperscaler comparison","SiliconCarbon™"]},ctas:[{label:"Get started",href:"/pricing",variant:"primary"},{label:"Talk to sales",href:"/contact-us",variant:"secondary"}]}},"gpuIndices",0,h,"home",0,{hero:{headline:"Compute market\nintelligence for\nbetter decisions",sub:"The data intelligence platform delivering systematic visibility\nand institutional benchmarks across the AI economy.",ctas:[{label:"Getting Started",href:"/pricing",variant:"primary"},{label:"Talk to Sales",href:"/contact-us",variant:"secondary"}],backgroundImage:"/assets/hero/server-room.jpg",backgroundVideo:"/assets/hero/home-intro.mp4",partnersEyebrow:"Our Partners",partners:[{name:"TSMC",src:"/assets/partners/logos/tsmc.svg",href:"https://www.tsmc.com",width:109,height:48},{name:"NVIDIA",src:"/assets/partners/logos/nvidia.svg",href:"https://www.nvidia.com",width:179,height:48},{name:"DRW",src:"/assets/partners/logos/drw.svg",href:"https://drw.com",width:108,height:48},{name:"Jump Trading",src:"/assets/partners/logos/jump-trading.svg",href:"https://www.jumptrading.com",width:113,height:48}]},productShowcase:[{slug:"silicon-index",label:"Silicon Data Indices",

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3cture budgets."}]},{id:"data-centers",eyebrow:"FOR DATA CENTERS",title:"Provide your customers peace\nof mind when buying compute",image:"/assets/products/nnf6CfPO01UHvXEbNDKRrlzQZk.png",features:[{title:"Differentiate with transparency",body:"Publish a results to prove real‑world performance to prospective customers."},{title:"Monitor market positioning",body:"Track competitor pricing and trends with Silicon Data’s daily indices and align offerings dynamically."},{title:"Enable sustainability reporting",body:"Integrate SiliconCarbon™ to provide clients with carbon‑impact metrics, winning ESG‑minded deals."}]}],backedByLeaders:{headline:"Backed by leaders in AI & finance",readMoreLink:{label:"Read about our fundraise",href:"/news-room/silicon-data-secures-4-7m-in-seed-funding-and-expands-compute-industry-data-product-suite"},quotes:[{quote:"Silicon Data® is at the leading edge of providing transparency into compute markets.",name:"Donald Wilson",role:"Founder and CEO @ DRW",photo:"/assets/team/don-wilson.png"},{quote:"The data aggregation capability of Silicon Data will also make the development of calculating market indices a very realistic possibility.",name:"Carmen Li",role:"Founder and CEO @ Silicon Data",photo:"/assets/team/carmen-li.png"}]},statBand:{datapointsLabel:"Over 3.5 million datapoints",datapointsValue:"3,500,000",distributionLabel:"Built to financial-index standards and distributed on the Bloomberg and Refinitiv.",distributionImage:"/assets/partners/distribution-partners.png",distributionAlt:"Bloomberg and Refinitiv Eikon"},finalCta:{headline:"Make better compute\ndecisions today",ctas:[{label:"Getting started",href:"/pricing",variant:"primary"},{label:"Talk to sales",href:"/contact-us",variant:"secondary"}],backgroundImage:"/assets/hero/server-room.jpg"}},"hubspotMeeting",0,{region:"na2",slug:"smyers1/silicon-data-intro-call",links:{default:"smyers1/silicon-data-intro-call",data:"smyers1/data-sales-request"},portalId:0xe6fc6b1,durationMs:18e5,rep:"Scott Myers",contactEmail:"[email protected]"},"llmTokenIndex",0,{hero:{breadcrumb:["Silicon Index","LLM Token Expenditure Index"],eyebrow:"Silicon Index — daily LLM token price benchmarks",headline:"LLM Token Expenditure Index Family",sub:"Daily benchmarks for what the market pays per million LLM tokens, across the broad market, open models, and proprietary models.\nThe flagship broad-market series is published on Bloomberg as SDLLMTK; Open and Proprietary series are available in the Silicon Data portal.",ctas:[{label:"Get Access",href:"/pricing",variant:"primary"},{label:"Talk to sales",href:"/contact-us",variant:"secondary"}]},chart:{tabs:[{key:"llm-token",label:"LLM Token",title:"LLM Token Expenditure Index",subtitle:"Daily readings of ticker SDLLMTK — Silicon Data's benchmark for blended LLM inference token prices.",caption:"LLM token expenditure trend (SDLLMTK), frontier segment, 7-day view. Source: Silicon Data,",src:"https://portal.silicondata.com/token-indexes-chart",baseline:{value:"1.02",change:"9.1% (7D)",trend:"down",asOf:"As of Aug 16, 2026"}},{key:"open-llm",label:"Open LLM",title:"Open LLM Token Expenditure Index",subtitle:"Daily readings of Silicon Data's benchmark for open LLM inference token prices.",caption:"Open LLM token expenditure trend, 7-day view. Source: Silicon Data,",src:"https://portal.silicondata.com/token-indexes-chart?token=open_expenditure",baseline:{value:"0.50",change:"13.3% (7D)",trend:"down",asOf:"As of Aug 16, 2026"}},{key:"proprietary-llm",label:"Proprietary LLM",title:"Proprietary LLM Token Expenditure Index",subtitle:"Daily readings of Silicon Data's benchmark for proprietary LLM inference token prices.",caption:"Proprietary LLM token expenditure trend, 7-day view. Source: Silicon Data,",src:"https://portal.silicondata.com/token-indexes-chart?token=closed_expenditure",baseline:{value:"2.16",change:"6.7% (7D)",trend:"down",asOf:"As of Aug 16, 2026"}}],unit:"USD / M tokens",note:"We are constantly updating our indices. For announcements, please visit ",noteLink:{label:"Index announcements",href:"/products/silicon-index#methodology"}},about:{eyebrow:"ABOUT THIS INDEX",headline:"What the LLM Token Expenditure Index tracks",intro:"The Silicon Data LLM Token Expenditure Index family is a set of daily benchmarks for the price of large-language-model tokens. Each series computes the usage-weighted price of one million tokens across a defined 

3LLM universe. The indices blend active provider pricing with real consumption volume observed across multi-provider routing gateways and inference networks, publishing a true effective price per million tokens daily.",indexList:[{title:"LLM Token Expenditure Index",chip:"Bloomberg ticker SDLLMTK",desc:"The broad-market benchmark covering the observable active LLM market."},{title:"Open LLM Token Expenditure Index",desc:"An internal portal benchmark covering open-source and open-weight models."},{title:"Proprietary LLM Token Expenditure Index",desc:"An internal portal benchmark covering closed-source models."}],stats:[{value:"200+",label:"Open models tracked"},{value:"120+",label:"Proprietary models tracked"},{value:"20+",label:"Price and volume sources"}],closing:"Posted price sheets alone do not describe the market: a model may remain listed while losing economic relevance, and real activity may concentrate in a different set of models. The indices price each market segment as it is consumed, not as it is cataloged. They are intended to answer practical questions:",pullQuote:"Given where real usage sits today, how much is the market actually paying for a million tokens?"},drivers:{eyebrow:"INDEX DRIVERS",headline:"How to read an LLM Token Expenditure Index move",cards:[{icon:"pricing",title:"Pricing",body:"The index moves when model providers change posted or reference token prices. These repricing events can directly raise or lower the effective cost of access, especially when they affect models with meaningful observed usage."},{icon:"mix",title:"Mix",body:"The index moves even when individual model prices are unchanged. If usage shifts toward higher-priced models, the index rises; if usage shifts toward lower-priced models, the index falls. This makes the index a measure of where demand is concentrating, not just what providers list."},{icon:"composition",title:"Composition",body:"The index evolves as the market evolves. Models can enter or leave the basket as their economic relevance changes, and a model's input/output token balance can shift as workloads change. These composition effects can alter the index without a headline provider price change."}]},methodology:{eyebrow:"Methodology",headline:"How the LLM index is calculated",sub:"The LLM Token Expenditure Index Family is calculated daily from observations across different model providers. Observations are normalized for input/output mix, context window, then filtered for models with sustained usage and market expenditure. 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It is a daily market reference that combines model pricing with market activity so users can understand what it effectively costs to access the active LLM market.\n\nWithin the index family, the broad-market index is complemented by Open LLM and Proprietary LLM views, allowing users to compare the active market with open-source/open-weight and closed-source model segments."},{q:"Why use the term “token expenditure” instead of “token price”?",a:"“Token price” is too narrow for the way LLM markets work. A customer does not experience one universal token price. They experience a mix of input tokens, output tokens, model choices, provider prices, and workload patterns. Two models can have similar posted prices but very different expenditure impact if one receives more market activity or has a different input-output mix.\n\nThis is why the same framework can support the broad-market, Open LLM, and Proprietary LLM views: each view measures expenditure conditions for a defined model universe rather than a single posted token price."},{q:"How does the index avoid becoming a static catalog of models?",a:"The index is designed to follow sustained economic relevance, not catalog availability. Models enter the benchmark basket because they show meaningful market activity, not simply because they appear on a provider page. Older models can lose influence as usage fades, and emerging models can gain influence as market adoption becomes durable.\n\nThe same principle applies across the Open LLM and Proprietary LLM series, where each segment follows sustained activity inside its own model universe."},{q:"What can cause the index to move up or down?",a:"The index can rise when providers increase prices, when usage shifts toward more expensive models, when output-heavy workloads become more important, or when high-expenditure models gain benchmark weight. It can fall when prices decline, when lower-cost models gain adoption, when users substitute toward more efficient model choices, or when the market diversifies away from expensive concentration.\n\nAcross the index family, a move in the Open LLM view may point to changing open-model competitiveness, while a move in the Proprietary LLM view may reflect shifts in the premium paid for closed-model capability, integration, or distribution."},{q:"How should customers use the index in practice?",a:"Customers can use the index as a market baseline for LLM cost conditions. Procurement teams can compare internal token spend against the broader market and separate vendor-specific exposure from market-wide movement. Finance teams can use it to monitor whether LLM access is becoming structurally more or less expensive. Product and platform teams can use it to understand whether their model-routing decisions are moving with or against the market.\n\nThe index family also helps customers compare whether market movement is being led by open models, proprietary models, or neither. If the broad-market index begins to follow the Open LLM view lower, that may suggest open models are gaining enough economic weight to influence the market benchmark. If it follows the Proprietary LLM view instead, demand may still be set by closed-model capability and willingness to pay. If neither view explains the broad-market move cleanly, the change may reflect broader mix, composition, or input-output dynamics across the market."},{q:"What does the index deliberately not disclose or claim?",a:"The index does not disclose proprietary raw usage inputs, or private commercial terms. It is also not a guarantee of any customer’s realized invoice, because individual bills can depend on private discounts, routing policy, workload design, c

3aching, context length, and provider-specific terms.\n\nThe index is meant to be a high-integrity benchmark, not a replacement for account-level cost accounting. It is supported by validation checks around market coverage, model activity, basket composition, weight concentration, and day-to-day movement."}]},finalCta:{headline:"Get access to Silicon Data",sub:"Put the LLM Token Expenditure Index to work — track frontier and open-weight token economics daily, alongside Silicon Data's full family of GPU and compute indices.",ctas:[{label:"Get Access",href:"/pricing",variant:"primary"},{label:"Talk to sales",href:"/contact-us",variant:"secondary"}]}},"navCompany",0,[{label:"About us",href:"/about-us",blurb:"Who we are and what we're building."},{label:"Careers",href:"/careers",blurb:"Open roles — help build the price layer for compute."},{label:"Data Partnerships",href:"/data-partnerships",blurb:"Contribute data and power the GPU economy's price layer."},{label:"Media",href:"/media",blurb:"Press releases, coverage, videos & brand kit."}],"navItems",0,[{label:"Products",href:"#",hasMenu:!0},{label:"Pricing",href:"/pricing"},{label:"Company",href:"#",hasMenu:!0},{label:"Resources",href:"#",hasMenu:!0}],"navProducts",0,i,"navResources",0,s,"pricing",0,d,"pricingTrialFirst",0,u,"productPath",0,o,"ramIndex",0,{hero:{eyebrow:"RAM Index™",headline:"Stop Guessing What Memory Costs",sub:"One number, every business day — the independent GDDR6 spot benchmark that procurement teams, analysts, and traders use to price, settle, and negotiate with confidence.",note:"Published daily at 16:00 UTC. Weighted. Transparent. Settlement-ready.",ctas:[{label:"Get RAM Index Access",href:"/pricing",variant:"primary"},{label:"Talk to sales",href:"/contact-us",variant:"secondary"}],image:"/assets/products/ram-index/hero.png"},problem:{headline:"GDDR6 Pricing Has No Standard — Until Now",body:["Today, GDDR6 memory pricing is scattered across private broker quotes, manufacturer NDAs, and back-channel deals. Procurement teams overpay because they can't verify what the market actually trades at. Analysts model GPU margins on stale, quarterly data. Traders can't settle contracts against a number both 

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