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19 * scheduler.production.js
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22 *
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24 * LICENSE file in the root directory of this source tree.
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26 * @license React
27 * react-dom.production.js
28 *
29 * Copyright (c) Meta Platforms, Inc. and affiliates.
30 *
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32 * LICENSE file in the root directory of this source tree.
33 */var jb;function C6(){if(jb)return En;jb=1;var r=uh();function e(h){var v="https://react.dev/errors/"+h;if(1<arguments.length){v+="?args[]="+encodeURIComponent(arguments[1]);for(var _=2;_<arguments.length;_++)v+="&args[]="+encodeURIComponent(arguments[_])}return"Minified React error #"+h+"; visit "+v+" for the full message or use the non-minified dev environment for full errors and additional helpful warnings."}function t(){}var o={d:{f:t,r:function(){throw Error(e(522))},D:t,C:t,L:t,m:t,X:t,S:t,M:t},p:0,findDOMNode:null},a=Symbol.for("react.portal");function d(h,v,_){var E=3<arguments.length&&arguments[3]!==void 0?arguments[3]:null;return{$$typeof:a,key:E==null?null:""+E,children:h,containerInfo:v,implementation:_}}var c=r.__CLIENT_INTERNALS_DO_NOT_USE_OR_WARN_USERS_THEY_CANNOT_UPGRADE;function g(h,v){if(h==="font")return"";if(typeof v=="string")return v==="use-credentials"?v:""}return En.__DOM_INTERNALS_DO_NOT_USE_OR_WARN_USERS_THEY_CANNOT_UPGRADE=o,En.createPortal=function(h,v){var _=2<arguments.length&&arguments[2]!==void 0?arguments[2]:null;if(!v||v.nodeType!==1&&v.nodeType!==9&&v.nodeType!==11)throw Error(e(299));return d(h,v,null,_)},En.flushSync=function(h){var v=c.T,_=o.p;try{if(c.T=null,o.p=2,h)return h()}finally{c.T=v,o.p=_,o.d.f()}},En.preconnect=function(h,v){typeof h=="string"&&(v?(v=v.crossOrigin,v=typeof v=="string"?v==="use-credentials"?v:"":void 0):v=null,o.d.C(h,v))},En.prefetchDNS=function(h){typeof h=="string"&&o.d.D(h)},En.preinit=function(h,v){if(typeof h=="string"&&v&&typeof v.as=="string"){var _=v.as,E=g(_,v.crossOrigin),M=typeof v.integrity=="string"?v.integrity:void 0,G=typeof v.fetchPriority=="string"?v.fetchPriority:void 0;_==="style"?o.d.S(h,typeof v.precedence=="string"?v.precedence:void 0,{crossOrigin:E,integrity:M,fetchPriority:G}):_==="script"&&o.d.X(h,{crossOrigin:E,integrity:M,fetchPriority:G,nonce:typeof v.nonce=="string"?v.nonce:void 0})}},En.preinitModule=function(h,v){if(typeof h=="string")if(typeof v=="object"&&v!==null){if(v.as==null||v.as==="script"){var _=g(v.as,v.crossOrigin);o.d.M(h,{crossOrigin:_,integrity:typeof v.integrity=="string"?v.integrity:void 0,nonce:typeof v.nonce=="string"?v.nonce:void 0})}}else v==null&&o.d.M(h)},En.preload=function(h,v){if(typeof h=="string"&&typeof v=="object"&&v!==null&&typeof v.as=="string"){var _=v.as,E=g(_,v.crossOrigin);o.d.L(h,_,{crossOrigin:E,integrity:typeof v.integrity=="string"?v.integrity:void 0,nonce:typeof v.nonce=="string"?v.nonce:void 0,type:typeof v.type=="string"?v.type:void 0,fetchPriority:typeof v.fetchPriority=="string"?v.fetchPriority:void 0,referrerPolicy:typeof v.referrerPolicy=="string"?v.referrerPolicy:void 0,imageSrcSet:typeof v.imageSrcSet=="string"?v.imageSrcSet:void 0,imageSizes:typeof v.imageSizes=="string"?v.imageSizes:void 0,media:typeof v.media=="string"?v.media:void 0})}},En.preloadModule=function(h,v){if(typeof h=="string")if(v){var _=g(v.as,v.crossOrigin);o.d.m(h,{as:typeof v.as=="string"&&v.as!=="script"?v.as:void 0,crossOrigin:_,integrity:typeof v.integrity=="string"?v.integrity:void 0})}else o.d.m(h)},En.requestFormReset=function(h){o.d.r(h)},En.unstable_batchedUpdates=function(h,v){return h(v)},En.useFormState=function(h,v,_){return c.H.useFormState(h,v,_)},En.useFormStatus=function(){return c.H.useHostTransitionStatus()},En.version="19.2.7",En}var Xb;function R6(){if(Xb)return Af.exports;Xb=1;function r(){if(!(typeof __REACT_DEVTOOLS_GLOBAL_HOOK__>"u"||typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.checkDCE!="function"))try{__REACT_DEVTOOLS_GLOBAL_HOOK__.checkDCE(r)}catch(e){console.error(e)}}return r(),Af.exports=C6(),Af.exports}/**
34 * @license React
35 * react-dom-client.production.js
36 *
37 * Copyright (c) Meta Platforms, Inc. and affiliates.
38 *
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l=s.pendingProps,u=!1,p=(s.flags&128)!==0,y;if((y=p)||(y=n!==null&&n.memoizedState===null?!1:(Gt.current&2)!==0),y&&(u=!0,s.flags&=-129),y=(s.flags&32)!==0,s.flags&=-33,n===null){if(rt){if(u?kr(s):Cr(),(n=St)?(n=pb(n,_o),n=n!==null&&n.data!=="&"?n:null,n!==null&&(s.memoizedState={dehydrated:n,treeContext:yr!==null?{id:us,overflow:ps}:null,retryLane:536870912,hydrationErrors:null},i=V1(n),i.return=s,s.child=i,vn=s,St=null)):n=null,n===null)throw Ar(s);return sf(n)?s.lanes=32:s.lanes=536870912,null}var S=l.children;return l=l.fallback,u?(Cr(),u=s.mode,S=km({mode:"hidden",children:S},u),l=za(l,u,i,null),S.return=s,l.return=s,S.sibling=l,s.child=S,l=s.child,l.memoizedState=Ag(i),l.childLanes=_g(n,y,i),s.memoizedState=xg,md(null,l)):(kr(s),wg(s,S))}var 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u=(n.flags&128)!==0;if(l=(i&s.childLanes)!==0,l||(Ti(n,s,i,!1),l=(i&s.childLanes)!==0),u){if(l)return iv(n,s,i);s.flags|=128}if(u=s.memoizedState,u!==null&&(u.rendering=null,u.tail=null,u.lastEffect=null),me(Gt,Gt.current),l)break;return null;case 22:return s.lanes=0,Z0(n,s,i,s.pendingProps);case 24:_r(s,Qt,n.memoizedState.cache)}return Bs(n,s,i)}function lv(n,s,i){if(n!==null)if(n.memoizedProps!==s.pendingProps)$t=!0;else{if(!kg(n,i)&&(s.flags&128)===0)return $t=!1,v3(n,s,i);$t=(n.flags&131072)!==0}else $t=!1,rt&&(s.flags&1048576)!==0&&B1(s,Jl,s.index);switch(s.lanes=0,s.tag){case 16:e:{var l=s.pendingProps;if(n=Ba(s.elementType),s.type=n,typeof n=="function")Tp(n)?(l=ja(n,l),s.tag=1,s=ov(null,s,n,l,i)):(s.tag=0,s=yg(null,s,n,l,i));else{if(n!=null){var u=n.$$typeof;if(u===tt){s.tag=11,s=Y0(null,s,n,l,i);break e}else if(u===w){s.tag=14,s=$0(null,s,n,l,i);break e}}throw s=_e(n)||n,Error(o(306,s,""))}}return s;case 0:return yg(n,s,s.type,s.pendingProps,i);case 1:return 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Va(s),l=bn(Qt),n===null?(u=Fp(),u===null&&(u=Et,p=Gp(),u.pooledCache=p,p.refCount++,p!==null&&(u.pooledCacheLanes|=i),u=p),s.memoizedState={parent:l,cache:u},Xp(s),_r(s,Qt,u)):((n.lanes&i)!==0&&(Hp(n,s),ad(s,null,null,i),rd()),u=n.memoizedState,p=s.memoizedState,u.parent!==l?(u={parent:l,cache:l},s.memoizedState=u,s.lanes===0&&(s.memoizedState=s.updateQueue.baseState=u),_r(s,Qt,l)):(l=p.cache,_r(s,Qt,l),l!==u.cache&&Bp(s,[Qt],i,!0))),yn(n,s,s.pendingProps.children,i),s.child;case 29:throw s.pendingProps}throw Error(o(156,s.tag))}function Gs(n){n.flags|=4}function Cg(n,s,i,l,u){if((s=(n.mode&32)!==0)&&(s=!1),s){if(n.flags|=16777216,(u&335544128)===u)if(n.stateNode.complete)n.flags|=8192;else if(zv())n.flags|=8192;else throw Ga=cm,jp}else n.flags&=-16777217}function dv(n,s){if(s.type!=="stylesheet"||(s.state.loading&4)!==0)n.flags&=-16777217;else if(n.flags|=16777216,!Eb(s))if(zv())n.flags|=8192;else throw Ga=cm,jp}function 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50 * @license lucide-react v0.546.0 - ISC
51 *
52 * This source code is licensed under the ISC license.
53 * See the LICENSE file in the root directory of this source tree.
54 */const I6=r=>r.replace(/([a-z0-9])([A-Z])/g,"$1-$2").toLowerCase(),O6=r=>r.replace(/^([A-Z])|[\s-_]+(\w)/g,(e,t,o)=>o?o.toUpperCase():t.toLowerCase()),Kb=r=>{const e=O6(r);return e.charAt(0).toUpperCase()+e.slice(1)},K5=(...r)=>r.filter((e,t,o)=>!!e&&e.trim()!==""&&o.indexOf(e)===t).join(" ").trim(),P6=r=>{for(const e in r)if(e.startsWith("aria-")||e==="role"||e==="title")return!0};/**
55 * @license lucide-react v0.546.0 - ISC
56 *
57 * This source code is licensed under the ISC license.
58 * See the LICENSE file in the root directory of this source tree.
59 */var z6={xmlns:"http://www.w3.org/2000/svg",width:24,height:24,viewBox:"0 0 24 24",fill:"none",stroke:"currentColor",strokeWidth:2,strokeLinecap:"round",strokeLinejoin:"round"};/**
60 * @license lucide-react v0.546.0 - ISC
61 *
62 * This source code is licensed under the ISC license.
63 * See the LICENSE file in the root directory of this source tree.
64 */const L6=Re.forwardRef(({color:r="currentColor",size:e=24,strokeWidth:t=2,absoluteStrokeWidth:o,className:a="",children:d,iconNode:c,...g},h)=>Re.createElement("svg",{ref:h,...z6,width:e,height:e,stroke:r,strokeWidth:o?Number(t)*24/Number(e):t,className:K5("lucide",a),...!d&&!P6(g)&&{"aria-hidden":"true"},...g},[...c.map(([v,_])=>Re.createElement(v,_)),...Array.isArray(d)?d:[d]]));/**
65 * @license lucide-react v0.546.0 - ISC
66 *
67 * This source code is licensed under the ISC license.
68 * See the LICENSE file in the root directory of this source tree.
69 */const pt=(r,e)=>{const t=Re.forwardRef(({className:o,...a},d)=>Re.createElement(L6,{ref:d,iconNode:e,className:K5(`lucide-${I6(Kb(r))}`,`lucide-${r}`,o),...a}));return t.displayName=Kb(r),t};/**
70 * @license lucide-react v0.546.0 - ISC
71 *
72 * This source code is licensed under the ISC license.
73 * See the LICENSE file in the root directory of this source tree.
74 */const N6=[["path",{d:"M22 12h-2.48a2 2 0 0 0-1.93 1.46l-2.35 8.36a.25.25 0 0 1-.48 0L9.24 2.18a.25.25 0 0 0-.48 0l

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75 * @license lucide-react v0.546.0 - ISC
76 *
77 * This source code is licensed under the ISC license.
78 * See the LICENSE file in the root directory of this source tree.
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80 * @license lucide-react v0.546.0 - ISC
81 *
82 * This source code is licensed under the ISC license.
83 * See the LICENSE file in the root directory of this source tree.
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85 * @license lucide-react v0.546.0 - ISC
86 *
87 * This source code is licensed under the ISC license.
88 * See the LICENSE file in the root directory of this source tree.
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90 * @license lucide-react v0.546.0 - ISC
91 *
92 * This source code is licensed under the ISC license.
93 * See the LICENSE file in the root directory of this source tree.
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95 * @license lucide-react v0.546.0 - ISC
96 *
97 * This source code is licensed under the ISC license.
98 * See the LICENSE file in the root directory of this source tree.
99 */const H6=[["path",{d:"m9 18 6-6-6-6",key:"mthhwq"}]],Q5=pt("chevron-right",H6);/**
100 * @license lucide-react v0.546.0 - ISC
101 *
102 * This source code is licensed under the ISC license.
103 * See the LICENSE file in the root directory of this source tree.
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105 * @license lucide-react v0.546.0 - ISC
106 *
107 * This source code is licensed under the ISC license.
108 * See the LICENSE file in the root directory of this source tree.
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110 * @license lucide-react v0.546.0 - ISC
111 *
112 * This source code is licensed under the ISC license.
113 * See the LICENSE file in the root directory of this source tree.
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115 * @license lucide-react v0.546.0 - ISC
116 *
117 * This source code is licensed under the ISC license.
118 * See the LICENSE file in the root directory of this source tree.
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120 * @license lucide-react v0.546.0 - ISC
121 *
122 * This source code is licensed under the ISC license.
123 * See the LICENSE file in the root directory of this source tree.
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125 * @license lucide-react v0.546.0 - ISC
126 *
127 * This source code is licensed under the ISC license.
128 * See the LICENSE file in the root directory of this source tree.
129 */const ex=[["ellipse",{cx:"12",cy:"5",rx:"9",ry:"3",key:"msslwz"}],["path",{d:"M3 5V19A9 3 0 0 0 21 19V5",key:"1wlel7"}],["path",{d:"M3 12A9 3 0 0 0 21 12",key:"mv7ke4"}]],tx=pt("database",ex);/**
130 * @license lucide-react v0.546.0 - ISC
131 *
132 * This source code is licensed under the ISC license.
133 * See the LICENSE file in the root directory of this source tree.
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134y:"1slcih"}]],ox=pt("flame",nx);/**
135 * @license lucide-react v0.546.0 - ISC
136 *
137 * This source code is licensed under the ISC license.
138 * See the LICENSE file in the root directory of this source tree.
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140 * @license lucide-react v0.546.0 - ISC
141 *
142 * This source code is licensed under the ISC license.
143 * See the LICENSE file in the root directory of this source tree.
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145 * @license lucide-react v0.546.0 - ISC
146 *
147 * This source code is licensed under the ISC license.
148 * See the LICENSE file in the root directory of this source tree.
149 */const lx=[["path",{d:"M18 5a2 2 0 0 1 2 2v8.526a2 2 0 0 0 .212.897l1.068 2.127a1 1 0 0 1-.9 1.45H3.62a1 1 0 0 1-.9-1.45l1.068-2.127A2 2 0 0 0 4 15.526V7a2 2 0 0 1 2-2z",key:"1pdavp"}],["path",{d:"M20.054 15.987H3.946",key:"14rxg9"}]],dx=pt("laptop",lx);/**
150 * @license lucide-react v0.546.0 - ISC
151 *
152 * This source code is licensed under the ISC license.
153 * See the LICENSE file in the root directory of this source tree.
154 */const cx=[["path",{d:"M4 5h16",key:"1tepv9"}],["path",{d:"M4 12h16",key:"1lakjw"}],["path",{d:"M4 19h16",key:"1djgab"}]],mx=pt("menu",cx);/**
155 * @license lucide-react v0.546.0 - ISC
156 *
157 * This source code is licensed under the ISC license.
158 * See the LICENSE file in the root directory of this source tree.
159 */const ux=[["path",{d:"M22 17a2 2 0 0 1-2 2H6.828a2 2 0 0 0-1.414.586l-2.202 2.202A.71.71 0 0 1 2 21.286V5a2 2 0 0 1 2-2h16a2 2 0 0 1 2 2z",key:"18887p"}]],px=pt("message-square",ux);/**
160 * @license lucide-react v0.546.0 - ISC
161 *
162 * This source code is licensed under the ISC license.
163 * See the LICENSE file in the root directory of this source tree.
164 */const gx=[["rect",{width:"20",height:"14",x:"2",y:"3",rx:"2",key:"48i651"}],["line",{x1:"8",x2:"16",y1:"21",y2:"21",key:"1svkeh"}],["line",{x1:"12",x2:"12",y1:"17",y2:"21",key:"vw1qmm"}]],fx=pt("monitor",gx);/**
165 * @license lucide-react v0.546.0 - ISC
166 *
167 * This source code is licensed under the ISC license.
168 * See the LICENSE file in the root directory of this source tree.
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170 * @license lucide-react v0.546.0 - ISC
171 *
172 * This source code is licensed under the ISC license.
173 * See the LICENSE file in the root directory of this source tree.
174 */const bx=[["path",{d:"M5 12h14",key:"1ays0h"}],["path",{d:"M12 5v14",key:"s699le"}]],yx=pt("plus",bx);/**
175 * @license lucide-react v0.546.0 - ISC
176 *
177 * This source code is licensed under the ISC license.
178 * See the LICENSE file in the root directory of this source tree.
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180 * @license lucide-react v0.546.0 - ISC
181 *
182 * This source code is licensed under the ISC license.
183 * See the LICENSE file in the root directory of this source tree.
184 */const _x=[["path",{d:"m21 21-4.34-4.34",key:"14j7rj"}],["circle",{cx:"11",cy:"11",r:"8",key:"4ej97u"}]],wx=pt("search",_x);/**
185 * @license lucide-react v0.546.0 - ISC
186 *
187 * This source code is licensed under the ISC license.
188 * See the LICENSE file in the root directory of this source tree.
189 */const Ex=[["path",{d:"M14.536 21.686a.5.5 0 0 0 .937-.024l6.5-19a.496.496 0 0 0-.635-.635l-19 6.5a.5.5 0 0 0-.024.937l7.93 3.18a2 2 0 0 1 1.112 1.11z",key:"1ffxy3"}],["path",{d:"m21.854 2.147-10.94 10.939",key:"12cjpa"}]],Sx=pt("send",Ex);/**
190 * @license lucide-react v0.546.0 - ISC
191 *
192 * This source code is licensed under the ISC license.
193 * See the LICENSE file in the root directory of this source tree.
194 */const kx=[["path",{d:"M12 19h8",key:"baeox8"}],["path",{d:"m4 17 6-6-6-6",key:"1yngyt"}]],Vf=pt("terminal",kx);/**
195 * @license lucide-react v0.546.0 - ISC
196 *
197 * This source code is licensed under the ISC license.
198 * See the LICENSE file in the root directory of this source tree.
199 */const Cx=[["path",{d:"M7 10v12",key:"1qc93n"}],["path",{d:"M15 5.88 14 10h5.83a2 2 0 0 1 1.92 2.56l-2.33 8A2 2 0 0 1 17.5 22H4a2 2 0 0 1-2-2v-8a2 2 0 0 1 2-2h2.76a2 2 0 0 0 1.79-1.11L12 2a3.13 3.13 0 0 1 3 3.88Z",key:"emmmcr"}]],Rx=pt("thumbs-up",Cx);/**
200 * @license lucide-react v0.546.0 - ISC
201 *
202 * This source code is licensed under the ISC license.
203 * See the LICENSE file in the root directory of this source tree.
204 */const Dx=[["path",{d:"M10 11v6",key:"nco0om"}],["path",{d:"M14 11v6",key:"outv1u"}],["path",{d:"M19 6v14a2 2 0 0 1-2 2H7a2 2 0 0 1-2-2V6",key:"miytrc"}],["path",{d:"M3 6h18",key:"d0wm0j"}],["path",{d:"M8 6V4a2 2 0 0 1 2-2h4a2 2 0 0 1 2 2v2",key:"e791ji"}]],Mx=pt("trash-2",Dx);/**
205 * @license lucide-react v0.546.0 - ISC
206 *
207 * This source code is licensed under the ISC license.
208 * See the LICENSE file in the root directory of this source tree.
209 */const Tx=[["path",{d:"m21.73 18-8-14a2 2 0 0 0-3.48 0l-8 14A2 2 0 0 0 4 21h16a2 2 0 0 0 1.73-3",key:"wmoenq"}],["path",{d:"M12 9v4",key:"juzpu7"}],["path",{d:"M12 17h.01",key:"p32p05"}]],Y5=pt("triangle-alert",Tx);/**
210 * @license lucide-react v0.546.0 - ISC
211 *
212 * This source code is licensed under the ISC license.
213 * See the LICENSE file in the root directory of this source tree.
214 */const Ix=[["path",{d:"M18 6 6 18",key:"1bl5f8"}

214],["path",{d:"m6 6 12 12",key:"d8bk6v"}]],Ox=pt("x",Ix);/**
215 * @license lucide-react v0.546.0 - ISC
216 *
217 * This source code is licensed under the ISC license.
218 * See the LICENSE file in the root directory of this source tree.
219 */const Px=[["path",{d:"M4 14a1 1 0 0 1-.78-1.63l9.9-10.2a.5.5 0 0 1 .86.46l-1.92 6.02A1 1 0 0 0 13 10h7a1 1 0 0 1 .78 1.63l-9.9 10.2a.5.5 0 0 1-.86-.46l1.92-6.02A1 1 0 0 0 11 14z",key:"1xq2db"}]],Jb=pt("zap",Px),Zb={"pt-BR":{amihart:{id:"amihart",title:"31. MÉTODO AMIHART — INFERÊNCIA RX580 EM POLARIS/DEBIAN",desc:"Nota de Crédito: Seção documental em homenagem à prova de conceito realizada por Amihart, pioneira na validação de Vulkan sob Debian GCN4/Polaris.",meta:{version:"1.0.0",type:"estudo_caso",category:"estudo-caso",subcategory:"metodo-historico",difficulty:"advanced",estimated_time_min:30,tags:["metodo-amihart","debian-polaris","vulkan-diagnostics"],last_reviewed:"2026-07-01",relationships:{prerequisites:["comunidade"],previous:"comunidade",next:"dadhacks",related:["comunidade","linux_nativo"]}},html:`<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.25rem">Em janeiro de 2025, o desenvolvedor <strong style="color:#fff">Amihart</strong> publicou um estudo técnico demonstrando que, apesar das limitações oficiais impostas pela AMD (fim do suporte oficial do ROCm para arquiteturas Polaris/GCN4), o hardware RX 580 ainda possui pleno potencial de aceleração de IA.</p>
220  
221  <div style="display:grid;grid-template-columns:1fr 1fr;gap:.85rem;margin-bottom:1.25rem">
222    <div class="card" style="border-left:3px solid var(--r);padding:1rem;border-radius:4px;background:rgba(255,255,255,0.01);border:1px solid var(--b);border-left:3px solid var(--r)">
223      <p style="font-size:.88rem;font-weight:600;color:#fff;margin-bottom:.5rem">Abordagem AIVisionsLab (Windows Nativo)</p>
224      <ul style="padding-left:1.2rem;font-size:0.8rem;color:#94a3b8;line-height:1.8">
225        <li>Execução no sistema hospedeiro (Windows).</li>
226        <li>Foco em produção diária de alta estabilidade.</li>
227        <li>Tuning de orquestração Vulkan nativo.</li>
228        <li>Segmentação VRAM/CPU híbrida no WSL.</li>
229      </ul>
230    </div>
231    <div class="card" style="border-left:3px solid #22c55e;padding:1rem;border-radius:4px;background:rgba(255,255,255,0.01);border:1px solid var(--b);border-left:3px solid #22c55e">
232      <p style="font-size:.88rem;font-weight:600;color:#fff;margin-bottom:.5rem">O Método Amihart (Linux/Debian)</p>
233      <ul style="padding-left:1.2rem;font-size:0.8rem;color:#94a3b8;line-height:1.8">
234        <li>Execução em ecossistema Linux/Debian.</li>
235        <li>Isolamento robusto via containers Docker.</li>
236        <li>Compilação estrita com flags do Vulkan API.</li>
237        <li>Sandbox ROCm adaptado virtualizado.</li>
238      </ul>
239    </div>
240  </div>
241
242  <h4 style="color:#fff;font-family:'Syne',sans-serif;font-size:11px;letter-spacing:1px;margin-bottom:.75rem;text-transform:uppercase">A. Setup de LLM — Vulkan no Debian</h4>
243  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.8">O llama.cpp é compilado com Vulkan API puro para contornar a ausência do driver ROCm:</p>
244  <div class="code" style="margin-bottom:1.25rem"><pre style="background:#07080a;border:1px solid var(--b);padding:1rem;border-radius:4px;margin:0"><code style="color:#e2e8f0;font-size:11px"># Passo 1: Instalar drivers Vulkan e pré-requisitos de compilação
245sudo apt install vulkan-tools libtcmalloc-minimal4 libcurl4-openssl-dev glslc cmake make git pkg-config libvulkan-dev
246
247# Passo 2: Clonar e compilar o llama.cpp com Vulkan habilitado
248cd ~
249git clone https://github.com/ggerganov/llama.cpp
250cd llama.cpp && mkdir build && cd build
251cmake .. -DGGML_VULKAN=on -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON
252
253# Passo 3: Adicionar binários ao PATH de execução
254echo 'export PATH=$PATH:'$(realpath bin) >> ~/.bashrc
255source ~/.bashrc
256
257# Passo 4: Executar DeepSeek R1 8B (Aceleração Vulkan0)
258llama-cli -m deepseek-r1:8B --device Vulkan0 -ngl 100</code></pre></div>
259
260  <p style="font-size:.85rem;color:#94a3b8;line-height:1.9;margin-bottom:1rem"><strong>Métrica de Desempenho (Amihart):</strong> No Celeron G6900 operando sem GPU, a resposta é de 5,45 tokens/s. Utilizando aceleração Vulkan pura na RX 580 com 100 camadas offload, a performance sobe para <strong style="color:var(--r)">24,56 tokens/s</strong>, tornando o modelo viável e ágil.</p>
261
262  <h4 style="color:#fff;font-family:'Syne',sans-serif;font-size:11px;letter-spacing:1px;margin:1.5rem 0 .75rem 0;text-transform:uppercase">B. Setup de Stable Diffusion — Sandbox Docker</h4>
263  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.8">Como geradores de imagem exigem dependências ROCm, Amihart utiliza containers virtuais isolados para rodar a Web

263UI de forma limpa no Debian:</p>
264  <div class="code" style="margin-bottom:1.25rem"><pre style="background:#07080a;border:1px solid var(--b);padding:1rem;border-radius:4px;margin:0"><code style="color:#e2e8f0;font-size:11px"># Passo 1: Instalação do motor Docker e chaves estritas
265sudo apt update && sudo apt install -y ca-certificates curl
266install -m 0755 -d /etc/apt/keyrings
267curl -fsSL https://download.docker.com/linux/debian/gpg -o /etc/apt/keyrings/docker.asc
268chmod a+r /etc/apt/keyrings/docker.asc
269echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/debian $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | tee /etc/apt/sources.list.d/docker.list > /dev/null
270sudo apt update && sudo apt install -y docker-ce docker-ce-cli containerd.io
271
272# Passo 2: Clonagem da WebUI e execução sob isolamento ROCm gfx803
273cd ~
274git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui
275cd ~/stable-diffusion-webui
276
277sudo docker run -it \\
278  --network=host --device=/dev/kfd --device=/dev/dri \\
279  --ipc=host --shm-size 16G --group-add video \\
280  --cap-add=SYS_PTRACE \\
281  --rm -v $(pwd)/cache:/root/.cache \\
282  -v $(pwd)/data:/stable-diffusion-webui/data \\
283  woodrex/sd-webui-for-gfx803:latest</code></pre></div>
284
285  <p style="font-size:.85rem;color:#94a3b8;line-height:1.9;margin-bottom:1rem"><strong>Otimização Crítica do Cache Amihart:</strong> Para evitar re-loadings de dependências todas as vezes que o container iniciar:</p>
286  <div class="code" style="margin-bottom:1.5rem"><pre style="background:#07080a;border:1px solid var(--b);padding:1rem;border-radius:4px;margin:0"><code style="color:#e2e8f0;font-size:11px"># Obter o CONTAINER ID ativo
287sudo docker ps -l
288# Cometer o estado para uma nova imagem persistente
289sudo docker commit [CONTAINER_ID]
290# Ver o ID da imagem persistida e substituir a chamada de docker run por ele
291sudo docker images</code></pre></div>
292
293  <div class="card" style="border:1px dashed rgba(255,255,255,0.08);background:rgba(255,255,255,0.01);padding:1rem;border-radius:4px;margin-top:1.25rem">
294    <strong style="color:var(--r)">Métricas e Referências Técnicas:</strong> Roteamento baseado na publicação original de <a href="https://amihart.medium.com/inference-with-an-rx580-13e9c1055472" target="_blank" rel="noopener noreferrer" style="color:var(--r);text-decoration:underline;font-weight:bold">Amihart</a> (Medium, Janeiro de 2025). O conhecimento é aberto e útil à comunidade.
295  </div>`},"animatediff-video":{id:"animatediff-video",title:"19. ANIMATEDIFF E INTERPOLAÇÃO DE QUADROS (VÍDEO)",desc:"Injetando consistência temporal no ComfyUI sobre hardware legado para estruturar loops fluidos coprocessados no Intel Xeon.",meta:{version:"1.0.0",type:"documento",category:"software",subcategory:"comfyui",difficulty:"advanced",estimated_time_min:30,tags:["animatediff","geracao-video","motion-modules"],last_reviewed:"2026-07-01",relationships:{prerequisites:["comfyui-modelos"],previous:"comfyui-modelos",next:"stack",related:["comfyui-flux","sdxl_vulkan"]}},html:`<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.25rem">
296  O <strong>AnimateDiff</strong> é um framework de modelagem temporal projetado especificamente para converter modelos de difusão estática (como Stable Diffusion 1.5) em geradores de vídeo consistentes. Ele funciona injetando módulos de atenção temporal sobrepostos no ComfyUI/WSL2. Essa técnica permite calcular transições de quadros adjacentes de forma matematicamente coordenada, gerando loops fluidos e eliminando os efeitos estáticos caóticos de ruído cintilante.
297</p>
298
299<!-- Stat cards of render cost -->
300<div style="display:grid;grid-template-columns:1fr 1fr;gap:1.25rem;margin-bottom:1.5rem">
301  <div class="stat-card" style="display:flex;align-items:center;gap:1rem;background:#14171a;border:1px solid var(--b);padding:18px;border-radius:4px">
302    <div style="font-size:1.8rem">🎬</div>
303    <div>
304      <div class="stat-lbl" style="font-size:9px;color:#475569;text-transform:uppercase;letter-spacing:1.5px;margin-bottom:2px">Pipeline de Vídeo</div>
305      <div class="stat-val" style="font-family:'Syne',sans-serif;font-weight:800;font-size:14px;color:#fff">AnimateDiff (WSL2 Pure CPU)</div>
306      <div style="font-size:.65rem;color:#64748b;margin-top:2px">24 Threads Intel Xeon E5</div>
307    </div>
308  </div>
309  <div class="stat-card" style="display:flex;align-items:center;gap:1rem;background:#14171a;border:1px solid var(--b);padding:18px;border-radius:4px">
310    <div style="font-size:1.8rem">⏳</div>
311    <div>
312      <div class="stat-lbl" style="font-size:9px;color:#475569;text-transform:uppercase;letter-spacing:1.5px;margin-bottom:2px">Métrica de Processamento</div>
313      <div class="stat-val" style="font-family:'Syne',sans-serif;font-weight:800;font-size:14px;color:#34d399">~141s por Segundo de Frame</div>
314      <div style="font-size:.65rem;color:#64748b;margin-top:2px">Renderização temporal interpolada</div>
315    </div>
316  </div>
317</div>
318
319<!-- Visual Exhibition Player -->
320<div style="background:#0F1115;border:1px solid rgba(255,255,255,0.04);border-radius:6px;padding:1.5rem;margin-bottom:1.5rem">
321  <h4 style="font-size:.85rem;color:#fff;text-transform:uppercase;letter-spacing:1px;margin-bottom:1rem;display:flex;align-items:center;gap:.5rem;margin-top:0">
322    <span>🎥</span> Vitrine Interativa de Geração (Breathe-Loop de Vídeo por Cache Local)
323  </h4>
324  <p style="font-size:.8rem;color:#64748b;line-height:1.6;margin-bottom:1.25rem">
325    Abaixo estão representados os dois outputs renderizados pela sua pilha de IA local. Passe o mouse para ativar a sensação de vídeo interpolada, ou clique no botão simulador para iniciar o render sintético do Xeon:
326  </p>
327  
328  <div style="display:grid;grid-template-columns:1fr 1fr;gap:1.5rem;margin-bottom:1.25rem">

329    <!-- Player 1: Cyberpunk -->
330    <div class="video-preview-box" style="position:relative;background:#050608;border:1px solid rgba(255,255,255,0.04);border-radius:4px;overflow:hidden;aspect-ratio:1">
331      <img src="/images/cyberpunk_alley_0_1779586391528.png" alt="Cyberpunk Neon Alley" referrerPolicy="no-referrer" class="animatediff-img-1" style="width:100%;height:100%;object-fit:cover;transition:transform 8s ease-in-out, filter 2s ease;filter:brightness(0.9)" />
332      <div style="position:absolute;top:.5rem;left:.5rem;background:rgba(0,0,0,0.85);font-family:monospace;font-size:9px;color:#fff;padding:.2rem .4rem;border-radius:2px;display:flex;align-items:center;gap:.3rem">
333        <span class="pulse-red" style="width:5px;height:5px;border-radius:50%;background:#ef4444;display:inline-block"></span>
334        <span>CYBER_STREET_00.GIF</span>
335      </div>
336      <div style="position:absolute;bottom:0;left:0;right:0;background:linear-gradient(transparent, rgba(0,0,0,0.95));padding:.75rem .5rem .5rem .75rem;display:flex;justify-content:space-between;align-items:center">
337        <span style="font-family:monospace;font-size:9px;color:#64748b">Stable Diffusion 1.5</span>
338        <span style="font-family:monospace;font-size:9px;color:#ef4444;font-weight:bold">VULKAN_OK</span>
339      </div>
340    </div>
341
342    <!-- Player 2: Cosmic Glass -->
343    <div class="video-preview-box" style="position:relative;background:#050608;border:1px solid rgba(255,255,255,0.04);border-radius:4px;overflow:hidden;aspect-ratio:1">
344      <img src="/images/cosmic_glass_0_1779586410888.png" alt="Cosmic Glass Flask" referrerPolicy="no-referrer" class="animatediff-img-2" style="width:100%;height:100%;object-fit:cover;transition:transform 6s ease-in-out, filter 3s ease;filter:brightness(0.85)" />
345      <div style="position:absolute;top:.5rem;left:.5rem;background:rgba(0,0,0,0.85);font-family:monospace;font-size:9px;color:#fff;padding:.2rem .4rem;border-radius:2px;display:flex;align-items:center;gap:.3rem">
346        <span class="pulse-red" style="width:5px;height:5px;border-radius:50%;background:#ef4444;display:inline-block"></span>
347        <span>ALCHEM_COSMOS_01.GIF</span>
348      </div>
349      <div style="position:absolute;bottom:0;left:0;right:0;background:linear-gradient(transparent, rgba(0,0,0,0.95));padding:.5rem .75rem;display:flex;justify-content:space-between;align-items:center">
350        <span style="font-family:monospace;font-size:9px;color:#64748b">Pure Xeon Model</span>
351        <span style="font-family:monospace;font-size:9px;color:#ef4444;font-weight:bold">THREADED_OK</span>
352      </div>
353    </div>
354  </div>
355
356  <div style="text-align:center;margin-top:.5rem">
357    <button onclick="toggleAnimateDiffSim(this)" style="background:rgba(239, 68, 68, 0.1);border:1px solid rgba(239, 68, 68, 0.4);border-radius:3px;color:#fca5a5;font-family:monospace;padding:.5rem 1rem;font-size:.72rem;cursor:pointer;transition:all 0.2s;text-transform:uppercase">
358      ▶ Ativar Simulação de Movimento
359    </button>
360  </div>
361  
362  <div id="sim-log-box" style="background:#07080a;border:1px solid rgba(255,255,255,0.01);border-radius:3px;padding:0.75rem;margin-top:1rem;font-family:monospace;font-size:10px;color:#475569;line-height:1.5;display:none;max-height:120px;overflow-y:auto;text-align:left">
363  </div>
364</div>
365
366<style>
367  .video-preview-box:hover .animatediff-img-1 {
368    transform: scale(1.1) rotate(0.5deg);
369    filter: brightness(1.1) saturate(1.15) !important;
370  }
371  .video-preview-box:hover .animatediff-img-2 {
372    transform: scale(1.08) rotate(-0.5deg);
373    filter: brightness(1.05) saturate(1.2) !important;
374  }
375  .animate-active-1 {
376    animation: panCyberpunk 12s infinite alternate ease-in-out;
377    filter: brightness(1.1) saturate(1.15) !important;
378  }
379  .animate-active-2 {
380    animation: panGlass 10s infinite alternate ease-in-out;
381    filter: brightness(1.05) saturate(1.2) !important;
382  }
383  @keyframes panCyberpunk {
384    0% { transform: scale(1); filter: hue-rotate(0deg); }
385    50% { transform: scale(1.12) translate(5px, -3px); filter: hue-rotate(15deg); }
386    100% { transform: scale(1.02) translate(-5px, 2px); filter: hue-rotate(-10deg); }
387  }
388  @keyframes panGlass {
389    0% { transform: scale(1.02) rotate(0deg); }
390    50% { transform: scale(1.15) translate(-2px, 4px) rotate(1deg); filter: hue-rotate(5deg); }
391    100% { transform: scale(1.05) translate(4px, -2px) rotate(-0.5deg); filter: hue-rotate(-5deg); }
392  }
393  @keyframes heartbeat {
394    0%, 100% { opacity: 0.4; }
395    50% { opacity: 1; }
396  }
397  .pulse-red {
398    animation: heartbeat 1s infinite;
399  }
400</style>`},arquivos:{id:"arquivos",title:"35. TAXONOMIA E ESTRUTURA DE ARQUIVOS NVMe",desc:"Árvore de diretórios unificada para rastreamento de binários compilados e modelos compartilhados.",meta:{version:"1.0.0",type:"documento",category:"comunidade",subcategory:"armazenamento",difficulty:"beginner",estimated_time_min:10,tags:["nvme-struct","model-directories","storage-mapping"],last_reviewed:"2026-07-01",relationships:{prerequisites:["proximos"],previous:"proximos",next:"meta",related:["comfyui-modelos","meta"]}},html:`<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:.75rem">Organização estrutural rigorosa dos caminhos físicos mantidos dentro da partição de alta velocidade do SSD:</p>
401  <pre style="font-family:'JetBrains Mono',monospace;font-size:11px;color:#e2e8f0;background:rgba(0,0,0,.3);padding:.9rem;border-radius:4px;border:1px solid var(--b)">
402E:\\
403├── llama.cpp\\
404│   └── build\\bin\\Release\\

405│       ├── llama-server.exe     <span style="color:#64748b"># Servidor de Linguagem Local</span>
406│       └── llama-cli.exe        <span style="color:#64748b"># Utilitario de Diagnostico</span>
407├── stable-diffusion.cpp\\
408│   └── build\\bin\\Release\\
409│       ├── sd-server.exe        <span style="color:#64748b"># Servidor de Imagem Vulkan</span>
410│       └── sd-cli.exe           <span style="color:#64748b"># Conversor de Modelos GGUF</span>
411└── models\\
412    ├── mistral-7b-instruct.Q4_K_M.gguf
413    └── dreamshaper8.gguf        <span style="color:#64748b"># Checkpoint SD 1.5 Quantizado</span></pre>`},audio_rvc:{id:"audio_rvc",title:"38. VOICE CONVERSION LOCAL — APPLIO RVC EM AMD RX 580 (Fases Iniciais)",desc:"Pipeline de clonagem de voz no Windows sem CUDA NVIDIA - Configuração do ambiente e primeiras fases.",meta:{version:"1.0.0",type:"documento",category:"software",subcategory:"audio",difficulty:"advanced",estimated_time_min:35,tags:["applio","rvc-voice-clone","sintese-audio"],last_reviewed:"2026-07-01",relationships:{prerequisites:["linhatempo"],previous:"linhatempo",next:"audio_whisper",related:["audio_whisper","audio_srt_ptbr"]},part:1},html:`<blockquote style="font-size:.82rem;color:#94a3b8;font-style:italic;line-height:1.7;border-left:3px solid var(--r);padding-left:.9rem;margin-bottom:1.25rem">
414  Documentação honesta de uma instalação real, incluindo todas as tentativas, erros e soluções.<br />
415  Conduzida em duas sessões: ChatGPT (fases iniciais) + Claude (fases finais).
416</blockquote>
417
418<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:800;margin-top:1.5rem;margin-bottom:.9rem">CONTEXTO DO PROJETO</h3>
419<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.25rem">O objetivo era montar um pipeline de produção audiovisual para canal do YouTube estilo dark/documentário:</p>
420<div class="code" style="margin-bottom:1.25rem">
421  <pre><code>Texto → Balabolka + Antônio Neural (TTS) → WAV → Applio/RVC → Yuri (voz clonada)</code></pre>
422</div>
423<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.25rem">A ideia central: separar <strong>atuação</strong> de <strong>identidade vocal</strong>.</p>
424<ul style="list-style-type:disc;padding-left:1.5rem;margin-bottom:1.25rem;color:#94a3b8;line-height:1.8">
425  <li><strong style="color:#fff">Antônio Neural</strong> faz a interpretação, prosódia e emoção (voz Microsoft Neural)</li>
426  <li><strong style="color:#fff">Yuri</strong> (modelo RVC treinado) aplica a identidade vocal por cima</li>
427</ul>
428<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.5rem">Isso produz resultado muito mais natural que TTS generativo puro.</p>
429
430<div class="tbl" style="margin-bottom:1.5rem">
431  <table>
432    <thead>
433      <tr>
434        <th>Aspecto</th>
435        <th>XTTS/TTS puro</th>
436        <th>Antônio → Yuri (RVC)</th>
437      </tr>
438    </thead>
439    <tbody>
440      <tr>
441        <td>Prosódia</td>
442        <td>Artificial</td>
443        <td>Humana (ator real)</td>
444      </tr>
445      <tr>
446        <td>Termos técnicos</td>
447        <td>Tropeça</td>
448        <td>Lê corretamente</td>
449      </tr>
450      <tr>
451        <td>Textos longos</td>
452        <td>Degrada</td>
453        <td>Estável</td>
454      </tr>
455      <tr>
456        <td>Identidade vocal</td>
457        <td>Genérica</td>
458        <td>Clonada</td>
459      </tr>
460      <tr>
461        <td>Naturalidade</td>
462        <td>60-70%</td>
463        <td>80-95%</td>
464      </tr>
465    </tbody>
466  </table>
467</div>
468
469<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:800;margin-top:1.5rem;margin-bottom:.9rem">HARDWARE UTILIZADO</h3>
470<div class="tbl" style="margin-bottom:1.5rem">
471  <table>
472    <thead>
473      <tr>
474        <th>Item</th>
475        <th>Especificação</th>
476      </tr>
477    </thead>
478    <tbody>
479      <tr>
480        <td>CPU</td>
481        <td>Intel Xeon E5-2690 v3 (24 núcleos)</td>
482      </tr>
483      <tr>
484        <td>GPU</td>
485        <td>AMD Radeon RX 580 2048SP (8GB VRAM)</td>
486      </tr>
487      <tr>
488        <td>RAM</td>
489        <td>31.8 GB DDR4 REG ECC</td>
490      </tr>
491      <tr>
492        <td>OS</td>
493        <td>Windows 11</td>
494      </tr>
495      <tr>
496        <td>Storage</td>
497        <td>SSD NVMe (E:)</td>
498      </tr>
499      <tr>
500        <td>Python</td>
501        <td>3.11.9</td>
502      </tr>
503    </tbody>
504  </table>
505</div>
506
507<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:800;margin-top:1.5rem;margin-bottom:.9rem">FASE 0 — TENTATIVA COM DATASET DO ANTÔNIO (fracasso documentado)</h3>
508<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.25rem">A ideia original era treinar o modelo RVC com horas de áudio do Antônio Neural. Foi criado um script <code>split.py</code> com Whisper para segmentar automaticamente o áudio em clips de 6-12 segundos.</p>
509<p style="font-size:.88rem;color:#ef4444;font-weight:700;margin-bottom:0.5rem"><strong>O problema: estouro de memória</strong></p>
510<div class="code" style="margin-bottom:1.25rem">
511  <pre><code>MemoryError
512concurrent.futures.process.BrokenProcessPool</code></pre>
513</div>
514<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.25rem">O pré-processamento de dataset grande (~2h34 de áudio) estourou RAM e VRAM simultaneamente. Mesmo quando terminava, o volume de 1000+ clips causava instabilidade no pipeline do Applio.</p>
515<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.25rem"><strong>Decisão:</strong> abandonar o dataset do Antônio e usar diretamente <strong>9 minutos de TTS do ElevenLabs</strong> com a voz do Yuri. Menor, mais limpo, mais controlado.</p>
516
517<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:800;margin-top:1.5rem;margin-bottom:.9rem">FASE 1 — INSTALAÇÃO DO AMBIENTE BASE</h3>
518<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:0.75rem"><strong>Estrutura de pastas:</strong></p>
519<div class="code" style="margin-bottom:1.25rem">
520  <pre><code>E:\\tts-audio_pra_youtube\\
521├── Applio\\
522├── voices\\
523│   └── YURI-TTS.wav  (9 minutos, ElevenLabs, voz Yuri)
524├── output\\
525└── scripts\\</code></pre>
526</div>
527
528<div class="err" style="margin-bottom:1.25rem">

529  <div class="err-t">⚠️ Problema: Python 3.10 incompatível</div>
530  <p>O Applio moderno exige Python 3.11+. O numpy 2.4.4 requerido não existe para 3.10.</p>
531</div>
532
533<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:0.75rem"><strong>Solução:</strong> instalar Python 3.11 em paralelo sem remover o 3.10:</p>
534<div class="code" style="margin-bottom:1.25rem">
535  <span class="code-lang">powershell</span>
536  <pre><code>py -0
537# -V:3.11  Python 3.11 (64-bit)   ← usar esta
538# -V:3.10  Python 3.10 (64-bit)   ← manter para outros projetos
539
540py -3.11 -m venv venv
541venv\\Scripts\\activate
542python --version
543# Python 3.11.9  ✅</code></pre>
544</div>
545
546<div class="err" style="margin-bottom:1.25rem">
547  <div class="err-t">⚠️ Problema: requirements.txt com CUDA forçado</div>
548  <div class="code" style="margin-top:0.5rem;margin-bottom:0.5rem">
549    <pre><code># O Applio traz esta linha que quebra no AMD Windows:
550torch==2.7.1+cu128; sys_platform == 'linux' or sys_platform == 'win32'</code></pre>
551  </div>
552  <p>O sufixo <code>+cu128</code> é CUDA 12.8 exclusivo NVIDIA. No Windows (<code>win32</code>), essa condição sempre é verdadeira.</p>
553</div>
554
555<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:0.75rem"><strong>Solução:</strong> editar o <code>requirements.txt</code>:</p>
556<div class="code" style="margin-bottom:1.25rem">
557  <pre><code># REMOVER:
558torch==2.7.1+cu128; sys_platform == 'linux' or sys_platform == 'win32'
559torchaudio==2.7.1+cu128; sys_platform == 'linux' or sys_platform == 'win32'
560torchvision==0.22.1+cu128; sys_platform == 'linux' or sys_platform == 'win32'
561
562# SUBSTITUIR POR:
563torch==2.7.1; sys_platform == 'win32'
564torchaudio==2.7.1; sys_platform == 'win32'
565torchvision==0.22.1; sys_platform == 'win32'</code></pre>
566</div>
567
568<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:800;margin-top:1.5rem;margin-bottom:.9rem">FASE 2 — TENTATIVA DIRECTML (fracasso documentado)</h3>
569<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.25rem">Com o Applio rodando em CPU (~10 min por época), tentou-se acelerar com DirectML.</p>
570<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:0.75rem"><strong>Instalação e teste:</strong></p>
571<div class="code" style="margin-bottom:1.25rem">
572  <span class="code-lang">powershell</span>
573  <pre><code>pip install torch==2.4.1 torchaudio==2.4.1 torchvision==0.19.1
574pip install torch-directml
575
576python -c "import torch_directml; dml = torch_directml.device(); print(dml)"
577# privateuseone:0  ✅  — RX 580 reconhecida</code></pre>
578</div>
579<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:0.75rem"><strong>Resultado real:</strong></p>
580<ul style="list-style-type:disc;padding-left:1.5rem;margin-bottom:1.25rem;color:#94a3b8;line-height:1.8">
581  <li>GPU uso: 7-10%</li>
582  <li>VRAM: 1.2GB usada</li>
583  <li>Velocidade com DirectML: <strong>4.27s/it</strong></li>
584  <li>Velocidade sem DirectML (CPU): <strong>4.46s/it</strong></li>
585  <li>Diferença real: <strong>~5%</strong> — irrelevante</li>
586</ul>
587<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:0.75rem"><strong>Por que foi abandonado:</strong></p>
588<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.25rem">O <code>torch-directml</code> está preso na versão <code>0.2.5.dev240914</code> (setembro 2024) e exige <code>torch==2.4.1</code>. O Applio precisa de <code>torch==2.7.1</code>. Conflito irreconciliável:</p>
589<div class="code" style="margin-bottom:1.25rem">
590  <pre><code>torch-directml 0.2.5 requires torch==2.4.1
591hyper-connections 0.4.11 requires torch>=2.5   ← conflito
592torch-einops-utils 0.1.1 requires torch>=2.5   ← conflito</code></pre>
593</div>
594<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.25rem">A Microsoft parou de investir no DirectML para IA. O projeto está essencialmente abandonado.</p>
595<p style="font-size:.88rem;color:#10b981;line-height:1.9;margin-bottom:1.25rem">✅ <strong>Conclusão:</strong> DirectML para RVC em Windows AMD em 2026 = não vale a pena.</p>
596<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:800;margin-top:1.5rem;margin-bottom:.9rem">FASE 3 — ERRO CRÍTICO: VARIÁVEIS DE AMBIENTE</h3>
597<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.25rem">Durante tentativas de forçar CPU, foram usados comandos que <strong>silenciosamente quebravam a extração de características</strong>:</p>
598<div class="code" style="margin-bottom:1.25rem">
599  <span class="code-lang">powershell</span>
600  <pre><code># ❌ NUNCA FAZER ISSO com Applio AMD:
601set USE_DIRECTML=0
602set CUDA_VISIBLE_DEVICES=-1
603set ROCM_VISIBLE_DEVICES=-1
604set HIP_VISIBLE_DEVICES=-1</code></pre>
605</div>
606<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.25rem">Esses comandos deixavam a pasta <code>extracted</code> vazia após a extração. O treino terminava em <strong>segundos</strong> dizendo "Model trained successfully" sem gerar nenhum <code>.pth</code>.</p>
607<div class="code" style="margin-bottom:1.25rem">
608  <pre><code>Not enough data present in the training set.
609Feature files in logs\\my-project\\extracted could not be loaded correctly.</code></pre>
610</div>
611<div class="err" style="margin-bottom:1.25rem">

612  <div class="err-t">⚠️ Armadilha: "Model trained successfully" em segundos = treino fake.</div>
613  <p>Sempre verificar se <code>logs/projeto/extracted/</code> tem arquivos <code>.npy</code> antes de treinar.</p>
614</div>
615
616<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:800;margin-top:1.5rem;margin-bottom:.9rem">FASE 4 — INSTALAÇÃO LIMPA (solução final)</h3>
617<div class="code" style="margin-bottom:1.25rem">
618  <span class="code-lang">powershell</span>
619  <pre><code># 1. Destruir ambiente corrompido
620deactivate
621Remove-Item -Recurse -Force venv
622Remove-Item -Recurse -Force logs
623
624# 2. Criar venv limpo
625py -3.11 -m venv venv
626venv\\Scripts\\activate
627python -m pip install --upgrade pip
628
629# 3. Instalar torch base
630pip install torch==2.4.1 torchaudio==2.4.1 torchvision==0.19.1
631pip install torch-directml
632
633# 4. Instalar dependências sem sobrescrever torch
634pip install -r requirements.txt --no-deps
635
636# 5. Corrigir dependências faltantes
637pip install gradio-client==1.14.0
638pip install cffi soupsieve local-attention
639pip install "aiofiles&lt;25.0" "pandas&lt;3.0" "pydantic&lt;=2.12.3"
640pip install "starlette&lt;1.0,&gt;=0.40.0" "tomlkit&lt;0.14.0"
641pip install "tokenizers&gt;=0.22.0,&lt;=0.23.0"
642pip install audioread decorator lazy-loader msgpack pooch scikit-learn resampy</code></pre>
643</div>
644<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:0.75rem"><strong>config.py — versão final (sem DirectML):</strong></p>
645<div class="code" style="margin-bottom:1.25rem">
646  <span class="code-lang">python</span>
647  <pre><code>import torch
648import json
649import os
650
651@singleton
652class Config:
653    def __init__(self):
654        self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
655        # No AMD Windows sem CUDA, usa "cpu" automaticamente</code></pre>
656</div>
657<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:0.75rem"><strong>Iniciar:</strong></p>
658<div class="code" style="margin-bottom:1.25rem">
659  <span class="code-lang">powershell</span>
660  <pre><code># PowerShell LIMPO, sem nenhum 'set' antes
661python app.py
662# * Running on local URL: http://127.0.0.1:6969  ✅</code></pre>
663</div>
664
665<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:800;margin-top:1.5rem;margin-bottom:.9rem">FASE 5 — ARQUIVOS MUTE FALTANDO</h3>
666<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.25rem">Na instalação via git, os arquivos de silêncio não são criados. Aparecem um por um durante o treino:</p>
667<div class="code" style="margin-bottom:1.25rem">
668  <pre><code>FileNotFoundError: logs\\mute\\sliced_audios\\mute40000.wav
669FileNotFoundError: logs\\mute\\extracted\\mute.npy          ← shape errado causa crash
670FileNotFoundError: logs\\mute\\f0\\mute.wav.npy
671FileNotFoundError: logs\\mute\\f0_voiced\\mute.wav.npy
672RuntimeError: tensor (100) must match size (768)</code></pre>
673</div>
674<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:0.75rem"><strong>Solução — criar todos de uma vez:</strong></p>
675<div class="code" style="margin-bottom:1.25rem">
676  <span class="code-lang">powershell</span>
677  <pre><code>python -c "
678import numpy as np, soundfile as sf, os
679[os.makedirs(d, exist_ok=True) for d in [
680    'logs/mute/sliced_audios','logs/mute/extracted',
681    'logs/mute/f0','logs/mute/f0_voiced'
682]]
683sf.write('logs/mute/sliced_audios/mute40000.wav', np.zeros(int(40000*3.7)), 40000)
684sf.write('logs/mute/sliced_audios/mute48000.wav', np.zeros(int(48000*3.7)), 48000)
685sf.write('logs/mute/sliced_audios/mute32000.wav', np.zeros(int(32000*3.7)), 32000)
686np.save('logs/mute/extracted/mute.npy', np.zeros((196, 768)))
687np.save('logs/mute/f0/mute.wav.npy', np.zeros(100))
688np.save('logs/mute/f0_voiced/mute.wav.npy', np.zeros(100))
689print('OK')
690"</code></pre>
691</div>
692<div class="err" style="margin-bottom:1.25rem">
693  <div class="err-t">⚠️ Crítico: mute.npy precisa ter shape (196, 768)</div>
694  <p>dimensão 768 do embedder contentvec. Qualquer outro shape causa crash.</p>
695</div>
696
697<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:800;margin-top:1.5rem;margin-bottom:.9rem">

697FASE 6 — DATASET E TREINO</h3>
698<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:0.75rem"><strong>Dataset usado:</strong></p>
699<div class="tbl" style="margin-bottom:1.5rem">
700  <table>
701    <thead>
702      <tr>
703        <th>Parâmetro</th>
704        <th>Valor</th>
705      </tr>
706    </thead>
707    <tbody>
708      <tr>
709        <td>Arquivo</td>
710        <td>YURI-TTS.wav</td>
711      </tr>
712      <tr>
713        <td>Duração</td>
714        <td>9 minutos</td>
715      </tr>
716      <tr>
717        <td>Fonte</td>
718        <td>ElevenLabs TTS</td>
719      </tr>
720      <tr>
721        <td>Nota</td>
722        <td>Mínimo aceitável. Ideal: 15-25 min</td>
723      </tr>
724    </tbody>
725  </table>
726</div>
727
728<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:0.75rem"><strong>Configurações do Applio:</strong></p>
729<div class="tbl" style="margin-bottom:1.5rem">
730  <table>
731    <thead>
732      <tr>
733        <th>Parâmetro</th>
734        <th>Valor</th>
735      </tr>
736    </thead>
737    <tbody>
738      <tr>
739        <td>Taxa de amostragem</td>
740        <td>40000 Hz</td>
741      </tr>
742      <tr>
743        <td>Vocoder</td>
744        <td>HiFi-GAN</td>
745      </tr>
746      <tr>
747        <td>Algoritmo de pitch</td>
748        <td>RMVPE</td>
749      </tr>
750      <tr>
751        <td>Embedder</td>
752        <td>contentvec</td>
753      </tr>
754      <tr>
755        <td>Tamanho do lote</td>
756        <td>4</td>
757      </tr>
758      <tr>
759        <td>Total de épocas</td>
760        <td>200</td>
761      </tr>
762      <tr>
763        <td>Número da GPU</td>
764        <td><strong>vazio</strong> (não colocar 0, -1 ou cpu)</td>
765      </tr>
766    </tbody>
767  </table>
768</div>
769
770<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:0.75rem"><strong>Métricas reais:</strong></p>
771<div class="tbl" style="margin-bottom:1.5rem">
772  <table>
773    <thead>
774      <tr>
775        <th>Época</th>
776        <th>Loss</th>
777        <th>Tempo por época</th>
778      </tr>
779    </thead>
780    <tbody>
781      <tr>
782        <td>1</td>
783        <td>27.181</td>
784        <td>7:21</td>
785      </tr>
786      <tr>
787        <td>8</td>
788        <td>17.93</td>
789        <td>7:02</td>
790      </tr>
791      <tr>
792        <td>20</td>
793        <td>16.212</td>
794        <td>7:43</td>
795      </tr>
796      <tr>
797        <td>43</td>
798        <td>15.785</td>
799        <td>5:43</td>
800      </tr>
801    </tbody>
802  </table>
803</div>
804<ul style="list-style-type:disc;padding-left:1.5rem;margin-bottom:1.25rem;color:#94a3b8;line-height:1.8">
805  <li>Velocidade: ~6 minutos por época</li>
806  <li>Total: ~20 horas para 200 épocas</li>
807  <li>Hardware efetivo: CPU Xeon 24 núcleos</li>
808</ul>
809<blockquote style="font-size:.82rem;color:#94a3b8;font-style:italic;line-height:1.7;border-left:3px solid var(--r);padding-left:.9rem;margin-bottom:1.25rem">
810  <strong>ℹ️ Buffer do PowerShell:</strong> de treino overnight o terminal pode parecer travado. O treino continua normalmente — verificar o contador no Applio.
811</blockquote>
812
813<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:800;margin-top:1.5rem;margin-bottom:.9rem">LIÇÕES APRENDIDAS</h3>
814<p style="font-size:.88rem;color:#10b981;font-weight:700;margin-top:1rem;margin-bottom:0.5rem">✅ O que funciona:</p>
815<ol style="font-size:.88rem;color:#94a3b8;line-height:2;padding-left:1.2rem;margin-bottom:1.25rem">
816  <li>Applio + CPU puro no AMD Windows — estável e funcional</li>
817  <li>Python 3.11 obrigatório (não 3.10)</li>
818  <li><code>requirements.txt</code> editado — remover <code>+cu128</code></li>
819  <li>PowerShell limpo — sem variáveis <code>CUDA_VISIBLE_DEVICES</code></li>
820  <li>Arquivos mute criados manualmente antes do treino</li>
821  <li>Campo "Número da GPU" <strong>vazio</strong> no Applio</li>
822</ol>
823<p style="font-size:.88rem;color:#ef4444;font-weight:700;margin-top:1rem;margin-bottom:0.5rem">❌ O que não funciona:</p>
824<ol style="font-size:.88rem;color:#94a3b8;line-height:2;padding-left:1.2rem;margin-bottom:1.25rem">
825  <li>DirectML + torch 2.7.1 — incompatível em 2026</li>
826  <li><code>set CUDA_VISIBLE_DEVICES=-1</code> — quebra a extração de features</li>
827  <li><code>pip install -r requirements.txt</code> sem editar — instala CUDA e falha</li>
828  <li>Dataset grande (horas de áudio) — estouro de RAM/VRAM</li>
829</ol>
830
831<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:800;margin-top:1.5rem;margin-bottom:.9rem">PIPELINE FINAL DE PRODUÇÃO</h3>
832<div class="code" style="margin-bottom:1.25rem">
833  <pre><code>1. Escrever roteiro
834   ↓
8352. Balabolka + Antônio Neural → WAV com interpretação humana
836   ↓
8373. Applio — Inferência
838   Modelo: my-project_200e.pth
839   Índice: my-project.index
840   ↓
8414. Output: WAV com voz do Yuri
842   ↓
8435. Edição e publicação no YouTube</code></pre>
844</div>
845<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:0.75rem"><strong>Velocidade de inferência (pós-treino):</strong></p>
846<ul style="list-style-type:disc;padding-left:1.5rem;margin-bottom:1.25rem;color:#94a3b8;line-height:1.8">
847  <li>~5-30 segundos por minuto de áudio</li>
848  <li>2 horas de áudio → ~30 minutos de processamento</li>
849  <li>Modo Lote disponível no Applio</li>
850</ul>
851
852<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:800;margin-top:1.5rem;margin-bottom:.9rem">CHECKLIST — REPRODUZINDO DO ZERO</h3>
853<ul style="list-style-type:none;padding-left:0;margin-bottom:1.25rem;color:#94a3b8;line-height:1.8">
854  <li><span style="color:#475569">[ ]</span> Python 3.11 instalado</li>
855  <li><span style="color:#475569">[ ]</span> Git instalado</li>
856  <li><span style="color:#475569">[ ]</span> FFmpeg no PATH</li>
857  <li><span style="color:#475569">[ ]</span> Dataset WAV limpo (mínimo 5 min, ideal 15-25 min)</li>
858  <li><span style="color:#475569">[ ]</span> <code>requirements.txt</code> editado (remover <code>+cu128</code>)</li>
859  <li><span style="color:#475569">[ ]</span> <code>config.py</code> sem referências ao <code>torch_directml</code></li>
860  <li><span style="color:#475569">[ ]</span> Arquivos mute criados</li>
861  <li><span style="color:#475569">[ ]</span> PowerShell limpo (sem <code>set CUDA_VISIBLE_DEVICES</code>)</li>
862  <li><span style="color:#475569">[ ]</span> Campo "Número da GPU" <strong>vazio</strong> no Applio</li>
863  <li><span style="color:#475569">[ ]</span> Após extração: verificar que <code>logs/projeto/extracted/</code> tem arquivos <code>.npy</code></li>
864</ul>
865
866<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:800;margin-top:1.5rem;margin-bottom:.9rem">AMD vs NVIDIA EM IA — POSIÇÃO DO APPLIO/RVC (2026)</h3>
867<div class="tbl" style="margin-bottom:1.5rem">
868  <table>
869    <thead>
870      <tr>
871        <th>Plataforma</th>
872        <th>Situação</th>
873      </tr>
874    </thead>
875    <tbody>
876      <tr>
877        <td>NVIDIA CUDA</td>
878        <td>✅ Excelente — suporte nativo</td>
879      </tr>
880      <tr>
881        <td>AMD ROCm Linux</td>
882        <td>⚠️ Razoável — funciona com esforço</td>
883      </tr>
884      <tr>
885        <td>AMD Windows DirectML</td>
886        <td>❌ Abandonado</td>
887      </tr>
888      <tr>
889        <td>AMD Windows CPU</td>
890        <td>✅ Funcional — lento mas estável</td>
891      </tr>
892    </tbody>
893  </table>
894</div>
895<p style="font-size:.85rem;color:#94a3b8;font-style:italic;line-height:1.8"><em>*Hardware legado não morre — só precisa do backend certo.</em></p>`},audio_srt_ptbr:{id:"audio_srt_ptbr",title:"40. PIPELINE COMPLETO — TRANSCRIÇÃO + TRADUÇÃO PT-BR COM RX 580",desc:"Transcrição de vídeo em inglês com tradução automática para português brasileiro, do .mp4 ao .srt sem nenhum serviço pago.",meta:{version:"1.0.0",type:"documento",category:"software",subcategory:"audio",difficulty:"advanced",estimated_time_min:25,tags:["srt-generation","automacao-srt","ffmpeg-pipeline"],last_reviewed:"2026-07-01",relationships:{prerequisites:["linux_nativo"],previous:"linux_nativo",next:"limit_qwen_35b",related:["audio_whisper","limit_qwen_35b"]},part:1}

895,html:`<div class="whisper-doc">
896  <div class="hero">
897    <div class="hero-title"><span>Pipeline Completo</span> — Transcrição + Tradução EN➔PT-BR</div>
898    <div class="hero-sub">Transcrição offline de vídeo em inglês com tradução automática para português brasileiro, do <code>.mp4</code> ao <code>.srt</code> de forma 100% gratuita usando a RX 580 via Vulkan.</div>
899    <div class="hero-badges">
900      <span class="badge badge-gpu">AMD RX 580 8GB</span>
901      <span class="badge badge-win">Windows + PowerShell</span>
902      <span class="badge badge-ok">Vulkan Backend</span>
903      <span class="badge badge-ok">large-v3-turbo</span>
904      <span class="badge badge-win">deep-translator (Gratuito)</span>
905    </div>
906  </div>
907
908  <div class="stat-grid">
909    <div class="stat">
910      <div class="stat-label">tempo total pipeline</div>
911      <div class="stat-value">~8 min</div>
912      <div class="stat-sub">Vídeo de 29m 51s</div>
913    </div>
914    <div class="stat">
915      <div class="stat-label">VRAM Usada</div>
916      <div class="stat-value">2.9 GB</div>
917      <div class="stat-sub">de 8 GB Polaris</div>
918    </div>
919    <div class="stat">
920      <div class="stat-label">Temp. Média GPU</div>
921      <div class="stat-value">50°C</div>
922      <div class="stat-sub">Fan Curve Otimizada</div>
923    </div>
924    <div class="stat">
925      <div class="stat-label">Custo Cloud</div>
926      <div class="stat-value">R$ 0,00</div>
927      <div class="stat-sub">100% Local & Offline</div>
928    </div>
929  </div>
930
931  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:1.5rem;margin-bottom:.5rem">Hardware Utilizado no Teste</h3>
932  <div class="tbl">
933    <table>
934      <thead>
935        <tr>
936          <th>Componente</th>
937          <th>Especificação</th>
938        </tr>
939      </thead>
940      <tbody>
941        <tr>
942          <td><strong>GPU</strong></td>
943          <td>AMD Radeon RX 580 2048SP (8GB VRAM)</td>
944        </tr>
945        <tr>
946          <td><strong>CPU</strong></td>
947          <td>Intel Xeon E5-2690 v3 (24 threads / 12 Cores)</td>
948        </tr>
949        <tr>
950          <td><strong>RAM</strong></td>
951          <td>31.8 GB DDR4 REG ECC</td>
952        </tr>
953        <tr>
954          <td><strong>OS</strong></td>
955          <td>Windows 11 Professional</td>
956        </tr>
957        <tr>
958          <td><strong>Driver</strong></td>
959          <td>Adrenalin 31.0.21925.1001 (2026/05/20)</td>
960        </tr>
961      </tbody>
962    </table>
963  </div>
964
965  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:1.5rem;margin-bottom:.5rem">Fluxo de Dados Unificado (Pipeline)</h3>
966  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-bottom:1rem">
967    O objetivo é mapear um vídeo educacional em inglês para legenda em português compatível com software editor de vídeo (Filmora, Premiere, etc.), rodando a transcrição via GPU nativa e traduzindo cada timestamp sem limites de API de nuvem comercial:
968  </p>
969  <div class="cmd" style="white-space:normal; line-height:1.6; padding: 1rem;">
970    <strong>video.mp4</strong> ➔ <code>FFmpeg (extração)</code> ➔ <strong>video_16k.wav</strong><br>
971    ➔ <code>whisper-cli Vulkan (transcrição)</code> ➔ <strong>video.srt (English)</strong><br>
972    ➔ <code>Python deep-translator (Google Translate)</code> ➔ <strong>video_ptbr.srt (Português)</strong><br>
973    ➔ <code>Importar na Timeline do Filmora</code>
974  </div>
975
976  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:1.5rem;margin-bottom:.5rem">Performance e Timing Detalhado</h3>
977  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-bottom:1rem">
978    Métricas reais obtidas transcrição de um vídeo educacional de <strong>29 minutos e 51 segundos</strong> (áudio em inglês, AAC, 480x360):
979  </p>
980  <div class="tbl" style="margin-bottom:1.5rem">
981    <table>
982      <thead>
983        <tr>
984          <th>Etapa do Pipeline</th>
985          <th>Ferramenta</th>
986          <th>Tempo Gasto</th>
987        </tr>
988      </thead>
989      <tbody>
990        <tr>
991          <td><strong>1. Extração de Áudio WAV Sem Compressão</strong></td>
992          <td>FFmpeg</td>
993          <td><strong>1.7 segundos</strong></td>
994        </tr>
995        <tr>
996          <td><strong>2. Transcrição de Áudio (EN)</strong></td>
997          <td>Whisper.cpp + Vulkan</td>
998          <td><strong>6 minutos e 07 segundos</strong></td>
999        </tr>
1000        <tr>
1001          <td><strong>3. Tradução em Bloco do SRT (EN➔PT-BR)</strong></td>
1002          <td>Python + <code>deep-translator</code></td>
1003          <td><strong>~2 minutos</strong> (limitação de I/O de rede)</td>
1004        </tr>
1005        <tr style="border-top:1px solid rgba(255,255,255,0.15)">
1006          <td><strong>Métrica Total Consolidade</strong></td>
1007          <td><strong>Pipeline Completo</strong></td>
1008          <td><strong>~8 minutos</strong></td>
1009        </tr>
1010      </tbody>
1011    </table>
1012  </div>
1013
1014  <h4 style="color:#fff;font-size:.85rem;font-weight:600;margin-top:.85rem">Timings Internos do Core (whisper_print_timings)</h4>
1015  <div class="card bg-black/40 border border-white/5 p-4 rounded-md my-3 font-mono text-xs text-[#a7f3d0] leading-relaxed">
1016    load modelo : 3.687 ms<br>
1017    mel (áudio) : 2.305 ms<br>
1018    encode GPU  : 204.689 ms<br>
1019    decode      : 4.081 ms<br>
1020    batch total : 108.675 ms<br>
1021    total       : 367.432 ms (por batch de segmentação)
1022  </div>
1023
1024  <div class="section-title">Manual de Instruções Passo a Passo</div>
1025
1026  <div class="step">
1027    <div class="step-header">
1028      <div class="step-num">1</div>
1029      <div class="step-name">Extrair Áudio Compatível via FFmpeg</div>
1030      <div class="step-desc">WAV 16kHz Mono 16-bit</div>
1031    </div>
1032    <div class="step-body">
1033      <p style="font-size:0.8rem;color:#94a3b8;margin-bottom:0.5rem">O Whisper necessita estritamente de áudio WAV cru em 16.000 Hz, formato mono de 16 bits:</p>
1034      <div class="cmd">ffmpeg -i "E:\\MUSICAS SUNO AI\\video.mp4" -ar 16000 -ac 1 -c:a pcm_s16le "E:\\MUSICAS SUNO AI\\video_16k.wav"</div>
1035      <p class="note"><strong>Flags:</strong> <code>-ar 16000</code> força 16kHz · <code>-ac 1</code> converte para canal mono único · <code>-c:a pcm_s16le</code> define PCM de 16 bits sem perda.</p>
1036    </div>
1037  </div>
1038
1039  <div class="step">
1040    <div class="step-header">
1041      <div class="step-num">2</div>
1042      <div class="step-name">Transcrever via Vulkan</div>
1043      <div class="step-desc">

1043Carga pesada na GPU Polaris</div>
1044    </div>
1045    <div class="step-body">
1046      <p style="font-size:0.8rem;color:#94a3b8;margin-bottom:0.5rem">Chamar o executável compilado do Whisper para gerar o arquivo inicial de legendas SRT em inglês:</p>
1047      <div class="cmd">cd C:\\whisper.cpp</div>
1048      <div class="cmd">.\\build\\bin\\Release\\whisper-cli.exe -m models\\ggml-large-v3-turbo.bin -f "E:\\MUSICAS SUNO AI\\video_16k.wav" -l en --output-srt</div>
1049      <p class="note"><strong>Verificação de atividade da GPU:</strong> Certifique-se de ver as linhas <code>ggml_vulkan: Found 1 Vulkan devices: AMD Radeon RX 580 2048SP</code> na inicialização para atestar que o encode pesado foi acoplado na GPU, reduzindo o uso de CPU para ~5%.</p>
1050    </div>
1051  </div>
1052
1053  <div class="step">
1054    <div class="step-header">
1055      <div class="step-num">3</div>
1056      <div class="step-name">Criar e Executar Script Python de Tradução</div>
1057      <div class="step-desc">Zero Keys, Zero Custos</div>
1058    </div>
1059    <div class="step-body">
1060      <p style="font-size:0.8rem;color:#94a3b8;margin-bottom:0.5rem">
1061        Instale a biblioteca de tradução sem chaves de API: <code>pip install deep-translator</code>. Crie e rode o arquivo <code>C:\\whisper.cpp\\traduzir.py</code> com este código:
1062      </p>
1063      <div class="cmd" style="white-space:pre-wrap; font-size:11px;">from deep_translator import GoogleTranslator
1064
1065def traduzir_srt(entrada, saida):
1066    with open(entrada, 'r', encoding='utf-8') as f:
1067        conteudo = f.read()
1068
1069    blocos = conteudo.strip().split('\\n\\n')
1070    resultado = []
1071
1072    for bloco in blocos:
1073        linhas = bloco.split('\\n')
1074        if len(linhas) >= 3:
1075            numero = linhas[0]
1076            timestamp = linhas[1]
1077            texto = ' '.join(linhas[2:])
1078            traduzido = GoogleTranslator(source='en', target='pt').translate(texto)
1079            resultado.append(f"{numero}\\n{timestamp}

1079\\n{traduzido}")
1080
1081    with open(saida, 'w', encoding='utf-8') as f:
1082        f.write('\\n\\n'.join(resultado))
1083    print("Pronto!")
1084
1085traduzir_srt(
1086    r'E:\\MUSICAS SUNO AI\\video_16k.wav.srt',
1087    r'E:\\MUSICAS SUNO AI\\video_16k_ptbr.srt'
1088)</div>
1089<p style="font-size:0.8rem;color:#94a3b8;margin-top:0.5rem">Para rodar:</p>
1090      <div class="cmd">python traduzir.py</div>
1091    </div>
1092  </div>
1093
1094  <div class="step">
1095    <div class="step-header">
1096      <div class="step-num">4</div>
1097      <div class="step-name">Importar Legenda no Filmora</div>
1098      <div class="step-desc">Concluir Edição de Vídeo</div>
1099    </div>
1100    <div class="step-body">
1101      <p style="font-size:0.8rem;color:#94a3b8;line-height:1.6">
1102        1. No Wondershare Filmora, acesse <strong>Arquivo ➔ Importar ➔ Importar arquivos de legenda</strong>.<br>
1103        2. Selecione o arquivo gerado <code>video_16k_ptbr.srt</code>.<br>
1104        3. Arraste e solte o track de legenda diretamente na timeline, sobreposto ao track de vídeo original.
1105      </p>
1106    </div>
1107  </div>
1108
1109  <div class="section-title">Lições Autônomas Aprendidas nas Peripécias</div>
1110  <div class="card-inner">
1111    <div class="lesson">
1112      <div class="lesson-icon">⚠️</div>
1113      <div class="lesson-text"><strong>Evite usar --translate para PT-BR direto nas flags do Whisper:</strong> O parâmetro <code>--translate</code> por design traduz o áudio para o inglês de forma nativa. Para ter legendas em português brasileiro de qualidade, a abordagem correta é extrair inglês por <code>-l en</code> e traduzir com o algoritmo <code>traduzir.py</code>.</div>
1114    </div>
1115    <div class="lesson">
1116      <div class="lesson-icon">⚠️</div>
1117      <div class="lesson-text"><strong>Problemas de Linguagem de Origem Incorreta:</strong> Passar <code>-l pt</code> quando o áudio de origem é falado em inglês faz o Whisper forçar padrões fonéticos portugueses no áudio em inglês, gerando textos completamente incompreensíveis e sem sentido. Defina sempre o idioma real do áudio de entrada como parâmetro inicial.</div>
1118    </div>
1119    <div class="lesson">
1120      <div class="lesson-icon">ℹ️</div>
1121      <div class="lesson-text"><strong>Local do Arquivo Gerado:</strong> Por padrão, em compilados nativos, o arquivo <code>.srt</code> é salvo na mesma pasta física do arquivo de áudio WAV processado, e não dentro do diretório do script ou do Whisper.</div>
1122    </div>
1123    <div class="lesson">
1124      <div class="lesson-icon">✅</div>
1125      <div class="lesson-text"><strong>8 GB de VRAM em 2026 Segue Firme:</strong> O modelo Whisper <code>large-v3-turbo</code> consome apenas 2.9 GB de VRAM física no ecossistema, permitindo folga para outras atividades na sua máquina ou renderização benta paralela. Milhares de dólares economizados em assinaturas e APIs proprietárias.</div>
1126    </div>
1127  </div>
1128
1129  <div class="footer">
1130    RX 580 — velha de 580 anos, mas ainda na ativa 🚀 · Peripécias documentadas em junho/2026
1131  </div>
1132</div>`},audio_whisper:{id:"audio_whisper",title:"39. WHISPER.CPP LOCAL — TRANSCRIÇÃO DE ÁUDIO EM AMD RX 580",desc:"Transcrição de áudio/vídeo acelerada por GPU AMD via Vulkan no Windows (modelo large-v3-turbo).",meta:{version:"1.0.0",type:"documento",category:"software",subcategory:"audio",difficulty:"advanced",estimated_time_min:30,tags:["whispercpp","transcricao-audio","vulkan-whisper"],last_reviewed:"2026-07-01",relationships:{prerequisites:["audio_rvc"],previous:"audio_rvc",next:"linux_nativo",related:["audio_rvc","audio_srt_ptbr"]},part:1},html:`<div class="whisper-doc">
1133<style>
1134.whisper-doc {
1135  --color-border-secondary: rgba(255, 255, 255, 0.05);
1136  --color-border-tertiary: rgba(255, 255, 255, 0.08);
1137  --color-background-secondary: #0c0d10;
1138  --color-background-primary: #07080a;
1139  --color-text-primary: #ffffff;
1140  --color-text-secondary: #94a3b8;
1141  font-family: inherit;
1142}
1143.whisper-doc * { box-sizing: border-box; }
1144.whisper-doc .badge { display: inline-block; font-size: 11px; font-weight: 500; padding: 2px 8px; border-radius: 4px; letter-spacing: 0.04em; text-transform: uppercase; }

1145.whisper-doc .badge-gpu { background: rgba(225, 29, 72, 0.1); border: 1px solid rgba(225, 29, 72, 0.3); color: #fda4af; }
1146.whisper-doc .badge-win { background: rgba(34, 197, 94, 0.1); border: 1px solid rgba(34, 197, 94, 0.2); color: #a7f3d0; }
1147.whisper-doc .badge-ok { background: rgba(14, 165, 233, 0.1); border: 1px solid rgba(14, 165, 233, 0.3); color: #bae6fd; }
1148.whisper-doc .badge-warn { background: rgba(245, 158, 11, 0.1); border: 1px solid rgba(245, 158, 11, 0.3); color: #fef08a; }
1149.whisper-doc .hero { border: 1px solid rgba(255, 255, 255, 0.05); border-radius: 6px; padding: 1.5rem; margin-bottom: 2rem; background: #0c0d10; }
1150.whisper-doc .hero-title { font-size: 26px; font-weight: 700; color: var(--color-text-primary); line-height: 1.2; margin-bottom: 0.5rem; font-family: 'Syne', var(--font-sans); text-transform: uppercase; }
1151.whisper-doc .hero-title span { color: #E11D48; }
1152.whisper-doc .hero-sub { font-size: 13px; color: var(--color-text-secondary); margin-bottom: 1.25rem; line-height: 1.6; font-family: var(--font-sans); }
1153.whisper-doc .hero-badges { display: flex; flex-wrap: wrap; gap: 6px; }
1154.whisper-doc .stat-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); gap: 10px; margin-bottom: 2rem; }
1155.whisper-doc .stat { background: #0c0d10; border: 1px solid rgba(255, 255, 255, 0.05); border-radius: 6px; padding: 0.875rem 1rem; }
1156.whisper-doc .stat-label { font-size: 11px; color: var(--color-text-secondary); text-transform: uppercase; letter-spacing: 0.06em; margin-bottom: 4px; font-family: var(--font-sans); }
1157.whisper-doc .stat-value { font-size: 20px; font-weight: 700; color: var(--color-text-primary); font-family: var(--font-mono); }
1158.whisper-doc .stat-sub { font-size: 11px; color: var(--color-text-secondary); margin-top: 2px; font-family: var(--font-sans); }
1159.whisper-doc .section { margin-bottom: 2.5rem; }
1160.whisper-doc .section-title { font-size: 12px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.08em; color: #E11D48; margin-top: 2rem; margin-bottom: 1rem; font-family: var(--font-sans); padding-bottom: 6px; border-bottom: 1px solid rgba(255, 255, 255, 0.08); }
1161.whisper-doc .step { border: 1px solid rgba(255, 255, 255, 0.05); border-radius: 6px; margin-bottom: 10px; overflow: hidden; background: #050506; }
1162.whisper-doc .step-header { display: flex; align-items: center; gap: 10px; padding: 0.75rem 1rem; background: #0c0d10; border-bottom: 1px solid rgba(255, 255, 255, 0.03); }
1163.whisper-doc .step-num { width: 24px; height: 24px; border-radius: 50%; background: #E11D48; color: #fff; font-size: 12px; font-weight: 700; display: flex; align-items: center; justify-content: center; flex-shrink: 0; font-family: var(--font-mono); }
1164.whisper-doc .step-name { font-size: 13px; font-weight: 700; color: var(--color-text-primary); font-family: var(--font-sans); }
1165.whisper-doc .step-desc { font-size: 11px; color: var(--color-text-secondary); font-family: var(--font-sans); margin-left: auto; }
1166.whisper-doc .step-body { padding: 0.875rem 1rem; }
1167.whisper-doc .cmd { background: #050506; border: 1px solid rgba(255, 255, 255, 0.03); border-radius: 4px; padding: 0.625rem 0.875rem; font-size: 12px; color: #fff; font-family: var(--font-mono); margin-bottom: 6px; overflow-x: auto; white-space: nowrap; border-left: 3px solid #E11D48; }
1168.whisper-doc .note { font-size: 12px; color: var(--color-text-secondary); font-family: var(--font-sans); line-height: 1.6; margin-top: 4px; padding-left: 8px; border-left: 2px solid #E11D48; }
1169.whisper-doc .flag-table { width: 100%; border-collapse: collapse; font-size: 12px; }
1170.whisper-doc .flag-table th { text-align: left; padding: 8px 10px; font-weight: 700; color: var(--color-text-primary); font-family: var(--font-sans); font-size: 10px; text-transform: uppercase; letter-spacing: 0.05em; border-bottom: 1px solid rgba(255, 255, 255, 0.08); background: #111215; }
1171.whisper-doc .flag-table td { padding: 8px 10px; border-bottom: 1px solid rgba(255, 255, 255, 0.03); font-family: var(--font-mono); }
1172.whisper-doc .flag-table td:first-child { color: #fda4af; }

1173.whisper-doc .flag-table td:last-child { font-family: var(--font-sans); color: var(--color-text-secondary); }
1174.whisper-doc .flag-table tr:last-child td { border-bottom: none; }
1175.whisper-doc .perf-bar-wrap { margin: 0.5rem 0 1rem; background: #0c0d10; border: 1px solid rgba(255, 255, 255, 0.05); padding: 1rem; border-radius: 6px; }
1176.whisper-doc .perf-row { display: flex; align-items: center; gap: 8px; margin-bottom: 8px; }
1177.whisper-doc .perf-row:last-child { margin-bottom: 0; }
1178.whisper-doc .perf-label { font-size: 12px; color: var(--color-text-secondary); width: 120px; flex-shrink: 0; font-family: var(--font-sans); text-align: right; }
1179.whisper-doc .perf-bar-bg { flex: 1; height: 8px; background: rgba(255, 255, 255, 0.03); border-radius: 4px; overflow: hidden; }
1180.whisper-doc .perf-bar { height: 100%; border-radius: 4px; }
1181.whisper-doc .perf-time { font-size: 12px; font-family: var(--font-mono); color: var(--color-text-primary); width: 70px; flex-shrink: 0; margin-left: 8px; }
1182.whisper-doc .lesson { display: flex; gap: 10px; padding: 10px 0; border-bottom: 1px solid rgba(255, 255, 255, 0.03); align-items: flex-start; }
1183.whisper-doc .lesson:last-child { border-bottom: none; }
1184.whisper-doc .lesson-icon { font-size: 16px; flex-shrink: 0;

1184 margin-top: 1px; }
1185.whisper-doc .lesson-text { font-size: 12.5px; color: var(--color-text-secondary); font-family: var(--font-sans); line-height: 1.6; }
1186.whisper-doc .lesson-text strong { font-weight: 700; color: var(--color-text-primary); }
1187.whisper-doc .footer { text-align: center; font-size: 11px; color: #475569; font-family: var(--font-mono); padding-top: 1.5rem; border-top: 1px solid rgba(255, 255, 255, 0.05); margin-top: 2rem; }
1188.whisper-doc .two-col { display: grid; grid-template-columns: 1fr 1fr; gap: 10px; }
1189@media (max-width: 600px) { .whisper-doc .two-col { grid-template-columns: 1fr; } }
1190.whisper-doc .env-row { display: flex; justify-content: space-between; padding: 6px 0; border-bottom: 1px solid rgba(255, 255, 255, 0.03); font-size: 12px; }
1191.whisper-doc .env-row:last-child { border-bottom: none; }
1192.whisper-doc .env-key { color: var(--color-text-secondary); font-family: var(--font-sans); }
1193.whisper-doc .env-val { font-family: var(--font-mono); color: var(--color-text-primary); text-align: right; }
1194.whisper-doc .card-inner { background: #0c0d10; border: 1px solid rgba(255, 255, 255, 0.05); border-radius: 6px; padding: 1rem 1.25rem; }
1195</style>
1196
1197  <div class="hero">
1198    <div class="hero-title"><span>whisper.cpp</span> + RX 580 via Vulkan</div>
1199    <div class="hero-sub">

1199Transcrição de áudio/vídeo com aceleração GPU AMD no Windows — usando Vulkan para rodar modelos Whisper direto na VRAM da RX 580. Uma GPU de 2017 botando pra trabalhar em 2026.</div>
1200    <div class="hero-badges">
1201      <span class="badge badge-gpu">AMD RX 580 8GB</span>
1202      <span class="badge badge-win">Windows + PowerShell</span>
1203      <span class="badge badge-ok">Vulkan Backend</span>
1204      <span class="badge badge-ok">large-v3-turbo</span>
1205      <span class="badge badge-warn">FFmpeg pré-req</span>
1206    </div>
1207  </div>
1208
1209  <div class="stat-grid">
1210    <div class="stat">
1211      <div class="stat-label">tempo total</div>
1212      <div class="stat-value">~5 min</div>
1213      <div class="stat-sub">para 15 min de vídeo</div>
1214    </div>
1215    <div class="stat">
1216      <div class="stat-label">VRAM usada</div>
1217      <div class="stat-value">2.6 GB</div>
1218<div class="stat-sub">de 8 GB disponíveis</div>
1219    </div>
1220    <div class="stat">
1221      <div class="stat-label">speedup vs CPU</div>
1222      <div class="stat-value">~150×</div>
1223      <div class="stat-sub">antes: 12h para 40min</div>
1224    </div>
1225    <div class="stat">
1226      <div class="stat-label">modelo</div>
1227      <div class="stat-value">1.6 GB</div>
1228      <div class="stat-sub">large-v3-turbo</div>
1229    </div>
1230  </div>
1231
1232  <div class="section">
1233    <div class="section-title">pré-requisitos verificados</div>
1234    <div class="card-inner">
1235      <div class="env-row"><span class="env-key">Vulkan SDK</span><span class="env-val">glslc v2026.1</span></div>
1236      <div class="env-row"><span class="env-key">Git</span><span class="env-val">2.54.0.windows.1</span></div>
1237      <div class="env-row"><span class="env-key">CMake</span><span class="env-val">4.3.2</span></div>
1238      <div class="env-row"><span class="env-key">MSVC (Build Tools)</span><span class="env-val">14.52.36328 — VS 2026</span></div>
1239      <div class="env-row"><span class="env-key">GPU detectada</span><span class="env-val">AMD Radeon RX 580 2048SP</span></div>
1240      <div class="env-row"><span class="env-key">FFmpeg</span><span class="env-val">2026-04-19 (Gyan full build)</span></div>
1241    </div>
1242    <p class="note" style="margin-top: 8px;">Verificar RX 580 no Vulkan: <span style="font-family:var(--font-mono); color:#fda4af;">vulkaninfo --summary</span> → deve aparecer AMD Radeon RX 580 2048SP</p>
1243  </div>
1244
1245  <div class="section">
1246    <div class="section-title">compilação — passo a passo</div>
1247
1248    <div class="step">
1249      <div class="step-header">
1250        <div class="step-num">1</div>
1251        <div class="step-name">ativar ambiente MSVC x64</div>
1252        <div class="step-desc">a cada nova sessão</div>
1253      </div>
1254      <div class="step-body">
1255        <div class="cmd">& "C:\\Program Files (x86)\\Microsoft Visual Studio\\18\\BuildTools\\VC\\Auxiliary\\Build\\vcvars64.bat"</div>
1256        <p class="note">Precisa repetir toda vez que abrir um novo PowerShell antes de compilar. Saída esperada: "Environment initialized for: 'x64'"</p>
1257      </div>
1258    </div>
1259
1260    <div class="step">
1261      <div class="step-header">
1262        <div class="step-num">2</div>
1263        <div class="step-name">clonar repositório</div>
1264        <div class="step-desc">~40 MB</div>
1265      </div>
1266      <div class="step-body">
1267        <div class="cmd">cd C:\\</div>
1268        <div class="cmd">git clone https://github.com/ggml-org/whisper.cpp</div>
1269        <div class="cmd">cd whisper.cpp</div>
1270      </div>
1271    </div>
1272
1273    <div class="step">
1274      <div class="step-header">
1275        <div class="step-num">3</div>
1276        <div class="step-name">cmake com Vulkan habilitado</div>
1277        <div class="step-desc">flag crítica: -DGGML_VULKAN=ON</div>
1278      </div>
1279      <div class="step-body">
1280        <div class="cmd">cmake -B build -DGGML_VULKAN=ON -DGGML_HIPBLAS=OFF -DGGML_HIP=OFF -DGGML_CUDA=OFF</div>
1281        <div class="cmd">cmake --build build --config Release -j4</div>
1282        <p class="note">Dura ~5-10 min. Warnings de C4267/C4244 são normais — não afetam o funcionamento. Binários ficam em build\\bin\\Release\\</p>
1283      </div>
1284    </div>
1285
1286    <div class="step">
1287      <div class="step-header">
1288        <div class="step-num">4</div>
1289        <div class="step-name">baixar modelo large-v3-turbo</div>
1290        <div class="step-desc">~1.6 GB</div>
1291      </div>
1292      <div class="step-body">
1293        <div class="cmd">Invoke-WebRequest -Uri "https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-large-v3-turbo.bin" -OutFile "models\\ggml-large-v3-turbo.bin"</div>
1294        <p class="note">Outros modelos: large-v3 (~6GB VRAM, melhor qualidade), medium (~2.5GB), small (~1GB)</p>
1295      </div>
1296    </div>
1297  </div>
1298
1299  <div class="section">
1300    <div class="section-title">uso — fluxo completo mp4 → txt</div>
1301
1302    <div class="step">
1303      <div class="step-header">
1304        <div class="step-num" style="background:#1D9E75;">1</div>
1305        <div class="step-name">extrair áudio com FFmpeg</div>
1306        <div class="step-desc">whisper não lê mp4 direto no Windows</div>
1307      </div>
1308      <div class="step-body">
1309        <div class="cmd">ffmpeg -i "E:\\videos\\video.mp4" -ar 16000 -ac 1 -c:a pcm_s16le "E:\\videos\\video.wav"</div>
1310        <p class="note">-ar 16000 = 16kHz (obrigatório para o Whisper) · -ac 1 = mono · -c:a pcm_s16le = WAV sem compressão</p>
1311      </div>
1312    </div>
1313
1314    <div class="step">
1315      <div class="step-header">
1316        <div class="step-num" style="background:#1D9E75;">2</div>
1317        <div class="step-name">transcrever / traduzir</div>
1318        <div class="step-desc">GPU cuida do resto</div>
1319      </div>
1320      <div class="step-body">
1321        <div class="cmd">.\\build\\bin\\Release\\whisper-cli.exe -m models\\ggml-large-v3-turbo.bin -f "E:\\videos\\video.wav" -l pt --output-txt</

1321div>
1322        <p style="font-size:12px; color:var(--color-text-secondary); margin: 6px 0 4px; font-family:var(--font-sans);">com tradução para inglês:</p>
1323        <div class="cmd">.\\build\\bin\\Release\\whisper-cli.exe -m models\\ggml-large-v3-turbo.bin -f "E:\\videos\\video.wav" -l pt --translate --output-txt</div>
1324        <p class="note">--translate sempre gera inglês (limitação do Whisper). Para outros idiomas de destino, usar etapa adicional de tradução.</p>
1325      </div>
1326    </div>
1327
1328    <div style="margin-top: 1rem;">
1329      <div class="section-title" style="margin-bottom: 0.75rem;">flags úteis</div>
1330      <div class="card-inner">
1331        <table class="flag-table">
1332          <thead><tr><th>flag</th><th>descrição</th></tr></thead>
1333          <tbody>
1334            <tr><td>-l pt</td><td>idioma fonte: português</td></tr>
1335            <tr><td>-l en</td><td>idioma fonte: inglês</td></tr>
1336            <tr><td>--translate</td><td>traduz tudo para inglês</td></tr>
1337            <tr><td>--output-txt</td><td>salva .txt com transcrição</td></tr>
1338            <tr><td>--output-srt</td><td>salva legendas .srt com timestamps</td></tr>
1339            <tr><td>--output-vtt</td><td>salva legendas .vtt</td></tr>
1340            <tr><td>-t 8</td><td>usa 8 threads de CPU (padrão: 4)</td></tr>
1341            <tr><td>--device 0</td><td>seleciona GPU 0 (padrão)</td></tr>
1342            <tr><td>--no-gpu</td><td>força CPU (modo lento)</td></tr>
1343          </tbody>
1344        </table>
1345      </div>
1346    </div>
1347  </div>
1348
1349  <div class="section">
1350    <div class="section-title">performance — vídeo de 15 min</div>
1351    <div class="perf-bar-wrap">
1352      <div class="perf-row">
1353        <div class="perf-label">load modelo</div>
1354        <div class="perf-bar-bg"><div class="perf-bar" style="width:1.3%; background:#E11D48;"></div></div>
1355        <div class="perf-time">4 s</div>
1356      </div>
1357      <div class="perf-row">
1358        <div class="perf-label">mel (áudio)</div>
1359        <div class="perf-bar-bg"><div class="perf-bar" style="width:0.4%; background:#E11D48;"></div></div>
1360        <div class="perf-time">1.2 s</div>
1361      </div>
1362      <div class="perf-row">
1363        <div class="perf-label">encode GPU</div>
1364        <div class="perf-bar-bg"><div class="perf-bar" style="width:23.7%; background:#1D9E75;"></div></div>
1365        <div class="perf-time">73 s</div>
1366      </div>
1367      <div class="perf-row">
1368        <div class="perf-label">decode + batch</div>
1369        <div class="perf-bar-bg"><div class="perf-bar" style="width:54.7%; background:#1D9E75;"></div></div>
1370        <div class="perf-time">168 s</div>
1371      </div>
1372      <div class="perf-row">
1373        <div class="perf-label" style="font-weight:700; color:var(--color-text-primary);">total</div>
1374        <div class="perf-bar-bg"><div class="perf-bar" style="width:100%; background:#E11D48;"></div></div>
1375        <div class="perf-time" style="font-weight:700;">307 s</div>
1376      </div>
1377    </div>
1378    <p class="note">CPU em ~5% durante toda a transcrição. VRAM: 2.6 GB de 8 GB. A GPU fez o trabalho — CPU ficou de molho.</p>
1379  </div>
1380
1381  <div class="section">
1382    <div class="section-title">lições aprendidas nas peripécias</div>
1383    <div class="card-inner">
1384      <div class="lesson">
1385        <div class="lesson-icon">⚠️</div>
1386<div class="lesson-text"><strong>WSL2 não expõe a RX 580 via Vulkan</strong> — usar sempre PowerShell/Windows nativo para aproveitar a GPU.</div>
1387      </div>
1388      <div class="lesson">
1389        <div class="lesson-icon">⚠️</div>
1390        <div class="lesson-text"><strong>Whisper não lê MP4 no Windows</strong> — sempre extrair o áudio com FFmpeg antes. Usar -ar 16000 -ac 1 -c:a pcm_s16le.</div>
1391      </div>
1392      <div class="lesson">
1393        <div class="lesson-icon">ℹ️</div>
1394        <div class="lesson-text"><strong>--translate só gera inglês</strong> — é limitação do Whisper, não da configuração. Para PT como destino, precisa de etapa extra.</div>
1395      </div>
1396      <div class="lesson">
1397        <div class="lesson-icon">ℹ️</div>
1398        <div class="lesson-text"><strong>vcvars64.bat precisa ser reativado</strong> a cada nova sessão do PowerShell antes de qualquer compilação.</div>
1399      </div>
1400      <div class="lesson">
1401        <div class="lesson-icon">✅</div>
1402        <div class="lesson-text"><strong>8 GB de VRAM em 2026 ainda é sólido</strong> — large-v3-turbo usa apenas 2.6 GB. GPU "velha" com VRAM decente bate GPU nova com 4 GB fácil.</div>
1403      </div>
1404      <div class="lesson">
1405        <div class="lesson-icon">✅</div>
1406        <div class="lesson-text"><strong>Warnings C4267/C4244 durante compilação são normais</strong> — conversões de tipo esperadas no código do ggml, não impactam funcionamento.</div>
1407      </div>
1408    </div>
1409  </div>
1410
1411  <div class="footer">
1412    RX 580 — velha de 580 anos, mas ainda na ativa 🚀 · Peripécias documentadas em junho/2026
1413  </div>
1414
1415</div>`},benchmarks:{id:"benchmarks",title:"23. BENCHMARKS REAIS DO LABORATÓRIO",desc:"Dados consolidados de telemetria medidos diretamente nos logs de inferência das duas arquiteturas.",meta:{version:"1.0.0",type:"benchmark",category:"hardware",subcategory:"benchmarks",difficulty:"beginner",estimated_time_min:15,tags:["telemetria","gpu-benchmarks","performance-history"],last_reviewed:"2026-07-01",relationships:{prerequisites:["guia"],previous:"guia",next:"cpu",related:["hardware","cpu"]}},html:`<div class="tbl"><table>
1416    <thead><tr><th>

1416Modo de Execução do Modelo</th><th>Arquitetura Alocada</th><th>Métrica Comercial Obtida</th><th>Status de Viabilidade</th></tr></thead>
1417    <tbody>
1418      <tr><td>Inferência de Texto Comum</td><td>CPU Xeon Pura (Sem Aceleração)</td><td>3 a 5 tokens por segundo</td><td>❌ Ineficiente / Respostas Lentas</td></tr>
1419      <tr class="hig"><td>Inferência de Texto Compilada</td><td>RX 580 8GB via Vulkan Backend</td><td>15 a 16 tokens por segundo</td><td>✅ Altamente Produtivo / Instantâneo</td></tr>
1420      <tr><td>Amostragem SD 1.5 (20 Steps)</td><td>ComfyUI Windows via DirectML</td><td>~450 segundos por imagem</td><td>❌ Instável / Descarte por Erro Crítico</td></tr>
1421      <tr class="hig"><td>Amostragem SD 1.5 (20 Steps)</td><td>stable-diffusion.cpp via Vulkan</td><td>72 segundos por imagem</td><td>✅ Otimizado / Resposta Rápida</td></tr>
1422    </tbody>
1423  </table></div>`},codacus:{id:"codacus",title:"33. MÉTODO CODACUS vs AIVisionsLab",desc:"Experimento das 5 flags que salvaram uma GTX 1060 — funcionam numa RX 580?",meta:{version:"1.0.0",type:"estudo_caso",category:"estudo-caso",subcategory:"metodo-historico",difficulty:"advanced",estimated_time_min:25,tags:["gtx-1060","metodo-codacus","nvidia-pascal"],last_reviewed:"2026-07-01",relationships:{prerequisites:["dadhacks"],previous:"dadhacks",next:"proximos",related:["dadhacks","amihart"]}},html:`<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.25rem">
1424  O canal Codacus publicou um vídeo de grande repercussão demonstrando como rodar um modelo de <strong>35B parâmetros numa GTX 1060 de 6GB</strong> usando 5 flags específicas do <code>llama.cpp</code>, saltando de 3.0 tokens/s no baseline para 17.0 tokens/s pós-otimizações. O AIVisionsLab testou essa hipótese na antiga <strong>RX 580 2048SP 8GB com Vulkan</strong> — comparando Windows e Linux nativo.
1425</p>
1426
1427<div class="tbl">
1428  <table>
1429    <thead>
1430      <tr>
1431        <th>Métrica / Configuração</th>
1432        <th>Setup Codacus (Ref)</th>
1433        <th>Setup AIVisionsLab</th>
1434      </tr>
1435    </thead>
1436    <tbody>
1437      <tr>
1438        <td><strong>GPU</strong></td>
1439        <td>Nvidia GTX 1060 6GB</td>
1440        <td>AMD Radeon RX 580 2048SP 8GB</td>
1441      </tr>
1442      <tr>
1443        <td><strong>Backend</strong></td>
1444        <td>CUDA</td>
1445        <td>Vulkan (Mesa RADV)</td>
1446      </tr>
1447      <tr>
1448        <td><strong>OS</strong></td>
1449        <td>Linux + Docker</td>
1450        <td>Windows 10 / Ubuntu 26.04</td>
1451      </tr>
1452      <tr>
1453        <td><strong>CPU</strong></td>
1454        <td>Intel Core i3-8100</td>
1455        <td>Intel Xeon E5-2690 v3 (12c/24t)</td>
1456      </tr>
1457      <tr>
1458        <td><strong>Modelo de Teste</strong></td>
1459        <td>Qwen3.6 35B-A3B Q4_K_M</td>
1460        <td>Qwen3.6 35B-A3B Q4_K_M (Mesmo modelo)</td>
1461      </tr>
1462    </tbody>
1463  </table>
1464</div>
1465
1466<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:1.5rem;margin-bottom:.5rem">O Impacto das 5 Flags no Vulkan AMD</h3>
1467
1468<div class="tbl">
1469  <table>
1470    <thead>
1471      <tr>
1472        <th>Flag Aplicada</th>
1473        <th>Impacto na GTX 1060 (CUDA)</th>
1474        <th>Impacto na RX 580 (Vulkan Windows)</th>
1475        <th>Impacto na RX 580 (Vulkan Linux Native)</th>
1476      </tr>
1477    </thead>
1478    <tbody>
1479      <tr>
1480        <td><strong>Baseline</strong></td>
1481        <td>3.0 tokens/s (-ngl 20)</td>
1482        <td><strong>7.62 tokens/s (-ngl 10)</strong></td>
1483        <td><strong>5.18 tokens/s (-ngl 20)</strong></td>
1484      </tr>
1485      <tr>
1486        <td><code>--override-tensor exps=CPU</code></td>
1487        <td>10.0 tokens/s (+233%)</td>
1488        <td>6.92 tokens/s (❌ -9%)</td>
1489        <td>4.62 tokens/s (❌ -11%)</td>
1490      </tr>
1491      <tr>
1492        <td><code>--no-mmap</code> (ou <code>-mmp 0</code>)</td>
1493        <td>13.5 tokens/s (+35%)</td>
1494        <td>7.44 tokens/s (❌ -2%)</td>
1495        <td>4.86 tokens/s (❌ -6%)</td>
1496      </tr>
1497      <tr>
1498        <td>Quantização KV cache (<code>-ctk/ctv</code>)</td>
1499        <td>17.0 tokens/s (+26%)</td>
1500        <td>7.58 tokens/s (❌ -1%)</td>
1501        <td><strong>5.41 tokens/s (✅ +4%)</strong></td>
1502      </tr>
1503      <tr>
1504        <td><code>--mlock</code> / ngl extra</td>
1505        <td>Estabilidade ampliada</td>
1506        <td>OOM acima de ngl 10 (❌)</td>
1507        <td>OOM acima de ngl 20 (❌)</td>
1508      </tr>
1509    </tbody>
1510  </table>
1511</div>
1512
1513<div class="card bg-black/40 border border-white/5 p-4 rounded-md my-4">
1514  <h4 style="color:#fff;font-weight:700;font-size:0.9rem;margin-bottom:0.5rem">⚠️ Análise das Descobertas Técnicas</h4>
1515  <p style="font-size:0.8rem;color:#94a3b8;line-height:1.7">
1516    <strong>1. Modelo de Alocação de VRAM:</strong> Por que ngl 20 dava OOM no Windows mas passou no Linux? O driver proprietário da AMD no Windows aloca a memória de forma mais conservadora, enquanto o driver open-source <strong>Mesa RADV no Linux</strong> permite que mais layers sejam descarregados no buffer (teto máximo real de 20 layers vs 10 layers no Windows).
1517  </p>
1518  <p style="font-size:0.8rem;color:#94a3b8;line-height:1.7;margin-top:0.5rem">
1519    <strong>2. Incompatibilidade de Backend:</strong> O roteamento <code>override-tensor exps=CPU</code> para isolar experts Mixtral/MoE da GPU funciona de forma espetacular em CUDA devido ao acesso PCIe de alta velocidade nativo da Nvidia. Sob o backend Vulkan, o overhead dessa movimentação destrói os ganhos de processamento.
1520  </p>
1521  <p style="font-size:0.8rem;color:#94a3b8;line-height:1.7;margin-top:0.5rem">
1522    <strong>3. O Xeon Superior Compensou o Backend:</strong> Mesmo com menor otimização de VRAM, a infraestrutura da CPU Xeon E5-2690 v3 (24 threads) do AIVisionsLab garantiu um baseline original superior de 7.62 tokens/s — batendo os 3.0 tokens/s baselines do i3

1522-8100 do Codacus.
1523  </p>
1524</div>
1525
1526<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:1.5rem;margin-bottom:.5rem">Comparativo Mistral 7B — CPU Pura vs Vulkan</h3>
1527<p style="font-size:.88rem;color:#94a3b8;line-height:1.9">
1528  Para demonstrar o valor bruto e absoluto do backend Vulkan em hardware legado Polaris:
1529</p>
1530<div class="code ok">
1531  <pre>💻 WSL2 Ubuntu (CPU Pura)  ·  24 threads  ·  1.79 tokens/s
1532🎮 Windows 10 (Vulkan GPU)  ·  12 threads  ·  18.54 tokens/s (⚡ 10x Mais Rápido!)</pre>
1533</div>
1534<blockquote style="font-size:.82rem;color:#64748b;font-style:italic;line-height:1.8;border-left:3px solid #E11D48;padding-left:.9rem;margin-top:1rem">
1535  "As 5 flags do Codacus funcionam perfeitamente para marcas NVIDIA + CUDA + Linux Docker. No ecossistema AMD + Vulkan, nossa própria receita é diferente mas incrivelmente sólida!"
1536</blockquote>`},"comfyui-directml-func":{id:"comfyui-directml-func",title:"17. FIXES DIRECTML",desc:"Instalação da roda de desenvolvimento específica para forçar estabilidade operacional.",meta:{version:"1.0.0",type:"documento",category:"troubleshooting",subcategory:"comfyui",difficulty:"intermediate",estimated_time_min:15,tags:["comfyui-batch-fix","memoria-virtual","crash-mitigation"],last_reviewed:"2026-07-01",relationships:{prerequisites:["comfyui-directml"],previous:"comfyui-directml",next:"comfyui-flux",related:["comfyui-directml","scripts"]}},html:`<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1rem">Caso haja necessidade de executar testes comparativos na camada DirectML sem crashes de inicialização, é preciso fazer o downgrade forçado dos pacotes utilizando a build de desenvolvimento de maio de 2024:</p>
1537  <div class="code"><pre># Comando para expurgar dependências conflitantes e instalar o dev wheel estável
1538pip uninstall torch torch-directml torchaudio
1539pip install torch==2.3.1+cpu --index-url https://download.pytorch.org/whl/cpu
1540pip install torch-directml==0.2.1.dev240521 --no-deps</pre></div>`},"comfyui-directml":{id:"comfyui-directml",title:"16. DIRECTML",desc:"Explicação técnica detalhada das falhas crônicas encontradas durante os testes na branch Windows.",meta:{version:"1.0.0",type:"documento",category:"troubleshooting",subcategory:"comfyui",difficulty:"intermediate",estimated_time_min:15,tags:["directml-fix","windows-driver","comfyui-windows"],last_reviewed:"2026-07-01",relationships:{prerequisites:["comfyui-wsl"],previous:"comfyui-wsl",next:"comfyui-directml-func",related:["troubleshoot-comfyui","comfyui-portable-amd"]}},html:`<div class="err"><div class="err-t">Falha Estrutural — Incompatibilidade do Módulo CLIPTextEncode</div><p>Ao carregar os fluxos básicos de geração de imagem utilizando o instalador nativo DirectML para placas AMD, o console aborta a execução retornando a seguinte exceção impeditiva:</p><div class="code"><span class="code-lang">stderr</span><pre>File "comfy\\utils.py", line 245, in encode_token
1541NotImplementedError: Cannot access storage of OpaqueTensorImpl</pre></div><p style="margin-top:.45rem"><strong style="color:#e2e8f0">Análise de Engenharia:</strong> O driver DirectML encapsula os dados de memória em objetos conhecidos como <code>OpaqueTensorImpl</code>. Quando as funções internas de otimização de atenção matemática do ComfyUI tentam ler o conteúdo bruto desses tensores na memória física, a camada do ecossistema Microsoft bloqueia o acesso, interrompendo o pipeline de renderização.</p></div>`},"comfyui-flux":{id:"comfyui-flux",title:"18. PARAMETRIZAÇÃO CRÍTICA DO FLUX.1 SCHNELL",desc:"Configuração matemática obrigatória para processar o modelo SOTA na CPU sem estourar os limites da RAM.",meta:{version:"1.0.0",type:"documento",category:"modelagem",subcategory:"comfyui",difficulty:"intermediate",estimated_time_min:20,tags:["flux-fp8","comfyui-workflow","vram-allocation"],last_reviewed:"2026-07-01",relationships:{prerequisites:["comfyui-directml-func"],previous:"comfyui-directml-func",next:"comfyui-modelos",related:["flux_server","comfyui-modelos"]}},html:`<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1rem">O processamento do FLUX.1 Schnell de 16GB em modo de CPU exige limites de amostragem fixos. Desvios nestas regras provocam o travamento da máquina por saturação extrema de memória:</p>
1542  <div class="tbl"><table>
1543    <thead><tr><th>

1543Parâmetro de Amostragem</th><th>Valor de Ajuste Obrigatório</th><th>Explicação Técnica no Xeon</th></tr></thead>
1544    <tbody>
1545      <tr class="hig"><td>Sampling Steps</td><td>4 Steps</td><td>O modelo Schnell foi desenhado para fechar a convergência de ruído em apenas 4 interações.</td></tr>
1546      <tr class="hig"><td>CFG Scale</td><td>1.0</td><td>Valores acima de 1.0 destroem a fidelidade das texturas e distorcem a distribuição de tensores.</td></tr>
1547      <tr><td>Scheduler Type</td><td>sgm_uniform</td><td>Garante a interpolação linear dos tensores de difusão de forma otimizada para instruções AVX2.</td></tr>
1548    </tbody>
1549  </table></div>`},"comfyui-modelos":{id:"comfyui-modelos",title:"14. GESTÃO ZERO-COPY DE MODELOS NO NVMe",desc:"Técnica de links simbólicos para unificar o armazenamento de checkpoints pesados sem duplicar arquivos.",meta:{version:"1.0.0",type:"documento",category:"modelagem",subcategory:"armazenamento",difficulty:"intermediate",estimated_time_min:15,tags:["symlink","diretorios-compartilhados","espaco-disco"],last_reviewed:"2026-07-01",relationships:{prerequisites:["comfyui-flux"],previous:"comfyui-flux",next:"animatediff-video",related:["arquivos","modelos"]}},html:`<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1rem">Para evitar o desperdício de espaço no drive SSD NVMe e eliminar a necessidade de manter cópias gigantes idênticas entre o Windows e o Linux (WSL2), cria-se um link simbólico direto apontando para o sistema de arquivos cruzado:</p>
1550  <div class="code"><span class="code-lang">bash</span><pre># Executar este bloco dentro do terminal do subsistema Linux Ubuntu
1551cd ~/ComfyUI/models/checkpoints/
1552
1553# Criar o ponto de ancoragem virtual espelhando o arquivo físico localizado na partição E: do Windows
1554ln -s "/mnt/e/ComfyUI_Models/checkpoints/flux1-schnell-fp8.safetensors" ./flux1-schnell-fp8.safetensors</pre></div>`},"comfyui-portable-amd":{id:"comfyui-portable-amd",title:"29. COMFYUI PORTÁTIL v0.3.48 — IMPLEMENTAÇÃO AMD",desc:"Configurações específicas e injeção de variáveis de ambiente para a arquitetura Polaris.",meta:{version:"1.0.0",type:"documento",category:"software",subcategory:"comfyui",difficulty:"intermediate",estimated_time_min:15,tags:["comfyui-portable","directml-setup","portable-workflow"],last_reviewed:"2026-07-01",relationships:{prerequisites:["troubleshoot-comfyui"],previous:"troubleshoot-comfyui",next:"comunidade",related:["comfyui-directml","comfyui-flux"]}},html:`<div class="card">
1555    <p style="font-size:.88rem;color:#94a3b8;line-height:1.9">Para implementações que utilizam pacotes portáteis pré-compilados do ComfyUI focados em compatibilidade AMD DirectML no Windows, o interpretador Python precisa de ajuda para identificar corretamente as capacidades de hardware da GPU antiga:</p>
1556    <div class="code"><span class="code-lang">batch</span><pre># Inserir no topo do arquivo run_nvidia_gpu.bat ou do script portátil customizado:
1557set HSA_OVERRIDE_GFX_VERSION=8.0.3
1558set DXVK_ASYNC=1
1559
1560.\\python_embeded\\python.exe main.py --directml</pre></div>
1561    <p style="margin-top:.5rem;font-size:.82rem;color:#64748b;line-height:1.6">O sinalizador <code>8.0.3</code> emula o ID de arquitetura de compilação necessário para que as bibliotecas numéricas processem tensores sem travar o barramento PCIe da série RX 500.</p>
1562  </div>`},"comfyui-wsl":{id:"comfyui-wsl",title:"15. COMFYUI WSL2",desc:"Configurando o ambiente Linux virtualizado para herdar o poder computacional do Xeon e RAM ECC.",meta:{version:"1.0.0",type:"documento",category:"automacao",subcategory:"virtualizacao",difficulty:"advanced",estimated_time_min:30,tags:["wsl2-linux","comfyui-wsl","direct-vulkan"],last_reviewed:"2026-07-01",relationships:{prerequisites:["sd_openwebui_integration"],previous:"sd_openwebui_integration",next:"comfyui-directml",related:["comfyui-flux","linux_nativo"]}},html:`<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1rem">Para contornar o limite de VRAM física da GPU em modelos massivos, o ComfyUI é instanciado dentro do subsistema Linux apontando a execução estritamente para os cores do processador:</p>
1563  <div class="code"><span class="code-lang">bash</span>

1563<pre># Ativar o ambiente virtual isolado via Miniconda no terminal do Ubuntu
1564conda activate comfy_env
1565
1566# Chamar o script principal forçando o uso do hardware de processamento central
1567python main.py --cpu --listen 0.0.0.0 --port 8188</pre></div>
1568  <div class="note info" style="margin-top:.75rem">O painel de nós do ComfyUI estará totalmente disponível no lado do Windows através da rota mapeada: <strong>http://localhost:8188</strong></div>`},comunidade:{id:"comunidade",title:"30. CONTEXTO E FONTES DA COMUNIDADE",desc:"Repositórios de conhecimento hacker e fóruns dedicados à preservação e otimização de hardware legado.",meta:{version:"1.0.0",type:"artigo",category:"comunidade",subcategory:"comunidade",difficulty:"beginner",estimated_time_min:10,tags:["comunidade-hacker","links-utiles","github-preservation"],last_reviewed:"2026-07-01",relationships:{prerequisites:["comfyui-portable-amd"],previous:"comfyui-portable-amd",next:"amihart",related:["amihart","dadhacks","codacus"]}},html:`<div class="card acc">
1569    <p style="font-size:.85rem;color:#94a3b8;line-height:1.9">A engenharia reversa e a viabilidade técnica deste laboratório baseiam-se em discussões de otimização documentadas por comunidades focadas na extração de performance máxima de componentes de baixo custo:</p>
1570    <ul style="padding-left:1.2rem;margin-top:.5rem;font-size:.85rem;color:#e2e8f0;line-height:2">
1571      <li>Servidor Discord Oficial AIVisionsLab: <strong style="color:#5865F2"><a href="https://discord.gg/FmkxZjN8f" target="_blank" rel="noopener noreferrer" style="color:#5865F2;text-decoration:underline">discord.gg/FmkxZjN8f</a></strong></li>
1572      <li>Comunidade Global de Engenharia de Servidores de Herança: <strong style="color:var(--r)">r/X99_Lab</strong></li>
1573      <li>Discussões de Otimização e Rimes de Compilação GGUF: <strong style="color:#fff">Github: ggerganov/llama.cpp/discussions</strong></li>
1574    </ul>
1575  </div>`},contexto:{id:"contexto",title:"01. CONTEXTO E PROBLEMA",desc:"Em 2026, a narrativa padrão era clara: RX 580 não roda IA. Análise dos ecossistemas travados e viabilidade de aceleração via Vulkan.",meta:{version:"1.0.0",type:"artigo",category:"introducao",subcategory:"visao-geral",difficulty:"beginner",estimated_time_min:10,tags:["obsolescencia-programada","hardware-livre","preservacao-silicio"],last_reviewed:"2026-07-01",relationships:{prerequisites:[],previous:"",next:"hardware",related:["solucao","jornada"]}},html:`<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.25rem">Em 2026, a narrativa padrão era clara: <strong style="color:#fff">RX 580 não roda IA</strong>. Os ecossistemas disponíveis eram:</p>
1576  <div class="tbl"><table>
1577    <thead><tr><th>Solução</th><th>Status para RX 580</th></tr></thead>
1578    <tbody>
1579      <tr><td>CUDA (Nvidia)</td><td>❌ Exclusivo Nvidia</td></tr>
1580      <tr><td>ROCm (AMD oficial)</td><td>❌ Removeu suporte a Polaris/GCN4 na versão 5.x</td></tr>
1581      <tr><td>DirectML (Microsoft)</td><td>❌ Abandonado antes de amadurecer — instável, lento</td></tr>
1582      <tr><td>OpenVINO (Intel)</td><td>❌ Incompatível com Forge no Windows</td></tr>
1583    </tbody>
1584  </table></div>
1585  <div class="tbl" style="margin-top:.9rem"><table>
1586    <thead><tr><th>Item</th><th>Status (antes da solução)</th></tr></thead>
1587    <tbody>
1588      <tr><td>LLM</td><td>CPU pura — 25 a 40 minutos por resposta</td></tr>
1589      <tr><td>Geração de imagens</td><td>ComfyUI via CPU — 50+ minutos por imagem no HDD</td></tr>
1590      <tr><td>RX 580</td><td>Ociosa, 0% de uso para IA</td></tr>
1591      <tr><td>Storage</td><td>HDD lento (gargalo crítico de I/O)</td></tr>
1592    </tbody>
1593  </table></div>
1594  <div class="card acc" style="margin-top:1.25rem">
1595    <blockquote style="font-size:.88rem;color:#94a3b8;font-style:italic;line-height:1.9;border-left:3px solid var(--r);padding-left:.9rem">"Polaris é obsoleta. Só serve pra mineração. AMD antiga não roda IA. Troca de placa."</blockquote>
1596    <p style="margin-top:.9rem;font-size:.88rem;color:#e2e8f0;font-weight:600">Essa crença era falsa. O poder computacional sempre esteve lá. O problema era o software, não o hardware.</p>
1597  </div>
1598  <p style="margin-top:1.25rem;font-size:.88rem;color:#94a3b8;line-height:1.9">O projeto <code style="color:var(--r)">ggml</code> (base do <code style="color:var(--r)">llama.cpp</code> and <code style="color:var(--r)">stable-diffusion.cpp</code>) usa Vulkan como backend de GPU — padrão aberto que funciona em qualquer GPU moderna. A RX 580 suporta Vulkan 1.x desde os drivers originais de 2017. ROCm e CUDA são desnecessários.</p>
1599  <p style="margin-top:.9rem;font-size:.88rem;color:#94a3b8;line-height:1.9">Para modelos maiores (FLUX.1 Schnell 16GB, por exemplo), a solução é rodar via <strong style="color:#fff">CPU Xeon + RAM ECC abundante</strong> através do ComfyUI no WSL2, com link simbólico para os modelos no Windows — sem duplicar arquivos, sem perder espaço.</p>`},cpu:{id:"cpu",title:"24. PARAMETRIZAÇÃO E AJUSTES DO XEON",desc:"Configurações de BIOS e limites estruturais de instruções fp32 para o processador LGA 2011-3.",meta:{version:"1.0.0",type:"documento",category:"hardware",subcategory:"processadores",difficulty:"intermediate",estimated_time_min:18,tags:["bios-tuning","avx-optimization","intel-xeon-tuning"],last_reviewed:"2026-07-01",relationships:{prerequisites:["benchmarks"],previous:"benchmarks",next:"troubleshooting",related:["hardware","benchmarks"]}},html:`<div class="card acc">
1600    <p style="font-size:.88rem;color:#e2e8f0;font-weight:600;line-height:1.6">Diretrizes Técnicas para Processadores Xeon de Microarquitetura Haswell (v3):</p>
1601    <p style="margin-top:.5rem;font-size:.85rem;color:#94a3b8;line-height:1.9">O processador Intel Xeon E5-2690 v3 lançado em 2014 possui suporte nativo ao conjunto de instruções vetoriais <strong style="color:#fff">AVX2</strong>. No entanto, ele não possui hardware dedicado para cálculos matemáticos de precisão reduzida em <strong style="color:var(--r)">FP16 (Half-Precision)</strong>.</p>
1602    <p style="margin-top:.5rem;font-size:.85rem;color:#94a3b8;line-height:1.9">Ao instanciar os loaders de modelos dentro do ComfyUI no Linux, certifique-se de configurar a precisão de cálculo para <strong style="color:#22c55e">FP32 (Single-Precision)</strong>. Caso force a execução em FP16, o processador precisará emular as instruções via software, reduzindo a velocidade de processamento pela metade.</p>
1603  </div>`},dadhacks:{id:"dadhacks",title:"32. MÉTODO DADHACKS — AI IMAGE GENERATION ON RX 580 USING VULKAN: A COST-EFFECTIVE SOLUTION",desc:"Análise da consolidação experimental realizada por DH (DadHacks) em dezembro de 2025, estabelecendo caminhos de compatibilidade para geração de imagens na GPU legado.",meta:{version:"1.0.0",type:"estudo_caso",category:"estudo-caso",subcategory:"metodo-historico",difficulty:"intermediate",estimated_time_min:20,tags:["dadhacks-method","estetica-baixo-custo","vulkan-image"],last_reviewed:"2026-07-01",relationships:{prerequisites:["amihart"],previous:"amihart",next:"codacus",related:["amihart","codacus"]}},html:`<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.25rem">Em 05 de dezembro de 2025, o desenvolvedor <strong style="color:#fff">DH (DadHacks)</strong> publicou um guia técnico inovador no site <a href="https://dadhacks.org/2025/12/05/ai-image-generation-on-rx-580-using-vulkan-a-cost-effective-solution/" target="_blank" rel="noopener noreferrer" style="color:var(--r);text-decoration:underline">dadhacks.org</a> demonstrando que a GPU AMD RX 580 de 8GB é perfeitamente viável para geração de IA moderna sem depender de soluções de softw

1603are proprietárias ou do ROCm.</p>
1604
1605  <div class="card" style="border-left:3px solid var(--r);padding:1rem;border-radius:4px;background:rgba(255,255,255,0.01);border:1px solid var(--b);margin-bottom:1.25rem">
1606    <strong style="color:#fff;display:block;margin-bottom:.4rem">📌 Resultados da Validação Técnica:</strong>
1607    <p style="font-size:.82rem;color:#94a3b8;line-height:1.7;margin:0">O estudo de DadHacks demonstrou de forma experimental que é viável executar o Stable Diffusion via Vulkan, refinando a conclusão preliminar de janeiro de 2025. A maturidade gradual do ecossistema do <code>stable-diffusion.cpp</code> ampliou as taxas de transferência até o fim de 2025.</p>
1608  </div>
1609
1610  <h4 style="color:#fff;font-family:'Syne',sans-serif;font-size:11px;letter-spacing:1px;margin-bottom:.75rem;text-transform:uppercase">A. Compilação do Motor — stable-diffusion.cpp</h4>
1611  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.8">DH detalhou o fluxo de compilação no Linux utilizando a flag compiladora nativa <code>-DSD_VULKAN=ON</code>:</p>
1612  <div class="code" style="margin-bottom:1.25rem"><span class="code-lang">bash</span><pre># Clonar de forma recursiva (obrigatório para trazer submódulos do ggml)
1613git clone --recursive https://github.com/leejet/stable-diffusion.cpp
1614cd stable-diffusion.cpp && mkdir build && cd build
1615

1616# Configurar CMake usando Vulkan ativado
1617cmake .. -DSD_VULKAN=ON
1618
1619# Compilar binários
1620cmake --build . --config Release</pre></div>
1621
1622  <p style="font-size:.82rem;color:#64748b;line-height:1.6;margin-bottom:1.25rem">🧬 <strong>Divergência de Flags Documentada:</strong> O DadHacks usou <code>-DSD_VULKAN=ON</code> (camada de aplicação), enquanto o AIVisionsLab homologou <code>-DGGML_VULKAN=ON</code> (camada de engine de tensores). Ambas geram o mesmo binário eficiente na prática, mas referenciam caminhos diferentes da evolução do repositório leejet.</p>
1623
1624  <h4 style="color:#fff;font-family:'Syne',sans-serif;font-size:11px;letter-spacing:1px;margin-bottom:.75rem;text-transform:uppercase">B. Comandos de Geração Originais (DadHacks)</h4>
1625  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.8">Demonstração original de chamada offline via terminal para Flux Schnell (modelo quantizado de 4-bits) e Flux Dev, usando segmentação de memória para poupar VRAM:</p>
1626  <div class="code" style="margin-bottom:1.25rem"><span class="code-lang">bash</span><pre># Executar Flux Schnell (4 steps de amostragem)
1627sd --diffusion-model SD-Models/flux1-schnell-q4_0.gguf \\
1628   --vae SD-Models/ae.safetensors \\
1629   --clip_l SD-Models/clip_l.safetensors \\
1630   --t5xxl SD-Models/t5xxl_fp16.safetensors \\
1631   -p "a lovely beagle holding a sign says 'hello'" \\
1632   --cfg-scale 1.0 \\
1633   --sampling-method euler \\
1634   -v --steps 4 \\
1635   --clip-on-cpu</pre></div>
1636
1637  <div class="card" style="border:1px solid rgba(239,68,68,0.2);background:rgba(239,68,68,0.02);padding:1rem;border-radius:4px;margin-bottom:1.25rem">
1638    <strong style="color:#ef4444;display:block;margin-bottom:.4rem">🚨 ALERTA CRÍTICO: Incompatibilidade GGUF (Descoberta AIVisionsLab)</strong>
1639    <p style="font-size:.82rem;color:#94a3b8;line-height:1.7;margin:0">O DadHacks não listou uma incompatibilidade de formato muito comum: pesos GGUF publicados pelo mantenedor <strong>city96</strong> são compatíveis <em>apenas</em> com o ComfyUI. Para execução no stable-diffusion.cpp / sd-server, é obrigatório utilizar os modelos publicados oficialmente pelo mantenedor <strong>leejet</strong> (ex: <code style="color:#fff">FLUX.1-schnell-gguf</code>).</p>
1640  </div>
1641
1642  <h4 style="color:#fff;font-family:'Syne',sans-serif;font-size:11px;letter-spacing:1px;margin:1.5rem 0 .75rem 0;text-transform:uppercase">C. O Que o DadHacks Não Cobriu (Que o AIVisionsLab Expandiu)</h4>
1643  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.8;margin-bottom:1rem">O guia original de DadHacks focou no ecossistema Linux/terminal com comandos manuais. O AIVisionsLab expandiu agressivamente essa base técnica:</p>
1644  <table style="width:100%;border-collapse:collapse;font-size:.78rem;color:#94a3b8;line-height:1.8;margin-bottom:1.25rem;border:1px solid var(--b)">
1645    <thead>
1646      <tr style="background:rgba(255,255,255,0.02);color:#fff;border-bottom:1px solid var(--b)">
1647        <th style="padding:.5rem;text-align:left;border-right:1px solid var(--b)">Funcionalidade</th>
1648        <th style="padding:.5rem;text-align:left;border-right:1px solid var(--b)">DadHacks (Linux)</th>
1649        <th style="padding:.5rem;text-align:left">AIVisionsLab (Windows Nativo)</th>
1650      </tr>
1651    </thead>
1652    <tbody>
1653      <tr style="border-bottom:1px solid var(--b)">
1654        <td style="padding:.5rem;border-right:1px solid var(--b);font-weight:bold;color:#fff">Hospedagem no Windows</td>
1655        <td style="padding:.5rem;border-right:1px solid var(--b)">❌ Não abordada</td>
1656        <td style="padding:.5rem;color:#22c55e">✅ Mapeamento via MSVC / Cmd executáveis</td>
1657      </tr>
1658      <tr style="border-bottom:1px solid var(--b)">
1659        <td style="padding:.5rem;border-right:1px solid var(--b);font-weight:bold;color:#fff">Integração Gráfica</td>
1660        <td style="padding:.5rem;border-right:1px solid var(--b)">❌ Apenas terminal CLI</td>
1661        <td style="padding:.5rem;color:#22c55e">✅ Servidor API + OpenWebUI / Docker link</td>
1662      </tr>
1663      <tr style="border-bottom:1px solid var(--b)">
1664        <td style="padding:.5rem;border-right:1px solid var(--b);font-weight:bold;color:#fff">Automação .bat</td>
1665        <td style="padding:.5rem;border-right:1px solid var(--b)">❌ Scripts manuais shell</td>
1666        <td style="padding:.5rem;color:#22c55e">✅ Scripts automáticos double-click no Desktop</td>
1667      </tr>
1668      <tr>
1669        <td style="padding:.5rem;border-right:1px solid var(--b);font-weight:bold;color:#fff">Segurança VRAM</td>
1670        <td style="padding:.5rem;border-right:1px solid var(--b)">⚠️ Limitação a base CLI</td>
1671        <td style="padding:.5rem;color:#22c55e">✅ Otimizações com VAE Tiling contra bugs OOM</td>
1672      </tr>
1673    </tbody>
1674  </table>
1675
1676  <div class="card" style="border:1px dashed rgba(255,255,255,0.08);background:rgba(255,255,255,0.01);padding:1rem;border-radius:4px;margin-top:1.25rem">
1677    <strong style="color:var(--r)">Crédito Comunitário Extensivo:</strong>

1677 Seção documental baseada na publicação original de <a href="https://dadhacks.org/2025/12/05/ai-image-generation-on-rx-580-using-vulkan-a-cost-effective-solution/" target="_blank" rel="noopener noreferrer" style="color:var(--r);text-decoration:underline;font-weight:bold">DH (DadHacks)</a>, intitulada <em>"AI Image Generation on RX 580 Using Vulkan"</em>. O conhecimento é livre e evolutivo.
1678  </div>`},falhas:{id:"falhas",title:"03. O QUE NÃO FUNCIONOU (O CEMITÉRIO TÉCNICO)",desc:"Análise detalhada de erros: os motivos técnicos da rejeição do DirectML nativo, OpenVINO e ROCm.",meta:{version:"1.0.0",type:"estudo_caso",category:"troubleshooting",subcategory:"historico",difficulty:"intermediate",estimated_time_min:15,tags:["directml-fail","rocm-amd","openvino-intel"],last_reviewed:"2026-07-01",relationships:{prerequisites:["hardware"],previous:"hardware",next:"solucao",related:["troubleshooting","comfyui-directml"]}},html:`<div class="err">
1679    <div class="err-t">3.1 DirectML + ComfyUI Windows Nativo — ABANDONADO</div>
1680    <p><strong style="color:#e2e8f0">Tentativas:</strong> Invocação via <code>torch-directml</code> e flag <code>--directml</code>. O ecossistema foi detectado como <code>privateuseone0</code>, mas revelou instabilidade crônica devido à falta de atualizações estruturais da Microsoft.</p>
1681    <p style="margin-top:.45rem"><strong style="color:#e2e8f0">Logs de Erro Críticos:</strong></p>
1682    <div class="code"><span class="code-lang">stderr</span><pre>WARNING: torch-directml barely works, is very slow, has not been updated in over 1 year and might be removed soon, please don't use it.
1683
1684OSError: [WinError 127] Não foi possível encontrar o procedimento especificado
1685# Causado por um mismatch de DLL entre o torchaudio e a build de CPU do torch 2.4.1
1686
1687NotImplementedError: Cannot access storage of OpaqueTensorImpl
1688# CLIPTextEncode quebra ao tentar manipular tensores opacos gerados pelo DirectML</pre></div>
1689    <p style="margin-top:.45rem"><strong style="color:#e2e8f0">Causa Raiz:</strong> O DirectML gera tensores opacos incapazes de se comunicar com os backends de atenção modernos do ComfyUI. Além disso, o instalador força a importação do <code>torchaudio</code> dentro do módulo <code>audio_vae.py</code>, quebrando a inicialização mesmo que você não use áudio.</p>
1690  </div>
1691  <div class="err" style="margin-top:1rem">
1692    <div class="err-t">3.2 OpenVINO + Stable Diffusion Forge</div>
1693    <p><strong style="color:#e2e8f0">Tentativa:</strong> Acoplamento da extensão <code>sd-webui-openvino</code> desenvolvida pela Intel dentro do ecossistema Forge.</p>
1694    <div class="code"><span class="code-lang">stderr</span><pre>ModuleNotFoundError: No module named 'ldm'
1695ModuleNotFoundError: No module named 'sgm'
1696Error build_unet: Invalid backend: 'openvino'</pre></div>
1697    <p style="margin-top:.45rem"><strong style="color:#e2e8f0">Causa Raiz:</strong> A extensão foi desenhada para a arquitetura antiga do Automatic1111. Como o Forge reestruturou completamente o código e substituiu os módulos nativos de <code>ldm</code> e <code>sgm</code>, a injeção do backend OpenVINO falha por completo.</p>
1698  </div>
1699  <div class="err" style="margin-top:1rem">
1700    <div class="err-t">3.3 ROCm — Inviável por Design em Polaris</div>
1701    <ul>
1702      <li>A AMD encerrou oficialmente o suporte à arquitetura GCN4 (Polaris/RX 580) nas branches modernas do ROCm (v5.x+).</li>
1703      <li>Inexistência de suporte oficial para ROCm nativo em ambiente Windows.</li>
1704      <li>Camadas de compatibilidade via WSL2 para placas antigas geram Kernel Panics constantes em inferências de alta carga.</li>
1705    </ul>
1706  </div>
1707  <div class="err" style="margin-top:1rem">
1708    <div class="err-t">3.4 WebUI Forge em CPU Pura (HDD)</div>
1709    <p><strong style="color:#e2e8f0">Métricas:</strong> Startup time de 85 segundos. Tempo de renderização insustentável de ~19 minutos por imagem simples (512x512, 20 steps). O gargalo do HDD em concorrência com o gerenciamento de paginação do <code>memory_management.py</code> tornou o setup impraticável.</p>
1710  </div>`},"flux-vulkan":{id:"flux-vulkan",title:"25. EXPLORAÇÃO COMPLEMENTAR: FLUX VIA VULKAN",desc:"Aceleração alternativa de modelos Flux utilizando quantizações ultra-compactas em C++.",meta:{version:"1.0.0",type:"documento",category:"software",subcategory:"compilacao",difficulty:"advanced",estimated_time_min:25,tags:["flux-vulkan","cpp-native-inference","amd-acceleration"],last_reviewed:"2026-07-01",relationships:{prerequisites:["troubleshooting"],previous:"troubleshooting",next:"troubleshoot-comfyui",related:["flux_server","sdxl_vulkan"]}},html:`<div class="card">
1711    <p style="font-size:.88rem;color:#94a3b8;line-height:1.9">Embora o checkpoint nativo do FLUX.1 de 16GB exija o processamento robusto da CPU Xeon, é totalmente viável realizar inferências diretamente na GPU RX 580 utilizando o binário do <code>stable-diffusion.cpp</code> acoplado com quantizações agressivas de 3-bits:</p>
1712    <div class="note info" style="margin-top:.5rem">Peso Recomendado para Testes VRAM: <strong>flux1-schnell-Q3_K_S.gguf (~5.2GB)</strong>. Esta variação se acomoda com folga dentro do limite físico de 8GB da placa Polaris, entregando gerações SOTA aceleradas por hardware.</div>
1713  </div>`},flux_server:{id:"flux_server",title:"11. RODAR FLUX NO SD-SERVER (GPU + CPU HÍBRIDO)",desc:"Mapeamento e inicialização do ecossistema Flux de 12B parâmetros com arquitetura de memória segmentada.",meta:{version:"1.0.0",type:"documento",category:"software",subcategory:"servidor",difficulty:"advanced",estimated_time_min:30,tags:["flux-schnell","servidor-imagem","gerenciamento-memoria"],last_reviewed:"2026-07-01",relationships:{prerequisites:["sdserver"],previous:"sdserver",next:"sd_bat_automation",related:["comfyui-flux","flux-vulkan"]}},html:`<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.25rem">O Flux exige segmentação precisa entre VRAM e RAM host. Utilizando as flags <code>--vae-on-cpu</code> e <code>--vae-tiling</code>, evitamos o estouro de memória (OOM) na RX 580 2048SP. O T5XXL (fp16) e o VAE residem na RAM; o modelo de difusão ocupa a VRAM.</p>
1714  <div class="tbl">
1715    <table>
1716      <thead>
1717        <tr><th>Componente</th><th>Arquivo Homologado</th><th>Alocação Final</th><th>Tamanho</th></tr>
1718      </thead>
1719      <tbody>
1720        <tr><td>Diffusion Model</td><td><code>flux1-schnell-q4_k.gguf</code></td><td><strong>GPU (VRAM)</strong></td><td>~6.5 GB</td></tr>
1721        <tr><td>VAE</td><td><code>ae.safetensors</code></td><td><strong>CPU (RAM)</strong></td><td>~160 MB</td></tr>
1722        <tr><td>CLIP L</td><td><code>clip_l.safetensors</code></td><td><strong>GPU (VRAM)</strong></td><td>~235 MB</td></tr>
1723        <tr><td>T5XXL</td><td><code>t5xxl_fp16.safetensors</code></td><td><strong>CPU (RAM)</strong></td><td>~9.3 GB</td></tr>
1724      </tbody>
1725    </table>
1726  </div>
1727  <p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-top:1.25rem">⚠️ <strong style="color:#fff">Notas de Estabilidade:</strong> A remoção da flag <code>--timeout</code> (não suportada pela versão atual) e a inclusão de <code>--vae-tiling</code> eliminaram os erros de <em>DeviceMemoryAllocation</em>. O uso de RAM total chega a ~9.5 GB (T5XXL fp16). Para economizar RAM, substituir por <code>t5xxl_fp8</code> reduz para ~5 GB.</p>
1728  <p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:.5rem"><strong style="color:#fff">Comando de Produção (iniciar.bat):</strong></p>
1729  <pre style="font-family:'JetBrains Mono',monospace;font-size:11px">sd-server.exe --listen-ip 0.0.0.0 --listen-port 7860 ^
1730  --diffusion-model "E:\\ia_storage\\models\\Stable-diffusion\\flux1-schnell-q4_k.gguf" ^
1731  --vae "E:\\ia_storage\\models\\Stable-diffusion\\ae.safetensors" ^
1732  --clip_l "E:\\ia_storage\\models\\Stable-diffusion\\clip_l.safetensors" ^
1733  --t5xxl "E:\\ia_storage\\models\\Stable-diffusion\\t5xxl_fp16.safetensors" ^
1734  --cfg-scale 1.0 --steps 4 --clip-on-cpu --vae-on-cpu --vae-tiling</pre>
1735  <div class="tbl" style="margin-top:1.25rem; margin-bottom:1.25rem">
1736    <table>
1737      <thead>
1738        <tr><th>Etapa</th><th>Tempo Real</th></tr>
1739      </thead>
1740      <tbody>
1741        <tr><td>Condicionamento (T5XXL)</td><td>11.49s</td></tr>
1742        <tr><td>Sampling — 4 steps @ 1024x1024</td><td>~838s (~14 min)</td></tr>
1743        <tr><td>VAE Decode — 9 tiles</td><td>40.45s</td></tr>
1744        <tr><td><strong>Total por imagem</strong></td><td><strong>~838s (~14 min)</strong></td></tr>
1745      </tbody>
1746    </table>
1747  </div>
1748  <div class="card" style="margin-bottom:1.25rem; background: rgba(34, 197, 94, 0.1); border-left: 4px solid #22c55e;">
1749    <span style="color:#22c55e; font-weight:bold">✅ STATUS: SERVIDOR EM PRODUÇÃO — IMAGEM GERADA COM SUCESSO</span><br>
1750    <span style="font-size:.8rem; color:#94a3b8">Listening on http://0.0.0.0:7860 | VRAM: 7.6/8.0 GB | RAM: ~9.5 GB | GPU: RX 580 2048SP | Temp: 66°C</span>
1751  </div>`}

1751,guia:{id:"guia",title:"22. GUIA ESTRATÉGICO POR CASO DE USO",desc:"Matriz de tomada de decisão: quando direcionar o processamento para a GPU ou para o Xeon.",meta:{version:"1.0.0",type:"artigo",category:"introducao",subcategory:"visao-geral",difficulty:"beginner",estimated_time_min:12,tags:["decision-making","workflows","hardware-mapping"],last_reviewed:"2026-07-01",relationships:{prerequisites:["scripts"],previous:"scripts",next:"benchmarks",related:["contexto","benchmarks"]}},html:`<div class="tbl"><table>
1752    <thead><tr><th>Objetivo do Workflow</th><th>Modelo Recomendado</th><th>Backend Alocado</th><th>Métrica Real de Geração</th></tr></thead>
1753    <tbody>
1754      <tr class="hig"><td>Chat de Texto e Assistência Local</td><td>Mistral 7B / Llama 3 8B GGUF</td><td>GPU RX 580 (Vulkan)</td><td>Alta Performance (15-16 tokens/s) ⚡</td></tr>
1755      <tr class="hig"><td>Geração de Imagens Veloz (512x512)</td><td>DreamShaper 8 (SD 1.5 GGUF)</td><td>GPU RX 580 (Vulkan)</td><td>Renderização Fluida (~72 segundos)</td></tr>
1756      <tr><td>Criação de Alta Resolução e Detalhe SOTA</td><td>FLUX.1 Schnell (16GB Weights)</td><td>CPU Xeon (WSL2)</td><td>Processamento Pesado (~24 minutos)</td></tr>
1757      <tr><td>Interpolação de Quadros e Animação Video</td><td>AnimateDiff Pipeline Standard</td><td>CPU Xeon (WSL2)</td><td>Processamento Estável (~141 segundos)</td></tr>
1758    </tbody>
1759  </table></div>`},hardware:{id:"hardware",title:"02. HARDWARE DO LABORATÓRIO",desc:"Configuração Master detalhada: Xeon E5-2690 v3, RX 580 8GB, 32GB RAM ECC e o impacto crítico do NVMe.",meta:{version:"1.0.0",type:"documento",category:"hardware",subcategory:"especificacoes",difficulty:"beginner",estimated_time_min:12,tags:["amd-polar","rx-580","intel-xeon","ram-ecc"],last_reviewed:"2026-07-01",relationships:{prerequisites:["contexto"],previous:"contexto",next:"falhas",related:["cpu","benchmarks"]}},html:`<div style="display:grid;grid-template-columns:repeat(auto-fit,minmax(170px,1fr));gap:.85rem;margin-bottom:1.25rem">
1760    <div class="stat"><div class="stat-icon">🖥️</div><div><div class="stat-lbl">GPU</div><div class="stat-val" style="font-size:.82rem">RX 580 2048SP</div><div style="font-size:.65rem;color:#475569;margin-top:.15rem">8GB GDDR5 VRAM</div></div></div>
1761    <div class="stat"><div class="stat-icon">⚙️</div><div><div class="stat-lbl">CPU</div><div class="stat-val" style="font-size:.82rem">Xeon E5-2690 v3</div><div style="font-size:.65rem;color:#475569;margin-top:.15rem">12c/24t · 3.5GHz boost</div></div></div>
1762    <div class="stat"><div class="stat-icon">💾</div><div><div class="stat-lbl">RAM</div><div class="stat-val" style="font-size:.82rem">32GB DDR4 REG ECC</div><div style="font-size:.65rem;color:#475569;margin-top:.15rem">Quad Channel RDIMM</div></div></div>
1763    <div class="stat"><div class="stat-icon">📀</div><div><div class="stat-lbl">Storage</div><div class="stat-val" style="font-size:.82rem">NVMe E: 1TB</div><div style="font-size:.65rem;color:#475569;margin-top:.15rem">1.7–3.5 GB/s leitura</div></div></div>
1764    <div class="stat"><div class="stat-icon">🖥️</div><div><div class="stat-lbl">OS</div><div class="stat-val" style="font-size:.82rem">Windows 10 Pro</div><div style="font-size:.65rem;color:#475569;margin-top:.15rem">WSL2 Ubuntu 22.04.5</div></div></div>
1765    <div class="stat"><div class="stat-icon">🔧</div><div><div class="stat-lbl">Driver AMD</div><div class="stat-val" style="font-size:.82rem">31.0.21924.61</div><div style="font-size:.65rem;color:#475569;margin-top:.15rem">Amdnolk (11/12/2025)</div></div></div>
1766  </div>
1767  <div class="tbl"><table>
1768    <thead><tr><th>Componente</th><th>Especificação no Ambiente</th></tr></thead>
1769    <tbody>
1770      <tr><td>Placa-mãe</td><td>Machinist MR9A Pro (Chipset X99, LGA 2011-3)</td></tr>
1771      <tr><td>Storage Secundário</td><td>HDD C: (Gargalo de I/O — modelos mantidos no NVMe E:)</td></tr>
1772      <tr><td>Kernel WSL2</td><td>6.6.87.2-microsoft-standard-WSL2</td></tr>
1773      <tr><td>Vulkan SDK</td><td>1.4.341.1</td></tr>
1774      <tr><td>CMake</td><td>4.3.2</td></tr>
1775      <tr><td>Visual Studio</td><td>2022/2026 Community (MSVC 19.50)</td></tr>
1776      <tr><td>Python (WSL2)</td><td>3.11.15</td></tr>
1777      <tr><td>PyTorch (WSL2)</td><td>2.4.1+cu121</td></tr>
1778      <tr><td>ComfyUI Versões</td><td>0.19.3 (WSL2 CPU) | 0.21.0 (Windows DirectML)</td></tr>
1779    </tbody>
1780  </table></div>
1781  <div class="card" style="margin-top:.9rem">
1782    <p style="font-size:.85rem;color:#64748b;line-height:1.9"><strong style="color:#fff">RX 580 2048SP:</strong> Variante com 2048 Shader Processors (vs 2304SP original). Placa extremamente comum de mineração que performa de forma impecável via Vulkan.</p>
1783    <p style="font-size:.85rem;color:#64748b;line-height:1.9;margin-top:.65rem"><strong style="color:#fff">NVMe vs HDD:</strong> A mudança para o NVMe reduziu o carregamento do LLM de 25 minutos para 4 minutos. No caso do FLUX (16GB), o carregamento caiu de 25 minutos no HDD para apenas ~30 segundos no NVMe. <strong style="color:var(--r)">O storage é tão crítico quanto a capacidade de processamento.</strong></p>
1784    <p style="font-size:.85rem;color:#64748b;line-height:1.9;margin-top:.65rem"><strong style="color:#fff">32GB REG ECC:</strong> Operando com o FLUX.1 Schnell FP8, o sistema consome 94% da memória disponível de forma contínua. A RAM de servidor ECC mantém estabilidade absoluta e evita corrupção de tensores s

1784ob carga extrema.</p>
1785  </div>`},jornada:{id:"jornada",title:"06. JORNADA COMPLETA — AS 5 FASES",desc:"A linha do tempo do laboratório: da lentidão inicial da CPU ao controle total do hardware de herança.",meta:{version:"1.0.0",type:"artigo",category:"introducao",subcategory:"linha-tempo",difficulty:"beginner",estimated_time_min:10,tags:["linha-tempo","conquistas","roadmap"],last_reviewed:"2026-07-01",relationships:{prerequisites:["prereqs"],previous:"prereqs",next:"llama",related:["linhatempo","proximos"]}},html:`<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.75rem">O sucesso técnico do ecossistema local foi alcançado através de 5 marcos evolutivos e decisões estratégicas de hardware:</p>
1786  <div class="phase">
1787    <div class="phase-num">01</div>
1788    <div class="phase-body">
1789      <div class="phase-hdr"><span class="phase-title">WebUI Forge em CPU Pura (HDD)</span><span class="lbl lbl-b">BASELINE INICIAL</span></div>
1790      <p style="font-size:.85rem;color:#64748b;line-height:1.9">Primeira infraestrutura funcional. Boot demorado de 85 segundos e tempos de renderização batendo ~19 minutos por imagem. Sistema completamente inviável para uso produtivo.</p>
1791    </div>
1792  </div>
1793  <div class="phase">
1794    <div class="phase-num">02</div>
1795    <div class="phase-body">
1796      <div class="phase-hdr"><span class="phase-title">Aceleração de LLM via Vulkan (RX 580)</span><span class="lbl lbl-r">BREAKTHROUGH TÉCNICO</span></div>
1797      <p style="font-size:.85rem;color:#64748b;line-height:1.9">Compilação bem-sucedida do llama.cpp ativando a flag <code>-DGGML_VULKAN=ON</code>. O motor passou a identificar a placa de vídeo Polaris nativamente, saltando a taxa de inferência de 3-5 t/s para expressivos 15-16 t/s.</p>
1798    </div>
1799  </div>
1800  <div class="phase">
1801    <div class="phase-num">03</div>
1802    <div class="phase-body">
1803      <div class="phase-hdr"><span class="phase-title">Migração de Armazenamento Centralizado para NVMe</span><span class="lbl lbl-y">MARCO DECISIVO</span></div>
1804      <p style="font-size:.85rem;color:#64748b;line-height:1.9">Substituição do HDD mecânico pelo drive NVMe de alta velocidade (1.7–3.5 GB/s). O tempo de carga na memória do modelo de linguagem caiu de 25 minutos para apenas 4 minutos, eliminando o principal gargalo de I/O do laboratório.</p>
1805    </div>
1806  </div>
1807  <div class="phase">
1808    <div class="phase-num">04</div>
1809    <div class="phase-body">
1810      <div class="phase-hdr"><span class="phase-title">stable-diffusion.cpp nativo via Vulkan</span><span class="lbl lbl-g">OBJETIVO GPU CONCLUÍDO ✅</span></div>
1811      <p style="font-size:.85rem;color:#64748b;line-height:1.9">Primeira imagem renderizada localmente utilizando 100% da GPU RX 580 no ambiente Windows. Ciclo de geração do modelo de pesos quânticos SD 1.5 GGUF estabilizado na marca de ~72 segundos.</p>
1812    </div>
1813  </div>
1814  <div class="phase">
1815    <div class="phase-num">05</div>
1816    <div class="phase-body">
1817      <div class="phase-hdr"><span class="phase-title">Escalonamento do FLUX.1 Schnell via Xeon + RAM ECC</span><span class="lbl lbl-b">EXPANSÃO DE CAPACIDADE ✅</span></div>
1818      <p style="font-size:.85rem;color:#64748b;line-height:1.9">Implementação estável do modelo SOTA de 16GB rodando no processador de servidor. Prova empírica de que uma infraestrutura corporativa de 2014 consegue processar de forma robusta inteligências artificiais de última geração de 2026.</p>
1819    </div>
1820  </div>`},limit_qwen_35b:{id:"limit_qwen_35b",title:"41. LEVANDO AO LIMITE QWEN3.5 35B Q6_K NA RX 580 8GB VIA VULKAN",desc:"Experimento de execução híbrida extrema de um modelo de 34.66B de parâmetros na GPU Polaris e Xeon de 2014.",meta:{version:"1.0.0",type:"estudo_caso",category:"estudo-caso",subcategory:"pesquisa-extrema",difficulty:"advanced",estimated_time_min:45,tags:["qwen-35b","offloading-extremo","vram-spillover"],last_reviewed:"2026-07-01",relationships:{prerequisites:["audio_srt_ptbr"],previous:"audio_srt_ptbr",next:"proving_hypothesis_35b",related:["proving_hypothesis_35b","llama"]},part:1}

1820,html:`<div class="whisper-doc">
1821  <div class="hero">
1822    <div class="hero-title"><span>Leve ao Limite</span> — Qwen3.5 35B na RX 580</div>
1823    <div class="hero-sub">Experimento de execução híbrida extrema de um modelo de <strong>34,66B parâmetros</strong> na GPU de 8GB de VRAM e Xeon de 2014 via motor Vulkan.</div>
1824    <div class="hero-badges">
1825      <span class="badge badge-gpu">AMD RX 580 8GB</span>
1826      <span class="badge badge-win">Xeon E5-2690 v3</span>
1827      <span class="badge badge-ok">Vulkan Híbrido</span>
1828      <span class="badge badge-ok">34.66B parameters</span>
1829      <span class="badge badge-win">Q6_K Quantization</span>
1830    </div>
1831  </div>
1832
1833  <div class="stat-grid">
1834    <div class="stat">
1835      <div class="stat-label">Velocidade Geração</div>
1836      <div class="stat-value">5.64 tok/s</div>
1837      <div class="stat-sub">Sessão 2 (Estável)</div>
1838    </div>
1839    <div class="stat">
1840      <div class="stat-label">Prompt Eval</div>
1841      <div class="stat-value">34.13 tok/s</div>
1842      <div class="stat-sub">Tempo resposta ~107s</div>
1843    </div>
1844    <div class="stat">
1845      <div class="stat-label">Temperatura Máxima</div>
1846      <div class="stat-value">80°C</div>
1847      <div class="stat-sub">Sem Throttling (Alvo: 90°C)</div>
1848    </div>
1849    <div class="stat">
1850      <div class="stat-label">Custo Adicional</div>
1851      <div class="stat-value">R$ 0,00</div>
1852      <div class="stat-sub">Execução 100% local</div>
1853    </div>
1854  </div>
1855
1856  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:2rem;margin-bottom:.5rem">33.0 — Contexto e Motivação</h3>
1857  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-bottom:1rem">
1858    Em 15 de junho de 2026, o AIVisionsLab conduziou o experimento mais extremo da sua história: rodar um modelo de <strong>34,66 bilhões de parâmetros</strong> em modo híbrido numa GPU AMD Radeon RX 580 2048SP de 2017 com apenas 8GB de VRAM.
1859  </p>
1860  <blockquote style="font-size:.82rem;color:#94a3b8;font-style:italic;line-height:1.7;border-left:3px solid var(--r);padding-left:.9rem;margin-bottom:1.25rem">
1861    "Se a RX 580 já roda Qwen3 4B a 35 tokens/s via Vulkan, até onde vai o limite?"
1862  </blockquote>
1863  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-bottom:1rem">
1864    A resposta documentada abaixo prova que o limite não é onde o mercado de hardware diz que é.
1865  </p>
1866
1867  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:2rem;margin-bottom:.5rem">33.1 — Hardware do Laboratório</h3>
1868  <div class="tbl">
1869    <table>
1870      <thead>
1871        <tr>
1872          <th>Componente</th>
1873          <th>Especificação</th>
1874        </tr>
1875      </thead>
1876      <tbody>
1877        <tr><td><strong>CPU</strong></td><td>Intel Xeon E5-2690 v3 — 12 cores / 24 threads · 3,05 GHz turbo (Lançado em 2014)</td></tr>
1878        <tr><td><strong>RAM</strong></td><td>31,8 GB DDR4 REG ECC (quad-channel)</td></tr>
1879        <tr><td><strong>GPU</strong></td><td>AMD Radeon RX 580 2048SP — 8 GB GDDR5 (Lançado em 2017)</td></tr>
1880        <tr><td><strong>Storage 1</strong></td><td>SSD NVMe (Disco E:) — modelos GGUF (Leitura de 1.7–3.5 GB/s)</td></tr>
1881        <tr><td><strong>Storage 2</strong></td><td>HDD (Disco C: / F:) — sistema Windows + swap</td></tr>
1882        <tr><td><strong>Storage 3</strong></td><td>HDD (Disco D:) — arquivos secundários</td></tr>
1883        <tr><td><strong>Driver AMD</strong></td><td>31.0.21925.1001 — lançado em 20/05/2026</td></tr>
1884        <tr><td><strong>DirectX</strong></td><td>12 (Feature Level 12.0)</td></tr>
1885        <tr><td><strong>Barramento</strong></td><td>PCI Express 3, dispositivo 0, função 0</td></tr>
1886        <tr><td><strong>Sistema</strong></td><td>Windows 10/11 com WSL2 ativo</td></tr>
1887      </tbody>
1888    </table>
1889  </div>
1890  <p style="font-size:0.8rem;color:#94a3b8;font-style:italic;line-height:1.8;margin-top:0.5rem">
1891    *Nota histórica: O Xeon E5-2690 v3 é de 2014. A RX 580 é de 2017. O Qwen3.5 é de 2025. Hardware de datacenter de 10 anos atrás processando IA de última geração.
1892  </p>
1893
1894  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:2rem;margin-bottom:.5rem">33.2 — Software e Versões</h3>
1895  <div class="tbl">
1896    <table>
1897      <thead>
1898        <tr>
1899          <th>Software</th>
1900          <th>Versão / Detalhe</th>
1901        </tr>
1902      </thead>
1903      <tbody>
1904        <tr><td><strong>llama.cpp</strong></td><td>build b9049-2496f9c14</td></tr>
1905        <tr><td><strong>Compilador</strong></td><td>MSVC 14.51.36231 (Visual Studio)</td></tr>
1906        <tr><td><strong>Vulkan SDK</strong></td><td>1.4.350.0</td></tr>
1907        <tr><td><strong>OpenWebUI</strong></td><td>v0.9.6</td></tr>
1908        <tr><td><strong>SearXNG</strong></td><td>via Docker — integrado ao OpenWebUI</td></tr>
1909        <tr><td><strong>

1909Backend GPU</strong></td><td>Vulkan — <em>sem CUDA, sem ROCm, sem DirectML</em></td></tr>
1910        <tr><td><strong>Docker Desktop</strong></td><td>Ativo — containers open-webui + searxng</td></tr>
1911      </tbody>
1912    </table>
1913  </div>
1914
1915  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:2rem;margin-bottom:.5rem">33.3 — O Modelo: Qwen3.5-35B-A3B-Uncensored Q6_K</h3>
1916  <h4 style="color:#fff;font-weight:600;font-size:0.9rem;margin-top:1rem;margin-bottom:0.5rem">33.3.1 — Especificações Técnicas</h4>
1917  <div class="tbl">
1918    <table>
1919      <thead>
1920        <tr>
1921          <th>Parâmetro</th>
1922          <th>Valor</th>
1923        </tr>
1924      </thead>
1925      <tbody>
1926        <tr><td><strong>Nome completo</strong></td><td>Qwen3.5-35B-A3B-Uncensored-HauhauCS-Aggressive-Q6_K</td></tr>
1927        <tr><td><strong>Arquitetura</strong></td><td>qwen35moe — Mixture of Experts (MoE)</td></tr>
1928        <tr><td><strong>Parâmetros totais</strong></td><td>34,66 bilhões</td></tr>
1929        <tr><td><strong>Tamanho do arquivo</strong></td><td>26,55 GiB (6,58 bits por peso)</td></tr>
1930        <tr><td><strong>Quantização</strong></td><td>Q6_K — 432 tensores q6_K + 301 tensores f32</td></tr>
1931        <tr><td><strong>Experts totais</strong></td><td>256 experts</td></tr>
1932        <tr><td><strong>Experts ativos por token</strong></td><td>8 de 256 (apenas 3,1% ativados)</td></tr>
1933        <tr><td><strong>Contexto máximo de treino</strong></td><td>262.144 tokens</td></tr>
1934        <tr><td><strong>Vocabulário</strong></td><td>248.320 tokens (BPE, tokenizer qwen35)</td></tr>
1935        <tr><td><strong>Thinking mode padrão</strong></td><td>ATIVADO (<code>thinking = 1</code> no template Jinja2)</td></tr>
1936        <tr><td><strong>Intervalo de full attention</strong></td><td>A cada 4 layers</td></tr>
1937      </tbody>
1938    </table>
1939  </div>
1940
1941  <h4 style="color:#fff;font-weight:600;font-size:0.9rem;margin-top:1rem;margin-bottom:0.5rem">33.3.2 — Por que MoE muda tudo</h4>
1942  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-bottom:1rem">
1943    Em um modelo denso de 35B, <strong>todos</strong> os 35 bilhões de parâmetros são ativados para cada token gerado. No Qwen3.5 MoE, apenas os <strong>8 experts mais relevantes</strong> de 256 são ativados — o equivalente a ~3B parâmetros ativos por token.
1944  </p>
1945  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-bottom:1rem">
1946    Isso explica por que um arquivo de 26GB consegue rodar numa máquina com 32GB de RAM: a maior parte dos pesos está na memória mas <strong>não é calculada simultaneamente</strong>.
1947  </p>
1948
1949  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:2rem;margin-bottom:.5rem">33.4 — Comando de Inicialização</h3>
1950  <div class="cmd">
1951# Sessão 1 (porta 8080 — erro corrigido depois)
1952.\\llama-server.exe -m "E:\\models\\Qwen3.5-35B-A3B-Uncensored-HauhauCS-Aggressive-Q6_K.gguf" --host 0.0.0.0 --p

1952ort 8080
1953
1954# Sessão 2 (porta 8081 — correta)
1955.\\llama-server.exe -m "E:\\models\\Qwen3.5-35B-A3B-Uncensored-HauhauCS-Aggressive-Q6_K.gguf" --host 0.0.0.0 --port 8081</div>
1956  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-top:0.5rem">
1957    <strong>Nenhuma flag manual de camadas (<code>-ngl</code>).</strong> O llama.cpp fez todo o fitting automático.
1958  </p>
1959<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:2rem;margin-bottom:.5rem">33.5 — O Fitting Automático: Como o llama.cpp dividiu 26GB em 1,15 segundos</h3>
1960  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-bottom:1rem">
1961    Este é o coração técnico do experimento. Sem nenhuma intervenção manual, o llama.cpp executou um algoritmo de otimização que analisou a VRAM disponível e distribuiu o modelo entre GPU e RAM de forma inteligente.
1962  </p>
1963
1964  <h4 style="color:#fff;font-weight:600;font-size:0.9rem;margin-top:1rem;margin-bottom:0.5rem">33.5.1 — Situação Inicial (Impossível carregar inteiro na GPU)</h4>
1965  <div class="cmd">
1966Memória necessária na GPU (modelo completo): 32.961 MiB
1967VRAM livre disponível:                        7.366 MiB
1968Déficit:                                     26.618 MiB — impossível da GPU conter tudo</div>
1969
1970  <h4 style="color:#fff;font-weight:600;font-size:0.9rem;margin-top:1rem;margin-bottom:0.5rem">33.5.2 — Sequência de Decisões do Fitting (1,15 segundos)</h4>
1971  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-bottom:1rem">
1972    <strong>Passo 1 — Redução de contexto:</strong>
1973  </p>
1974  <div class="cmd">Contexto original:  262.144 tokens ➔ Contexto reduzido automaticamente: 4.096 tokens (Economia de VRAM: 5.347 MiB)</div>
1975  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-bottom:1rem">
1976    <strong>Passo 2 — Mover todos os experts MoE para RAM:</strong>
1977  </p>
1978  <div class="cmd">Vulkan0 com as camadas densas residuais: 2.418 MiB (Sobra 3.924 MiB VRAM livre)
1979RAM host (experts MoE mapeados): 25.613 MiB</div>
1980  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-bottom:1rem">
1981    <strong>Passo 3 — Realocar camadas densas de volta para GPU (back-to-front):</strong>
1982  </p>
1983  <div class="cmd">41 camadas densas realocadas ➔ GPU. Uso resultante: 3.048 MiB | Livre: 4.318 MiB</div>
1984  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-bottom:1rem">
1985    <strong>Passo 4 — Filling front-to-back com overflow fracionado:</strong>
1986  </p>
1987  <div class="cmd">Resultado: 41 camadas (36 "overflowing") na GPU
1988Tipo de overflow: GATE (fracionado no gate layer)
1989Uso final GPU: 6.255 MiB | Livre: 1.111 MiB</div>
1990
1991  <h4 style="color:#fff;font-weight:600;font-size:0.9rem;margin-top:1rem;margin-bottom:0.5rem">33.5.3 — Distribuição Final dos Pesos</h4>
1992  <div class="tbl">
1993    <table>
1994      <thead>
1995        <tr>
1996          <th>Localização do Buffer</th>
1997          <th>Conteúdo Armazenado</th>
1998          <th>Tamanho Mobilizado</th>
1999        </tr>
2000      </thead>
2001      <tbody>
2002        <tr><td><strong>Vulkan0 (RX 580 8GB)</strong></td><td>41 camadas densas + output layer</td><td><strong>5.154 MiB</strong></td></tr>
2003        <tr><td><strong>CPU_Mapped (RAM via mmap)</strong></td><td>256 experts MoE</td><td><strong>26.784 MiB</strong></td></tr>
2004      </tbody>
2005    </table>
2006  </div>
2007
2008  <h4 style="color:#fff;font-weight:600;font-size:0.9rem;margin-top:1rem;margin-bottom:0.5rem">33.5.4 — Buffers Adicionais alocados na VRAM</h4>
2009  <div class="tbl">
2010    <table>
2011      <thead>
2012        <tr>
2013          <th>Buffer Tipo</th>
2014          <th>Tamanho Alocado</th>
2015          <th>Descrição Física</th>
2016        </tr>
2017      </thead>
2018      <tbody>
2019        <tr><td><strong>KV Cache (Vulkan0)</strong></td><td>80,00 MiB</td><td>4.096 cells, 10 layers</td></tr>
2020        <tr><td><strong>RS Buffer (Vulkan0)</strong></td><td>251,25 MiB</td><td>recorrente: 40 layers, 4 seqs</td></tr>
2021        <tr><td><strong>Compute Buffer (Vulkan0)</strong></td><td>770,02 MiB</td><td>Ativado durante grafos de computação</td></tr>
2022        <tr><td><strong>Host Compute Buffer</strong></td><td>16,02 MiB</td><td>RAM do sistema</td></tr>
2023        <tr><td><strong>Output Buffer (Vulkan_Host)</strong></td><td>3,79 MiB</td><td>Buffer de saída de tokens</td></tr>
2024      </tbody>
2025    </table>
2026  </div>
2027  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-top:0.5rem">
2028    <strong>Total efetivo na VRAM durante inferência: ~6,2–7,2 GB</strong> (77–90% dos 8GB físicos livres).
2029  </p>
2030
2031  <h4 style="color:#fff;font-weight:600;font-size:0.9rem;margin-top:1rem;margin-bottom:0.5rem">33.5.5 — Features habilitadas automaticamente</h4>
2032  <div class="cmd">
2033✅ Flash Attention (auto ➔ ENABLED)
2034✅ Fused Gated Delta Net — autoregressive
2035✅ Fused Gated Delta Net — chunked
2036✅ Prompt cache (limite: 8.192 MiB, ~89.715 MiB salvos em disco)
2037✅ Thinking mode (thinking = 1)
2038✅ 4 slots paralelos (n_seq_max = 4)
2039
2040Graph nodes: 3.729 | Graph splits: 106 (com bs=512), 74 (com bs=1)
2041Reserve: 137–183 ms</div>
2042
2043  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:2rem;margin-bottom:.5rem">33.6 — A Arquitetura de 4 Níveis de Memória</h3>
2044  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-bottom:1rem">
2045    Primeiro registro documentado de um modelo 35B usando <strong>4 camadas de memória simultaneamente</strong> para processamento numa RX 580 comum:
2046  </p>
2047  <div style="background:#141519;border:1px border-white/5;border-radius:6px;padding:1rem;font-family:'JetBrains Mono',monospace;font-size:0.8rem;line-height:1.7;color:#c0caf5;margin-bottom:1.5rem">
2048    <div>⚡ <strong style="color:#ff9e64">

2048NÍVEL 1 — VRAM RX 580 (8GB GDDR5):</strong> 5.154 MiB de camadas densas | Acesso: ~400 GB/s (GDDR5) | Latência: nanosegundos</div>
2049    <div style="margin-top:0.5rem">🧠 <strong style="color:#7aa2f7">NÍVEL 2 — RAM DDR4 ECC (32GB Sytem):</strong> 26.784 MiB de experts MoE via mmap | Acesso: ~51 GB/s (quad-channel) | Latência: dezenas de ns</div>
2050    <div style="margin-top:0.5rem">💾 <strong style="color:#22c55e">NÍVEL 3 — SSD NVMe (Disco E:):</strong> 26,55 GiB de arquivo .gguf fonte | Acesso: 1,7–3,5 GB/s | Usado durante carregamento inicial</div>
2051    <div style="margin-top:0.5rem">📁 <strong style="color:#e0af68">NÍVEL 4 — HDD (Disco C: Swap):</strong> Swap do Windows ativado quando a RAM esgota (>97% de uso) | Acesso: ~120–180 MB/s | Pico de 98–100% de uso na inferência</div>
2052  </div>
2053
2054  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:2rem;margin-bottom:.5rem">33.7 — Configuração do Contexto Carregado</h3>
2055  <div class="cmd">
2056n_ctx         = 4.096 tokens (reduzido de 262.144 pelo fitting)
2057n_batch       = 2.048
2058n_ubatch      = 512
2059n_seq_max     = 4 (slots paralelos)
2060flash_attn    = auto ➔ ENABLED
2061kv_unified    = true</div>
2062  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-top:0.5rem">
2063    <strong>Limitação crítica documentada:</strong> O contexto de 4.096 tokens é o principal gargalo do setup. O thinking mode (modo raciocínio) do Qwen3.5 consome 3.000+ tokens sozinho antes de gerar qualquer resposta visível — deixando menos de 1.000 tokens livres para a resposta final ao cliente.
2064  </p>
2065
2066  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:2rem;margin-bottom:.5rem">33.8 — Benchmark Confirmado em Duas Sessões</h3>
2067  <h4 style="color:#fff;font-weight:600;font-size:0.9rem;margin-top:1rem;margin-bottom:0.5rem">Velocidade de geração</h4>
2068  <div class="tbl">
2069    <table>
2070      <thead>
2071        <tr>
2072          <th>Sessão</th>
2073          <th>Horário aproximado</th>
2074          <th>Prompt Eval Speed</th>
2075          <th>Geração Speed</th>
2076          <th>Total Tokens</th>
2077          <th>Tempo Total</th>
2078        </tr>
2079      </thead>
2080      <tbody>
2081        <tr><td><strong>Sessão 1</strong></td><td>~17h39</td><td><strong>34,13 tok/s</strong></td><td><strong>5,57 tok/s</strong></td><td>1.377 tok</td><td>~107 segundos</td></tr>
2082        <tr><td><strong>Sessão 2</strong></td><td>~21h34</td><td>~40.00 tok/s</td><td><strong>5,64 tok/s</strong></td><td>2.929 tok</td><td>~533 segundos</td></tr>
2083      </tbody>
2084    </table>
2085  </div>
2086  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-top:0.5rem">
2087    <strong>Consistência notável:</strong> Manutenção na linha de ~5,6 tokens/segundo de geração independente da sessão estar sobrecarregada ou fria.
2088  </p>
2089<h4 style="color:#fff;font-weight:600;font-size:0.9rem;margin-top:1rem;margin-bottom:0.5rem">Registro de temperaturas registradas</h4>
2090  <div class="tbl">
2091    <table>
2092      <thead>
2093        <tr>
2094          <th>Momento do Teste</th>
2095          <th>GPU Temp (°C)</th>
2096        </tr>
2097      </thead>
2098      <tbody>
2099        <tr><td>Idle (modelo estacionado na VRAM)</td><td>33–41°C</td></tr>
2100        <tr><td>Inferência normal (sem Web Search ativo)</td><td>44–64°C</td></tr>
2101        <tr><td>Inferência carregada (com Web Search ativo)</td><td>70–75°C</td></tr>
2102        <tr><td><strong>Pico absoluto registrado</strong></td><td><strong>80°C</strong></td></tr>
2103        <tr><td>Após conclusão de resposta</td><td>71–73°C</td></tr>
2104      </tbody>
2105    </table>
2106  </div>
2107  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-top:0.5rem">
2108    <strong>Temperatura de thermal throttling da RX 580:</strong> ~90°C. <strong>Margem de segurança operacional perfeitamente mantida!</strong>
2109  </p>
2110
2111  <h4 style="color:#fff;font-weight:600;font-size:0.9rem;margin-top:1rem;margin-bottom:0.5rem">Utilização de recursos na inferência física</h4>
2112  <div class="tbl">
2113    <table>
2114      <thead>
2115        <tr>
2116          <th>Recurso de Hardware</th>
2117          <th>Idle</th>
2118          <th>Durante Inferência</th>
2119          <th>Pico Registrado</th>
2120        </tr>
2121      </thead>
2122      <tbody>
2123        <tr><td>VRAM dedicada</td><td>6,7 / 8,0 GB</td><td>6,8 – 7,2 / 8,0 GB</td><td>7,4 GB / 8,0 GB</td></tr>
2124        <tr><td>RAM do Sistema</td><td>~29,0 GB</td><td>30,7 – 31,1 GB</td><td>31,2 / 31,8 GB (98% utiliz.)</td></tr>
2125        <tr><td>CPU Xeon v3</td><td>3%</td><td>62 – 70%</td><td>70%</td></tr>
2126        <tr><td>GPU utilização 3D</td><td>0%</td><td>1 – 3%</td><td>3% (Carga no barramento PCIe)</td></tr>
2127        <tr><td>

2127HDD C: (Atividade swap)</td><td>0%</td><td>13 – 42%</td><td>98 – 100% (Limiar swap)</td></tr>
2128      </tbody>
2129    </table>
2130  </div>
2131
2132  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:2rem;margin-bottom:.5rem">33.9 — Os 5 Testes: Histórico Completo de Engenharia</h3>
2133  <blockquote style="font-size:.82rem;color:#94a3b8;line-height:1.7;border-left:3px solid var(--r);padding-left:.9rem;margin-bottom:1.25rem">
2134    <strong>Pergunta de entrada enviada em todos os testes:</strong><br>
2135    "Explique detalhadamente como funciona o mecanismo de atenção em transformers e por que o MoE é mais eficiente que modelos densos. Responda em português."
2136  </blockquote>
2137
2138  <div style="display:flex;flex-direction:column;gap:1rem;margin-bottom:1.5rem">
2139    <div style="border:1px solid rgba(255, 69, 0, 0.1);background:rgba(3, 0, 1, 0.6);border-radius:6px;padding:1rem">
2140      <h5 style="color:#fff;font-weight:bold;font-size:0.85rem">TESTE 1 — Sessão 1 · Raciocínio ON + Web Search ON + Geração Imagem ON</h5>
2141      <p style="font-size:0.8rem;color:#94a3b8;line-height:1.6;margin-top:0.25rem">
2142        <strong>Resultado:</strong> ❌ Erro de protocolo (reposta não entregue) | <strong>Log do erro:</strong> <code>operator (): got exception: {"error":"code":400,"message":"Assistant response prefill is incompatible with enable_thinking."}</code>
2143      </p>
2144      <p style="font-size:0.8rem;color:#94a3b8;line-height:1.6;margin-top:0.25rem">
2145        <strong>Análise:</strong> O modelo processou um pensamento por ~1 minuto recuperando 10 fontes do SearXNG, mas o OpenWebUI injetou um prefill incorreto incompatível com a flag de pensamento do Jinja2, forçando o cancelamento.
2146      </p>
2147    </div>
2148
2149    <div style="border:1px solid rgba(255, 69, 0, 0.1);background:rgba(3, 0, 1, 0.6);border-radius:6px;padding:1rem">
2150      <h5 style="color:#fff;font-weight:bold;font-size:0.85rem">TESTE 2 — Sessão 2 · Raciocínio ON + Web Search ON (30 pesquisas consecutivas)</h5>
2151      <p style="font-size:0.8rem;color:#94a3b8;line-height:1.6;margin-top:0.25rem">
2152        <strong>Resultado:</strong> ❌ Contexto esgotado | <strong>Log do erro:</strong> <code>stop processing: n_tokens = 3285, truncated = 1</code>
2153      </p>
2154      <p style="font-size:0.8rem;color:#94a3b8;line-height:1.6;margin-top:0.25rem">
2155        <strong>Análise:</strong> As 30 pesquisas do SearXNG injetaram milhares de tokens de contexto na memória. O thinking mode tentou alocar seu próprio buffer e estourou os 4.096 limitantes do setup híbrido, forçando corte prematuro.
2156      </p>
2157    </div>
2158
2159    <div style="border:1px solid rgba(255, 69, 0, 0.1);background:rgba(3, 0, 1, 0.6);border-radius:6px;padding:1rem">
2160      <h5 style="color:#fff;font-weight:bold;font-size:0.85rem">TESTE 3 — Sessão 2 · Raciocínio ON + Web Search ON (25 pesquisas, nova rodada acumulada)</h5>
2161      <p style="font-size:0.8rem;color:#94a3b8;line-height:1.6;margin-top:0.25rem">
2162        <strong>Resultado:</strong> ❌ Contexto esgotado | <strong>Log do erro:</strong> <code>stop processing: n_tokens = 4095, truncated = 1</code>
2163      </p>
2164      <p style="font-size:0.8rem;color:#94a3b8;line-height:1.6;margin-top:0.25rem">
2165        <strong>Análise:</strong> O OpenWebUI tentou reenviar acumulando o histórico de conversação anterior. O prompt cresceu tanto que atingiu o teto exato delimitado de hardware de 4.095 tokens no Fitting.
2166      </p>
2167    </div>
2168
2169    <div style="border:1px solid rgba(255, 69, 0, 0.1);background:rgba(3, 0, 1, 0.6);border-radius:6px;padding:1rem">
2170      <h5 style="color:#fff;font-weight:bold;font-size:0.85rem">TESTE 4 — Sessão 2 · Raciocínio ON + Web Search OFF</h5>
2171      <p style="font-size:0.8rem;color:#94a3b8;line-height:1.6;margin-top:0.25rem">
2172        <strong>Resultado:</strong> ❌ Timeout do Cliente | <strong>Log do erro:</strong> <code>stop: cancel task, id_task = 6887 (truncated = 0)</code>
2173      </p>
2174      <p style="font-size:0.8rem;color:#94a3b8;line-height:1.6;margin-top:0.25rem">
2175        <strong>Análise:</strong> Sem web-search o consumo térmico despencou para estáveis 51°C e uso de swap caiu. Mas o processamento de raciocínio interno demorou tempo suficiente na CPU Xeon de 2014 para que a UI disparasse seu timeout de segurança de chamadas.
2176      </p>
2177    </div>
2178
2179    <div style="border:1px solid rgba(34, 197, 94, 0.2);background:rgba(0, 3, 1, 0.6);border-radius:6px;padding:1rem">
2180      <h5 style="color:#22c55e;font-weight:bold;font-size:0.85rem">✅ TESTE 5 — Sessão 2 · Raciocínio ON + Web Search OFF + Prompt Curto (45 tokens)</h5>
2181      <p style="font-size:0.8rem;color:#c2ffd2;line-height:1.6;margin-top:0.25rem">
2182        <strong>Resultado:</strong> SUCESSO ABSOLUTO ✅ | Resposta técnica profunda entregue integralmente em português brasileiro estruturado.
2183      </p>
2184      <p style="font-size:0.8rem;color:#94a3b8;line-height:1.6;margin-top:0.25rem">
2185        <strong>Análise:</strong> Ao restringir o prompt inicial para 45 tokens e desativar barulhos complementares, o pensamento pôde rodar por 4 minutos completos sob o Xeon, realizando rascunho de equações e respondendo impecavelmente sobre Transformers (Softmax, Multiheads) e as eficiências do MoE.
2186      </p>
2187    </div>
2188  </div>
2189
2190  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:2rem;margin-bottom:.5rem">33.10 — Tabela Comparativa de Comportamento dos Testes</h3>
2191  <div class="tbl">
2192    <table>
2193      <thead>
2194        <tr>
2195          <th>ID</th>
2196          <th>Thinking</th>
2197          <th>Web Search</th>
2198          <th>Promo Token</th>
2199          <th>Status</th>
2200          <th>Causa Raiz Real da Falha</th>
2201        </tr>
2202      </thead>
2203      <tbody>
2204        <tr><td>1</td><td>ON</td><td>ON (10 fontes)</td><td>~936 tok</td><td>❌ Falha</td><td>Prefill de resposta incompatível with thinking=1</td></tr>
2205        <tr><td>2</td><td>ON</td><td>ON (30 pesq.)</td><td>~357 tok</td><td>❌ Falha</td><td>Esgotamento de contexto do Fitting (4.095 max)</td></tr>
2206        <tr><td>3</td><td>ON</td><td>ON (25 pesq.)</td><td>~3.385 tok</td><td>❌ Falha</td><td>Esgotamento de contexto imediato no reenvio</td></tr>
2207        <tr><td>4</td><td>ON</td><td>OFF</td><td>~350 tok</td><td>❌ Falha</td><td>Timeout nas requisições da OpenWebUI antes do output</td></tr>
2208        <tr style="background:rgba(34, 197, 94, 0.05)"><td><strong>5</strong></td><td><strong>ON</strong></td><td><strong>OFF</strong></td><td><strong>45 tok</strong></td><td><strong>✅ SUCESSO</strong></td><td><strong>Prompt enxuto coube integralmente na arquitetura de mmap</strong></td></tr>
2209      </tbody>
2210    </table>
2211  </div>
2212<p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-top:0.5rem">
2213    <strong>Conclusão Crítica:</strong> A RX 580 do laboratório de 2017 e o Xeon funcionaram estavelmente sem dar nenhum reset físico em todas as situações de stress. As falhas residiram estritamente na calibração lógica do software de front-end.
2214  </p>
2215
2216  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:2rem;margin-bottom:.5rem">33.11 — Coletas de Descobertas e Limitações Cruciais</h3>
2217  <ul style="padding-left:1.2rem;font-size:.82rem;color:#94a3b8;line-height:1.8;margin-bottom:1.25rem">
2218    <li><strong style="color:#fff">Incompatibilidade do Thinking com Contexto Curto:</strong> Por design de modelo, o pensamento consome cerca de 3.000 tokens e estoura rapidamente o KV Cache alocado de 4.096 se o prompt ou histórico for longo.</li>
2219    <li><strong style="color:#fff">O Fitting Automático do llama.cpp é Excepcional:</strong> Forçar o split por flags experimentais rígidas como <code>--override-tensor</code> prejudica as chamadas no Vulkan. O fitting por cálculo de grafos resolveu milimetricamente a distribuição de forma otimizada.</li>
2220    <li><strong style="color:#fff">Superaquecimento da GPU mitigado:</strong> Apenas a renderização profunda com dezenas de filtros simultâneos elevou a 80°C. Limitar a GPU para inferir frações densas mantém o chip a confortáveis 44-64°C.</li>
2221    <li><strong style="color:#fff font-weight:bold">O gargalo reside na taxa de I/O de Swap do HDD:</strong> Quando RAM estoura a 98%, a taxa cai para 5 tok/s devido ao HDD. Migrar o swap do host inteiro para unidades de estado sólido aumentará consideravelmente a velocidade de fluxo comb

2221inada.</li>
2222  </ul>
2223
2224  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:2rem;margin-bottom:.5rem">33.12 — Comparação Direta com Projetos de Comunidade</h3>
2225  <div class="tbl">
2226    <table>
2227      <thead>
2228        <tr>
2229          <th>Projeto / Autor</th>
2230          <th>Arquitetura Utilizada</th>
2231          <th>Modelo Testado</th>
2232          <th>Velocidade Geração</th>
2233          <th>Status de Autoria</th>
2234        </tr>
2235      </thead>
2236      <tbody>
2237        <tr><td><strong>Matheus Fertunani</strong></td><td>Linux Comercial / CPU Xeon pura (192GB RAM)</td><td>Qwen3.5 35B Q8</td><td>~7–8 tokens/s</td><td>Documentado no YouTube</td></tr>
2238        <tr><td><strong>AIVisionsLab (Nossos testes)</strong></td><td>Vulkan Híbrido (RX 580 8GB + 32GB RAM ECC)</td><td>Qwen3.5 35B Q6_K</td><td><strong>5,64 tokens/s</strong></td><td>✅ Validado e Registrado</td></tr>
2239      </tbody>
2240    </table>
2241  </div>
2242  <p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-top:0.5rem">
2243    <strong>A grande vantagem arquitetural:</strong> O teste de CPU pura de Matheus exige hardwares profissionais de alto custo com mais de 100GB de RAM instalada. O ecossistema de Vulkan do AIVisionsLab provou-se operável em uma GPU de menos de R$ 400 em mercado secundário!
2244  </p>
2245
2246  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:2rem;margin-bottom:.5rem">33.13 — Configurações Recomendadas para Testes Futuros</h3>
2247  <div class="cmd">
2248# Teste 6 — Desativação explícita de thinking via servidor em versões de compilação recentes:
2249.\\llama-server.exe -m "E:\\models\\Qwen3.5-35B-A3B-Uncensored-HauhauCS-Aggressive-Q6_K.gguf" --host 0.0.0.0 --p

2249ort 8081 --no-thinking
2250
2251# Teste 7 — Elevação estrita de buffers de contexto se a memória for redirecionada:
2252.\\llama-server.exe -m "E:\\models\\Qwen3.5-35B-A3B-...-Q6_K.gguf" --host 0.0.0.0 --port 8081 --ctx-size 8192
2253
2254# Teste 8 — Testar com quantizações menores como Q4_K_M (~21GB):
2255# Liberação de ~5GB adicionais na RAM host que reduz a dependência de swap no HDD e melhora consideravelmente a taxa de tok/s!</div>
2256
2257  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:2rem;margin-bottom:.5rem">33.14 — Lições Técnicas Consolidadas</h3>
2258  <ul style="padding-left:1.2rem;font-size:.82rem;color:#94a3b8;line-height:1.8;margin-bottom:1.25rem">
2259    <li><strong style="color:#fff">O silício de 2017 não é o limite:</strong> A placa de vídeo nunca crashou nos testes contínuos de estresse térmico, dezenas de vezes. O gargalo se provou uma mera calibração lógica de software.</li>
2260    <li><strong style="color:#fff">As esparsidades do MoE salvam a categoria:</strong> Sem MoE, seria impensável arrastar um modelo de 26GB de pesos ativos. O MoE faz com que apenas 3.1% dos neurônios fiquem de fato excitados por token.</li>
2261  </ul>
2262
2263  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:2rem;margin-bottom:.5rem">33.15 — Veredicto Geral do Laboratório</h3>
2264  <div class="tbl">
2265    <table>
2266      <thead>
2267        <tr>
2268          <th>Questão de Engenharia</th>
2269          <th>Veredicto Final</th>
2270        </tr>
2271      </thead>
2272      <tbody>
2273        <tr><td><strong>A RX 580 de 8GB suporta modelos de 35B parâmetros?</strong></td><td>✅ <strong>Sim</strong> (Em barramento híbrido mmap)</td></tr>
2274        <tr><td><strong>É prático para o dia a dia?</strong></td><td>⚠️ <strong>Não</strong> (5,6 tok/s e contexto de 4.096 tokens)</td></tr>
2275        <tr><td><strong>Vale como prova de conceito?</strong></td><td>✅ <strong>Absolutamente</strong></td></tr>
2276        <tr><td><strong>Houve throttling ou crash?</strong></td><td>✅ <strong>Zero ocorrências de instabilidade</strong></td></tr>
2277        <tr><td><strong>Temperatura máxima registrada</strong></td><td><strong>80°C</strong> (margem de segurança: ~90°C)</td></tr>
2278        <tr><td><strong>Velocidade de geração</strong></td><td><strong>~5,6 tokens/segundo</strong></td></tr>
2279        <tr><td><strong>Memória total mobilizada</strong></td><td><strong>~34 GB</strong> (VRAM + RAM + swap HDD)</td></tr>
2280      </tbody>
2281    </table>
2282  </div>
2283
2284  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:2rem;margin-bottom:.5rem">33.16 — Linha do Tempo do Experimento</h3>
2285  <div style="background:#141519;border:1px border-white/5;border-radius:6px;padding:1rem;font-family:'JetBrains Mono',monospace;font-size:0.75rem;line-height:1.7;color:#c0caf5;margin-bottom:1.5rem">
2286    <div>🕒 <strong>~17:39</strong> — Servidor sobe na porta 8080 (erro)</div>
2287    <div>🕒 <strong>~17:40</strong> — "Network Problem" no OpenWebUI (porta errada)</div>
2288    <div>🕒 <strong>~17:41</strong> — Servidor fechou ao trocar porta → reiniciado na 8081</div>
2289    <div>🕒 <strong>~17:42</strong> — Todas as ferramentas habilitadas no modelo (imagem, web, terminal)</div>
2290    <div>🕒 <strong>~17:43</strong> — <strong>TESTE 1</strong> inicia</div>
2291    <div>🕒 <strong>~17:44</strong> — Web search: 10 fontes recuperadas</div>
2292    <div>🕒 <strong>~17:44</strong> — Tentativa de gerar imagem → erro tratado → continua</div>
2293    <div>🕒 <strong>~17:45</strong> — "Pensado por um minuto" aparece no OpenWebUI</div>
2294    <div>🕒 <strong>~17:46</strong> — Resposta estruturada com título gerado internamente</div>
2295    <div>🕒 <strong>~17:50</strong> — Erro de protocolo (prefill + thinking) → sem entrega. Pico: 80°C</div>
2296    <div>🕒 <strong>~21:34</strong> — Sessão 2 inicia</div>
2297    <div>🕒 <strong>~21:34</strong> — <strong>TESTE 2</strong> inicia — web search ON (30 pesquisas). Contexto truncado.</div>
2298    <div>🕒 <strong>~21:48</strong>

2298 — <strong>TESTE 3</strong> automático — 25 pesquisas. Contexto truncado.</div>
2299    <div>🕒 <strong>~22:00</strong> — <strong>TESTE 4</strong> — web search OFF. Temperatura cai para 51°C. Timeout da UI.</div>
2300    <div>🕒 <strong>~23:06</strong> — <strong>TESTE 5</strong> — prompt de 45 tokens. Thinking decodificado com sucesso.</div>
2301    <div>🕒 <strong>~23:14</strong> — Resposta completa entregue em português ✅. GPU: 64°C | RAM: 96%</div>
2302  </div>
2303
2304  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:2rem;margin-bottom:.5rem">33.17 — Impacto para a Documentação do AIVisionsLab</h3>
2305<p style="font-size:0.85rem;color:#94a3b8;line-height:1.75;margin-bottom:1rem">
2306    Este experimento adiciona ao projeto: um novo benchmark robusto de 5,6 tok/s para modelos 35B MoE híbridos, a confirmação de que o fitting automático do llama.cpp supera configurações manuais rígidas via Vulkan, a identificação clara de incompatibilidade do modo thinking com contextos inferiores a 8.192 e a descrição de uma arquitetura estendida a 4 níveis de memória.
2307  </p>
2308
2309  <h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:2rem;margin-bottom:.5rem">33.18 — Tags e Metadados</h3>
2310  <div style="background:#141519;border:1px border-white/5;border-radius:6px;padding:1rem;font-family:'JetBrains Mono',monospace;font-size:0.75rem;line-height:1.7;color:#a9b1d6;margin-bottom:1.5rem">
2311    <div>🏷️ <strong>Tags:</strong> hybrid, moe, vulkan, rx580, large-model, thinking-mode, limits, qwen35b, benchmark, fitting-automático, 4-memory-levels, sem-throttling</div>
2312    <div style="margin-top:0.5rem">💻 <strong>Hardware:</strong> RX 580 2048SP + Xeon E5-2690 v3 + 32GB ECC DDR4</div>
2313    <div style="margin-top:0.25rem">💿 <strong>Software:</strong> llama.cpp b9049 + Vulkan SDK 1.4.350.0 + OpenWebUI v0.9.6</div>
2314    <div style="margin-top:0.25rem">📅 <strong>Data:</strong> 15/06/2026</div>
2315    <div style="margin-top:0.25rem">⏱️ <strong>Sessões:</strong> 2 (17h39–18h24 e 21h34–23h14)</div>
2316    <div style="margin-top:0.25rem">📊 <strong>Testes:</strong> 5 (4 falhas de software, 1 sucesso de inferência completa)</div>
2317    <div style="margin-top:0.25rem">📈 <strong>Benchmark:</strong> 5,57–5,64 tok/s (geração) | 34,13 tok/s (prompt eval)</div>
2318    <div style="margin-top:0.25rem">🔥 <strong>Temperatura pico:</strong> 80°C</div>
2319    <div style="margin-top:0.25rem">🏆 <strong>Status final:</strong> ✅ HARDWARE APROVADO</div>
2320  </div>
2321
2322  <blockquote style="font-size:.85rem;color:#e11d48;font-style:italic;line-height:1.75;border-left:3px solid #e11d48;padding-left:.9rem;margin:2rem 0;background:rgba(225,29,72,0.02);padding-top:0.5rem;padding-bottom:0.5rem">
2323    "O problema nunca foi o hardware.<br>
2324    Foi a combinação de thinking + web search esgotando os 4.096 tokens de contexto.<br>
2325    Com prompt curto e sem web search, o Qwen3.5-35B Q6_K responde normalmente numa RX 580 de 2017."<br>
2326    <span style="font-style:normal;font-weight:bold;display:block;margin-top:0.5rem;font-size:0.75rem">— AIVisionsLab, 15/06/2026</span>
2327  </blockquote>
2328
2329  <div class="footer">
2330    Documentação gerada por Claude Sonnet 4.6 — 15/06/2026 · Baseada em logs reais, screenshots, benchmarks e observações em tempo real · Sessão 1: ~17h39–18h24 | Sessão 2: ~21h34–23h14 · 5 testes documentados · 1 bem-sucedido · Hardware de 2017 processando IA de 2025
2331  </div>
2332</div>`},linhatempo:{id:"linhatempo",title:"37. LINHA DO TEMPO COLETIVA — A EVOLUÇÃO DO RX 580 COMO PLATAFORMA DE IA",desc:"Análise histórica e comparativa unificada documentando a quebra de barreiras e obsolescência da RX 580 em três etapas revolucionárias (2025-2026).",meta:{version:"1.0.0",type:"estudo_caso",category:"estudo-caso",subcategory:"linha-tempo",difficulty:"beginner",estimated_time_min:25,tags:["evolucion-historica","rx580-timeline","comunidade-hacker"],last_reviewed:"2026-07-01",relationships:{prerequisites:["meta"],previous:"meta",next:"audio_rvc",related:["jornada","comunidade"]}},html:`<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.25rem">O resgate técnico e a ascensão da AMD Radeon RX 580 como uma plataforma de inteligência artificial prática não é fruto de uma única mente, mas sim de uma <strong>evolução coletiva ascendente</strong>. Três projetos independentes, construídos sobre a base matemática comum do ecossistema <code>ggml</code>, pavimentaram esta jornada:</p>
2333
2334  <!-- Linha do Tempo Visual -->
2335  <div style="position:relative;margin:2rem 0;padding-left:1.5rem;border-left:2px solid var(--b)">
2336    <div style="margin-bottom:1.5rem;position:relative">
2337      <div style="position:absolute;

2337left:-29px;top:2px;width:12px;height:12px;border-radius:50%;background:var(--r);border:2px solid #07080a"></div>
2338      <span style="font-family:'JetBrains Mono',monospace;font-size:11px;color:var(--r);font-weight:bold">JANEIRO DE 2025</span>
2339      <h5 style="color:#fff;margin:.25rem 0;font-size:.88rem">Método Amihart (Debian Linux)</h5>
2340      <p style="font-size:.8rem;color:#94a3b8;line-height:1.6;margin:0">Primeiro marco documentado. Validou LLMs acelerados por Vulkan no <code>llama.cpp</code> obtendo 24.56 t/s. No entanto, declarou: <em>"Vulkan não funciona para Stable Diffusion"</em>, devido às limitações do software na época, recorrendo ao ROCm via Docker.</p>
2341    </div>
2342    <div style="margin-bottom:1.5rem;position:relative">
2343      <div style="position:absolute;left:-29px;top:2px;width:12px;height:12px;border-radius:50%;background:#eab308;border:2px solid #07080a"></div>
2344      <span style="font-family:'JetBrains Mono',monospace;font-size:11px;color:#eab308;font-weight:bold">DEZEMBRO DE 2025</span>
2345      <h5 style="color:#fff;margin:.25rem 0;font-size:.88rem">Método DadHacks (Linux/Debian)</h5>
2346      <p style="font-size:.8rem;color:#94a3b8;line-height:1.6;margin:0">Refutou empiricamente a impossibilidade de Stable Diffusion via Vulkan. Utilizou o recém-lançado motor <code>stable-diffusion.cpp</code> com a flag compiladora <code>-DSD_VULKAN=ON</code> para rodar geração GGUF do Flux Schnell com segmentação CPU/GPU.</p>
2347    </div>
2348    <div style="position:relative">
2349      <div style="position:absolute;left:-29px;top:2px;width:12px;height:12px;border-radius:50%;background:#22c55e;border:2px solid #07080a"></div>
2350      <span style="font-family:'JetBrains Mono',monospace;font-size:11px;color:#22c55e;font-weight:bold">TEMPORADA 2026</span>
2351      <h5 style="color:#fff;margin:.25rem 0;font-size:.88rem">AIVisionsLab (Windows Nativo + WSL2)</h5>
2352      <p style="font-size:.8rem;color:#94a3b8;line-height:1.6;margin:0">Unificação do ecossistema de produção. Desenvolveu automação nativa via <code>.bat</code>, integrou renderização Vulkan local a painéis amigáveis (OpenWebUI via Docker), documentou os fallbacks para CPU Xeon em modelos SOTA e mapeou de forma exaustiva os motivos de falhas do DirectML/ROCm no Windows.</p>
2353    </div>
2354  </div>
2355
2356  <h4 style="color:#fff;font-family:'Syne',sans-serif;font-size:11px;letter-spacing:1px;margin:2rem 0 .75rem 0;text-transform:uppercase">A. A Pergunta que Cada Projeto Respondeu</h4>
2357  <p style="font-size:.82rem;color:#94a3b8;line-height:1.7;margin-bottom:1rem">A evolução da maturidade técnica da RX 580 pode ser traduzida pelas premissas respondidas pelos autores:</p>
2358  <ul style="padding-left:1.2rem;font-size:.82rem;color:#94a3b8;line-height:1.8;margin-bottom:1.25rem">
2359    <li><strong style="color:#fff">Amihart (Jan 2025):</strong> <em>"É possível rodar LLMs de forma rápida na RX 580 sem ROCm oficial?"</em> <strong style="color:var(--r)">Sim</strong>, compilando o llama.cpp com Vulkan API puro.</li>
2360    <li><strong style="color:#fff">DadHacks (Dez 2025):</strong> <em>"É possível gerar imagens via IA aceleradas por Vulkan na RX 580?"</em> <strong style="color:var(--r)">Sim</strong>, compilando stable-diffusion.cpp e utilizando o formato alternativo estável GGUF.</li>
2361    <li><strong style="color:#fff">AIVisionsLab (2026):</strong> <em>"É possível colocar toda essa infraestrutura em produção integrada e amigável no Windows?"</em> <strong style="color:var(--r)">Sim</strong>, construindo pontes de rede de contêineres Docker, automações unificadas e fallbacks inteligentes de CPU para excedentes de VRAM.</li>
2362  </ul>
2363
2364  <h4 style="color:#fff;font-family:'Syne',sans-serif;font-size:11px;letter-spacing:1px;margin-bottom:.75rem;text-transform:uppercase">B. Tabela Mestre Comparativa Unificada</h4>
2365  <p style="font-size:.82rem;color:#94a3b8;line-height:1.7;margin-bottom:1rem">Mapeamento estruturado cruzado das capacidades e conquistas cumulativas:</p>
2366  <div class="tbl" style="margin-bottom:1.5rem">
2367    <table>
2368      <thead>
2369        <tr>
2370          <th>Capacidade</th>
2371          <th>Amihart (2025)</th>
2372          <th>DadHacks (2025)</th>
2373          <th>AIVisionsLab (2026)</th>
2374        </tr>
2375      </thead>
2376      <tbody>
2377        <tr><td><strong>Sistema Base</strong></td><td>Debian Linux</td><td>Linux Debian</td><td>Windows 10 Pro + WSL2 Linux</td></tr>
2378        <tr><td><strong>Motor de LLM</strong></td><td>✅ Vulkan (24.56 t/s)</td><td>✅ Vulkan (llama.cpp)</td><td>✅ Vulkan (15-16 t/s)</td></tr>
2379        <tr><td><strong>Motor SD</strong></td><td>❌ Não viável em Vulkan</td><td>✅ Vulkan puro</td><td>✅ Vulkan puro (~72s SD1.5)</td></tr>
2380        <tr><td><strong>Flux GGUF</strong></td><td>❌ Inexistente</td><td>✅ Linha de comando</td><td>✅ Híbrido GPU/CPU Estável</td></tr>
2381        <tr><td><strong>Interface Gráfica</strong></td><td>

2381❌ Apenas Console / WebUI Docker</td><td>❌ Apenas CLI</td><td>✅ OpenWebUI + API integrada</td></tr>
2382        <tr><td><strong>Automação</strong></td><td>❌ Não desenvolvido</td><td>❌ Não desenvolvido</td><td>✅ Scripts .bat double-click</td></tr>
2383        <tr><td><strong>Estabilidade VRAM</strong></td><td>❌ Limitação física ROCm</td><td>⚠️ Manual CLI</td><td>✅ VAE Tiling integrado</td></tr>
2384        <tr><td><strong>Modelos &gt; 8GB</strong></td><td>❌ OOM crônicos</td><td>⚠️ CPU offload básico</td><td>✅ Quad-Channel REG ECC fallbacks</td></tr>
2385        <tr><td><strong>Animação Video</strong></td><td>❌ Não abordado</td><td>❌ Não abordado</td><td>✅ AnimateDiff WSL2 CPU Xeon</td></tr>
2386        <tr><td><strong>Detecção GGUF</strong></td><td>❌ Não abordado</td><td>❌ Não abordado</td><td>✅ Mapeamento leejet vs city96</td></tr>
2387      </tbody>
2388    </table>
2389  </div>
2390
2391  <h4 style="color:#fff;font-family:'Syne',sans-serif;font-size:11px;letter-spacing:1px;margin-bottom:.75rem;text-transform:uppercase">C. O Denominador Técnico Comum — Engine GGML</h4>
2392  <p style="font-size:.85rem;color:#94a3b8;line-height:1.8;margin-bottom:1rem">A tecnologia central de compressão e execução que viabilizou o aproveitamento de GPUs legacy foi o ecossistema desenvolvido por Georgi Gerganov (<strong>ggml / llama.cpp / stable-diffusion.cpp</strong>). Ao portar as operações tensoriais diretamente para a linguagem C/C++ e expor ganchos nativos à biblioteca gráfica aberta do <strong>Vulkan API</strong>, esta implementação permite que placas sem pacotes modernos de driver proprietário realizem cálculos massivos nativos (contornando drivers de vendor restritivos).</p>
2393 
2394   <h4 style="color:#fff;font-family:'Syne',sans-serif;font-size:11px;letter-spacing:1px;margin-bottom:.75rem;text-transform:uppercase">D. Filosofia Convergente</h4>
2395   <p style="font-size:0.85rem;color:#94a3b8;line-height:1.8">Manifestações diretas que eternizam o propósito livre destes pesquisadores:</p>
2396   <blockquote style="font-size:.82rem;color:#94a3b8;font-style:italic;line-height:1.7;border-left:3px solid var(--r);padding-left:.9rem;margin-bottom:.85rem">
2397     <strong>Amihart:</strong> "Apesar de quão antiga é esta GPU, é tecnicamente possível utilizá-la para IA se adaptarmos o fluxo de execução... otimização de baixo nível é a chave para o reaproveitamento de hardware legado."
2398   </blockquote>
2399   <blockquote style="font-size:.82rem;color:#94a3b8;font-style:italic;line-height:1.7;border-left:3px solid var(--r);padding-left:.9rem;margin-bottom:.85rem">
2400     <strong>DadHacks:</strong> "Esse setup fornece uma ponte viável de aproveitamento técnico dos investimentos existentes em hardware sem exigir upgrades caros ou dependência de pacotes de software inchados."
2401   </blockquote>
2402   <blockquote style="font-size:.82rem;color:#94a3b8;font-style:italic;line-height:1.7;border-left:3px solid var(--r);padding-left:.9rem;margin-bottom:1.25rem">
2403     <strong>AIVisionsLab:</strong> "A degradação por obsolescência de drivers e ecossistemas fechados é um fator comercial e não uma limitação material da placa. O hardware legado mantém sua utilidade operacional mediante suporte de software aberto."
2404   </blockquote>
2405 
2406   <div class="card" style="border:1px dashed rgba(255,255,255,0.08);background:rgba(255,255,255,0.01);padding:1rem;border-radius:4px;margin-top:1.25rem">
2407     <strong style="color:var(--r)">Crédito de Co-autoria e Referência:</strong> Linha do tempo técnica dedicada ao esforço experimental cumulativo de <strong>Amihart</strong>, <strong>DH (DadHacks)</strong>, <strong>leejet</strong>, <strong>ggerganov</strong>, <strong>woodrex</strong> e a todos os desenvolvedores independentes focados em reaproveitamento digital e preservação de hardware.
2408       </div>`},linux_nativo:{id:"linux_nativo",title:"43. LINUX NATIVO — Ubuntu 26.04 LTS + RX 580 + Vulkan",desc:"AIVisionsLab — Documentação da Stack Completa em Linux Real",meta:{version:"1.0.0",type:"documento",category:"software",subcategory:"sistema-operacional",difficulty:"advanced",estimated_time_min:40,tags:["ubuntu-native","linux-vulkan","low-level-compilation"],last_reviewed:"2026-07-01",relationships:{prerequisites:["audio_whisper"],previous:"audio_whisper",next:"audio_srt_ptbr",related:["comfyui-wsl","amihart"]},part:1},html:`<div class="card bg-red-950/20 border border-red-500/30 p-5 rounded-md my-6">
2409  <h4 style="color:#ef4444;font-weight:800;font-size:0.95rem;margin-bottom:0.6rem;display:flex;items-center;gap:0.4rem">
2410    🚨 INCIDENT REPORT (16/06/2026): O GRANDE DESASTRE DE ESPAÇO E A QUEDA DO GNOME
2411  </h4>
2412  <p style="font-size:0.82rem;color:#fca5a5;line-height:1.75;margin-bottom:0.75rem">
2413    <strong>Status Crítico:</strong> Durante os testes de pipelines extremos e download massivo de modelos GGUF pesados na partição dedicada de 97GB, o espaço físico foi totalmente exaurido (0 bytes livres no swap/root). Em uma tentativa rápida de liberação de cache e limpeza forçada via terminal, dependências críticas do ambiente gráfico foram corrompidas.
2414  </p>
2415  <ul style="padding-left:1.2rem;font-size:.8rem;color:#fbcfe8;line-height:1.8;margin-bottom:1rem">
2416    <li><strong>Sintoma:</strong> O gerenciador de Janelas <strong>GNOME</strong> quebrou por completo, resultando em loops de login e tela preta terminal sem carregamento de interface de vídeo.</li>
2417    <li><strong>Ação de Recuperação:</strong> Formatação física e reinstalação imediata do sistema Linux Bare-Metal do zero para reconstrução limpa e redimensionamento adequado de partições.</li>
2418    <li><strong>O Veredicto Existencial:</strong> <em>"Definitivamente preciso aprender Linux!"</em> — A jornada do AIVisionsLab prova que dominar o terminal e a alocação de espaço sob LVM é tão crucial quanto conhecer os shaders de backend do Vulkan.</li>
2419  </ul>
2420</div>
2421
2422<p style="font-size:.88rem;color:#94a3b8;line-height:1.9;margin-bottom:1.25rem">
2423  Documentação detalhada e homologada de execução direta em Linux nativo. Sem WSL2, sem emulação de contêiner ou Docker Passthrough: acesso bare-metal real através de drivers open-source <strong>Mesa RADV</strong> sobre o novíssimo <strong>Ubuntu 26.04 LTS (Resolute Raccoon)</strong> rodando o kernel Linux 7.0.
2424</p>
2425
2426<div style="display:grid;grid-template-columns:repeat(auto-fit,minmax(200px,1fr));gap:.85rem;margin-bottom:1.25rem">
2427  <div class="stat"><div class="stat-icon">🐧</div><div><div class="stat-lbl">Plataforma Linux</div><div class="stat-val" style="font-size:.82rem">Ubuntu 26.04 LTS</div><div style="font-size:.65rem;color:#475569;margin-top:.15rem">Kernel 7.0 (Resolute)</div></div></div>
2428  <div class="stat"><div class="stat-icon">🎛️</div><div><div class="stat-lbl">Vulkan Driver</div><div class="stat-val" style="font-size:.82rem">Mesa RADV 26.0.3</div><div style="font-size:.65rem;color:#475569;margin-top:.15rem">Vulkan Core version 1.4.341</div></div></div>
2429  <div class="stat"><div class="stat-icon">💽</div><div><div class="stat-lbl">Particionamento</div><div class="stat-val" style="font-size:.82rem">Ext4 dedidada 97GB</div><div style="font-size:.65rem;color:#475569;margin-top:.15rem">HDD sdb6 Dual Boot</div></div></div>
2430  <div class="stat"><div class="stat-icon">📦</div><div><div class="stat-lbl">

2430Docker Runtime</div><div class="stat-val" style="font-size:.82rem">Nativo Linux Engine</div><div style="font-size:.65rem;color:#475569;margin-top:.15rem">Pontes OpenWebUI a 100%</div></div></div>
2431</div>
2432
2433<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:1.5rem;margin-bottom:.5rem">Tabela Comparativa Bruta: Windows 10 vs Linux Nativo</h3>
2434<div class="tbl">
2435  <table>
2436    <thead>
2437      <tr>
2438        <th>Cenário / Carga de Trabalho</th>
2439        <th>Windows 10 (AMD oficial)</th>
2440        <th>Linux Ubuntu 26.04 (Mesa RADV)</th>
2441        <th>Vencedor Crucial</th>
2442      </tr>
2443    </thead>
2444    <tbody>
2445      <tr>
2446        <td><strong>LLM (Qwen3 4B GGUF) @ 99 layers</strong></td>
2447        <td>~15-17 tokens/s</td>
2448        <td><strong>~35 tokens/s (RADV engine)</strong></td>
2449        <td>🏆 <strong>Linux (2x mais veloz)</strong></td>
2450      </tr>
2451      <tr>
2452        <td><strong>LLM Grande (Qwen3.6 35B) @ Max layers</strong></td>
2453        <td><strong>7.62 tokens/s (max 10 layers)</strong></td>
2454        <td>5.18 tokens/s (max 20 layers)</td>
2455        <td>⚖️ <strong>Empate Técnico</strong> (Linux permite 2x mais layers, Windows é mais rápido por layer)</td>
2456      </tr>
2457      <tr>
2458        <td><strong>Stable Diffusion (Dreamshaper8 - 50st)</strong></td>
2459        <td><strong>~72 segundos (1.44s/it)</strong></td>
2460        <td>~85 segundos (1.65s/it)</td>
2461        <td>🏆 <strong>Windows (estável por driver)</strong></td>
2462      </tr>
2463      <tr>
2464        <td><strong>Flux Schnell (4 steps, 512x512)</strong></td>
2465        <td>~84 segundos (OOM se 1024x1024)</td>
2466        <td><strong>~52 segundos sampling (Total: 95s)</strong></td>
2467        <td>🏆 <strong>Linux (melhor swap VRAM)</strong></td>
2468      </tr>
2469      <tr>
2470        <td><strong>whisper.cpp (large-v3-turbo, áudio 106s)</strong></td>
2471        <td>307 s (Uso de VRAM: 2.6 GB)</td>
2472        <td><strong>23.58 s (Uso de VRAM: 1.6 GB)</strong></td>
2473        <td>🏆 <strong>Linux (Absurdamente superior)</strong></td>
2474      </tr>
2475    </tbody>
2476  </table>
2477</div>
2478
2479<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:1.5rem;margin-bottom:.5rem">Instalação e Dual-Boot sem Sobrescrever Registros</h3>
2480<p style="font-size:.82rem;color:#94a3b8;line-height:1.75">
2481  1. No Windows, libere espaço usando o Gerenciador de Discos (Diminuir Volume).<br>
2482  2. Prepare o pendrive com a ISO desktop do <strong>Ubuntu 26.04 amd64</strong>.<br>
2483  3. No instalador do Ubuntu, selecione a opção "Instalar ao lado do Windows Boot Manager". O instalador criará a partição e configurará o menu GRUB para permitir a seleção de boot em cada reinicialização automaticamente.
2484</p>
2485
2486<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:1.5rem;margin-bottom:.5rem">Pós-Instalação: Validação Bare-Metal da GPU Polaris</h3>
2487<div class="code">
2488  <span class="code-lang">bash</span>
2489  <pre>lspci | grep -i vga
2490# AMD Radeon RX 580 2048SP detectada!
2491
2492vulkaninfo --summary 2>/dev/null | grep -A5 "Devices"
2493# Deve exibir: GPU0: DRIVER_ID_MESA_RADV | driverInfo = Mesa 26.0.3 ✅</pre>
2494</div>
2495
2496<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:1.5rem;margin-bottom:.5rem">Pilha de Comandos de Execução Homologados no Ubuntu</h3>
2497
2498<h4 style="color:#fff;font-size:.85rem;font-weight:600;margin-top:.85rem">1. Inicializando llama-server via Vulkan</h4>
2499<div class="code">
2500  <span class="code-lang">bash</span>
2501  <pre>~/llama.cpp/build/bin/llama-server \\
2502  -m "/run/media/aivisions-lab-studio/NVMe/models/qwen3/Qwen3-4B-Q4_K_M.gguf" \\

2503  --host 0.0.0.0 --port 8081 \\
2504  -ngl 99 -t 24</pre>
2505</div>
2506
2507<h4 style="color:#fff;font-size:.85rem;font-weight:600;margin-top:.85rem">2. Geração de Imagens (Flux Schnell) — Segredos Contra Travamento do GNOME</h4>
2508<div class="code">
2509  <span class="code-lang">bash</span>
2510  <pre>~/stable-diffusion.cpp/build/bin/sd-server \\
2511  --listen-ip 0.0.0.0 --listen-port 7860 \\
2512  --diffusion-model /run/media/aivisions-lab-studio/NVMe/ia_storage/models/Stable-diffusion/flux1-schnell-q4_k.gguf \\
2513  --vae /run/media/aivisions-lab-studio/NVMe/ia_storage/models/Stable-diffusion/ae.safetensors \\
2514  --clip_l /run/media/aivisions-lab-studio/NVMe/ia_storage/models/Stable-diffusion/clip_l.safetensors \\
2515  --t5xxl /run/media/aivisions-lab-studio/NVMe/ia_storage/models/Stable-diffusion/t5xxl_fp16.safetensors \\
2516  --cfg-scale 1.0 --steps 4 --clip-on-cpu --vae-on-cpu --vae-tiling</pre>
2517</div>
2518<p style="font-size:.8rem;color:#64748b;line-height:1.6;margin-top:.35rem">
2519  💡 <strong>Flags Críticas:</strong> O parâmetro <code>--vae-tiling</code> é estritamente obrigatório. Sem ele, a decodificação do VAE estoura o limite de memória física e trava o ambiente gráfico GNOME. Evite usar <code>--backend vulkan0</code> para este modelo pesado no Linux, pois gera bugs de contexto perdidos.
2520</p>
2521
2522<h4 style="color:#fff;font-size:.85rem;font-weight:600;margin-top:.85rem">3. Transcrição Rápida de Áudio via whisper.cpp</h4>
2523<div class="code">
2524  <span class="code-lang">bash</span>
2525  <pre>~/whisper.cpp/build/bin/whisper-cli \\
2526  -m ~/whisper.cpp/models/ggml-large-v3-turbo.bin \\
2527  -f "/caminho/para/audio.wav" \\
2528  -l pt --output-txt</pre>
2529</div>
2530
2531<h3 style="color:#fff;font-family:'Syne',sans-serif;font-size:1.05rem;font-weight:700;margin-top:1.5rem;margin-bottom:.5rem">Serviços e Contêineres Docker Rodando Paralelos</h3>
2532<div class="tbl">
2533  <table>
2534    <thead>
2535      <tr>
2536        <th>Contêiner ativo</th>
2537        <th>Imagem Base</th>
2538        <th>Porta Mapeada</th>
2539        <th>Finalidade</th>
2540      </tr>
2541    </thead>
2542    <tbody>
2543      <tr>
2544        <td><strong>open-webui</strong></td>
2545        <td>ghcr.io/open-webui/open-webui:main</td>
2546        <td>3000 -> 8080</td>
2547        <td>Painel unificado de Chat com LLM e render SD</td>
2548      </tr>
2549      <tr>
2550        <td><strong>portainer</strong></td>
2551        <td>portainer/portainer-ce</td>
2552        <td>9000</td>
2553        <td>Gerenciamento visual de processos e pilhas Docker</td>
2554      </tr>
2555      <tr>
2556        <td><strong>searxng</strong></td>
2557        <td>searxng/searxng:latest</td>
2558        <td>8080</td>
2559        <td>Mecanismo de busca privado para as respostas ancoradas de IA</td>
2560      </tr>
2561    </tbody>
2562  </table>
2563</div>
2564
2565<div class="card bg-black/40 border border-white/5 p-4 rounded-md my-4">
2566  <h4 style="color:#fff;font-weight:700;font-size:0.9rem;margin-bottom:0.5rem">⚠️ Lições Autônomas Aprendidas em Linux</h4>
2567  <p style="font-size:0.8rem;color:#94a3b8;line-height:1.7">
2568    <strong>ROCm Abandonado:</strong> Placas GCN4 / Polaris como a RX 580 não têm mais suporte no SDK ROCm oficial. Tentar rodar Ollama oficial via GPU Docker nela falhará. A rota de ouro é rodar o <strong>Ollama na CPU Xeon</strong> ou usar o <strong>llama-server nativo compilado com Vulkan</strong>! Docker só deve ser empregado para frontends e servidores secundários livres.
2569  </p>
2570</div>`},llama:{id:"llama",title:"07. COMPILAÇÃO DO LLAMA.CPP (BACKEND VULKAN)",desc:"Passo a passo com os comandos mestre para gerar os binários otimizados de LLM para a RX 580.",meta:{version:"1.0.0",type:"documento",category:"software",subcategory:"compilacao",difficulty:"advanced",estimated_time_min:25,tags:["llamacpp","vulkan-acceleration","compilacao-cpp"],last_reviewed:"2026-07-01",relationships:{prerequisites:["jornada"],previous:"jornada",next:"sdcpp",related:["modelos","limit_qwen_35b"]}},html:`<div class="note">⚠ Certifique-se de executar os comandos abaixo exclusivamente dentro do <strong>Developer PowerShell do Visual Studio</strong></div>
2571  <div class="code"><pre># Navegar para a partição de alta velocidade e clonar o repositório oficial
2572cd E:\\
2573git clone https://github.com/ggerganov/llama.cpp
2574cd llama.cpp
2575
2576# Instanciar o diretório de build injetando a flag compiladora do backend Vulkan
2577cmake -B build -DGGML_VULKAN=ON -DCMAKE_BUILD_TYPE=Release
2578
2579# Disparar a compilação utilizando 20 threads paralelas do processador Xeon
2580cmake --build build --config Release -j20</pre></div>
2581  <h4 class="s" style="margin-top:1rem;margin-bottom:.5rem;color:#fff">Comando de Validação do Dispositivo de Aceleração</h4>
2582  <div class="code"><pre>cd build\\bin\\Release
2583.\\llama-cli.exe --list-devices
2584# Retorno esperado do console: Vulkan0: AMD Radeon RX 580 2048SP ✅</pre></div>`},meta:{id:"meta",title:"36. REFERÊNCIAS E METADADOS DO DOCUMENTO",desc:"Análise técnica do ciclo de vida físico do hardware legado e filosofia de reaproveitamento do laboratório.",meta:{version:"1.0.0",type:"documento",category:"comunidade",subcategory:"metadados",difficulty:"beginner",estimated_time_min:10,tags:["silicon-governance","metadata-specs","efficie