https://sambanova-deploy.vercel.app/assets/index-DWEGEl4F.js

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The two facing SN50 rows with the switches at the row ends and the GPU rows alongside follow the customer layout SambaNova shared 09-17, as guidance. Node racks at 20 kW typical; switch and B300 rack power are nominal estimates.",Mo="Prefill feeds decode across a shared fabric.",La="Kimi K3";function ie(e){if(e>=1e3){const t=e/1e3;return`${t>=10?t.toFixed(1):t.toFixed(2)} MW`}

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5),i.items.forEach((p,m)=>r.push({x:-(e-(m+.5)*F),z:i.x,kind:p.kind,rotY:d}))}return{rows:n,slots:r}}function No(e,t,s){return{kind:"callout",id:`zone-${e}`,pos:[t.stand[0],Math.max(s,1.6)+.1,t.stand[1]],look:[t.look[0],1.5,t.look[1]]}}function z1(e){return e.zoneGeoms!==void 0}function B1(e,t){const s=ft(t);if(e.slots.length===0)return{...e,hetero:s,zoneGeoms:[],clusters:0,clusterLen:be,clusterTops:[]};const n=It(),r=Math.min(s.blocks,Ui),i=r*be+(r-1)*jo,l=[];for(let S=0;S<r;S++)l.push(i/2-S*Fi);const d=[],c=(S,C,Z,T)=>{S.forEach((_,L)=>{const P=T-(L+.5)*F,$=L===0,V=L===S.length-1,D=Z>0?"left":"right",R=Z>0?"right":"left",O=S.length===1?"both":$?D:V?R:void 0;d.push({x:C,z:P,kind:_.kind,rotY:Z,label:_.label,sub:_.sub,zone:_.zone,...O?{exposedSide:O}:{}})})};for(const S of l)c(n.p1,Ft,-Math.PI/2,S),c(n.a,Ce,Math.PI/2,S),c(n.b,Fe,-Math.PI/2,S),c(n.p2,Ie,Math.PI/2,S);const p=l[0],m=p,f=l[r-1]-be,x=n.a.length-n.aSn,h=n.b.length-n.bSn,u=Math.max(x,h)*F,b=S=>l.flatMap(S),w=[{id:"decode",frontZ:p,backZ:f,rowZs:l,width:2*st,racks:r*(n.aSn+n.bSn),marks:b(S=>[{x:Ce,z0:S,z1:S-n.aSn*F},{x:Fe,z0:S,z1:S-n.bSn*F}]),pin:[Ce,p-F*.5],stand:[0,p+1.8],look:[0,p-6]},{id:"networking",frontZ:p-Math.min(n.aSn,n.bSn)*F,backZ:f,rowZs:l,width:2*(Ri+G),racks:r*(x+h),marks:b(S=>[{x:Ce,z0:S-n.aSn*F,z1:S-n.a.length*F},{x:Fe,z0:S-n.bSn*F,z1:S-n.b.length*F}]),pin:[Fe,p-be+u/2],stand:[-.5,p-be+u+2.2],look:[Fe-G/2,p-be+u*.45]},{id:"prefill",frontZ:p,backZ:l[r-1]-n.p1.length*F,rowZs:l,width:2*st,racks:r*(n.p1.length+n.p2.length),marks:b(S=>[{x:Ft,z0:S,z1:S-n.p1.length*F},{x:Ie,z0:S,z1:S-n.p2.length*F}]),pin:[Ie,p-F*1],stand:[st+Ao*.625,p+1.2],look:[Ie+G/2,p-2]}],v=2*st,k=Math.tan(Zi*Math.PI/180/2)*Pi,M=(v/2+1.6)/k,y=9.5,g={pos:[0,3.7,m+y],look:[0,.6,m-2.4]},A=m+Math.max(z.frontClearanceM+2,M,y+1.5),E=f-Vi;return{...e,slots:d,rows:[],contentWidth:v,contentCenterZ:0,frontRowZ:m,backRowZ:f,lastRowZ:f,sizeZFront:A,sizeZBack:E,zFront:A,zBack:E,walkZFront:m+_i,walkStartZ:m+3.2,walkZBack:f-Ti,hallWidth:2*Hi,sizePose:g,hetero:s,zoneGeoms:w,clusters:r,clusterLen:be,clusterTops:l}}const ha={prefill:{dot:"bg-zinc-300",bar:"bg-zinc-400/80",text:"text-zinc-200"},networking:{dot:"bg-violet-200",bar:"bg-violet-900",text:"text-violet-100"},decode:{dot:"bg-purple-400",bar:"bg-purple-500",text:"text-purple-200"}};function Ii(e){return e>=1e3?`${(e/1e3).toFixed(1)}k m²`:`${e.toLocaleString(void 0,{maximumFractionDigits:0})} m²`}function Di(e){const t=e/1e3;return t>=1e3?`${(t/1e3).toFixed(1)} kt`:`${Math.round(t).toLocaleString()} t`}function Oi(){const e=j(x=>x.outputs),t=j(x=>x.zoneAnchors),s=j(x=>x.mode),n=j(x=>x.walkEyeHeight),r=j(x=>x.setMode),i=j(x=>x.setWalkFocus),l=j(x=>x.setTrayOpen),d=j(x=>x.setPanelExpanded),c=x=>{const h=t==null?void 0:t.find(u=>u.id===x);h&&(s!=="walk"&&r("walk"),l(!1),d(!1),i(No(x,h,n)))},p=ft(e.rackCount),m=e.rackCount>Ut,f=p.totalRacks*go;return o.jsxs("div",{className:"flex flex-col gap-4",children:[o.jsxs("div",{className:"flex flex-col gap-0.5",children:[o.jsxs("div",{className:"text-[11px] leading-tight text-purple-100/80",children:[p.blocks===1?"One":p.blocks," 256-socket scale-up domain",p.blocks===1?"":"s"," · ",La," · heterogeneous disaggregation"]}),o.jsx("div",{className:"text-[11px] italic text-white/50",children:Mo})]}),o.jsxs("div",{className:"grid grid-cols-3 gap-2 rounded-lg border border-purple-300/20 bg-[#1a0a2b]/80 px-3 py-2.5",children:[o.jsxs("div",{className:"flex flex-col",children:[o.jsx("span",{className:"font-mono text-xl font-semibold tabular-nums leading-none text-white",children:p.totalRacks.toLocaleString()}),o.jsx("span",{className:"mt-1 text-[9px] uppercase tracking-[0.14em] text-purple-200/70",children:"racks total"})]}),o.jsxs("div",{className:"flex flex-col",children:[o.jsx("span",{className:"font-mono text-xl font-semibold tabular-nums leading-none text-white",children:ie(p.totalKW)}),o.jsx("span",{className:"mt-1 text-[9px] uppercase tracking-[0.14em] text-purple-200/70",children:"typical draw"})]}),o.jsxs("div",{className:"flex flex-col",children:[o.jsx("span",{className:"font-mono text-xl font-semibold leading-none text-white",children:"Air"}),o.jsx("span",{className:"mt-1 text-[9px] uppercase tracking-[0.14em] text-purple-200/70",children:"cooled, no CDU"})]})]}),o.jsxs("div",{className:"text-[10px] leading-snug text-white/40",children:["SambaNova footprint: ",o.jsxs("span",{className:"text-white/65",children:[p.sambanovaRacks.toLocaleStr

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large-scale AI inference",body:["A grid of compute units (PCUs) and memory units (PMUs) j

5oined by mesh switches — so a whole inference pipeline runs spatially across the chip instead of one kernel at a time.","864 PCUs and PMUs on each SN50 RDU. PCUs compute, PMUs hold data right next to them, and the switches route between them.","AGCUs are the portal to off-chip memory and I/O."],terms:[{term:"PCU",def:"Compute unit — the programmable compute element of the grid."},{term:"PMU",def:"Memory unit — holds a tile of weights and activations beside the compute."},{term:"Mesh switch",def:"Routes data between PCUs and PMUs across the chip."},{term:"AGCU",def:"Address Generation and Coalescing Unit — the portal to off-chip memory and I/O."}],duration:14,camera:{pos:[8.5,14.5,17.5],look:[.5,0,.5]},shift:.165,dies:1,tokens:!1,memoryFlow:!1,gpuLane:!1,highlight:"switches"},{id:"memory",label:"Three-tier memory",title:"Three tiers of memory",kicker:"DDR · HBM · on-chip SRAM",body:["Large model pools live in DDR, active weights move through HBM, and activations stay in on-chip SRAM right next to compute — so a request never waits on memory traffic.","DDR holds every model, up to 512 GB per RDU. HBM holds the active model’s weights, 64 GB per RDU. SRAM keeps the working set beside the compute, 432 MB per RDU.","CXL-based DDR is also used for prompt caching."],terms:[{term:"DDR",def:"High-capacity tier — the full model pool, up to 512 GB per RDU."},{term:"HBM",def:"High-bandwidth tier — the active model’s weights, 64 GB per RDU."},{term:"SRAM",def:"On-chip tier — activations next to compute, 432 MB per RDU."}],duration:16,camera:{pos:[-14,12.5,13],look:[-7,0,.5]},shift:.17,dies:1,tokens:!1,memoryFlow:!0,gpuLane:!1,highlight:"memory"},{id:"pipeline",label:"Spatial pipeline",title:"One spatial pipeline",kicker:"Execution streams continuously across the processor",body:["A single RDU processes inference through its spatial pipeline: the operators — RMSNorm, QKV, Attention, Projection, FFN — are laid out across the grid, with execution streaming continuously across the processor.","Multiple switches, one pipeline: PCUs compute, PMUs hold data right next to them, and the mesh switches route between them — collectives pipeline with compute and terminate in SRAM."],terms:[{term:"Spatial pipeline",def:"The whole model mapped across the grid, execution streaming through it."},{term:"Operator fusion",def:"All decoders in one kernel call — no launch or synchronization overhead."}],duration:18,camera:{pos:[1,16,12.5],look:[0,0,-.3]},shift:.13,dies:1,tokens:!0,memoryFlow:!1,gpuLane:!1},{id:"gpu",label:"vs kernel by kernel",title:"Kernel by kernel, or spatial",kicker:"Inference depends on efficient data movement",body:["On GPUs, inference runs one kernel at a time, with intermediate data sent back to memory before the next operation begins. That adds delay, burns power, and at scale turns latency into the bottleneck.","RDUs use spatial execution to keep compute and communication moving together — collectives pipeline with compute and terminate in SRAM.","The result: faster inference, dramatically lower energy usage, and higher utilization at scale."],terms:[{term:"Kernel launch",def:"A GPU runs one kernel at a time; each launch and each all-reduce adds overhead between steps."},{term:"Spatial execution",def:"The pipeline is laid out across the chip; compute and communication overlap instead of alternating."}],duration:18,camera:{pos:[0,19,23],look:[0,0,4]},shift:.12,dies:1,tokens:!0,memoryFlow:!1,gpuLane:!0},{id:"system",label:"16 RDUs",title:"16 RDUs, one system",kicker:"That execution extends across the system",body:["When 16 RDUs are connected, the spatial pipeline extends across the system. Together they operate as one unified inference engine built to run large models efficie

5ntly at scale.","AGCUs link each chip to the others and to memory; the scale-up domain runs on 800G Ethernet, 16 RDUs to a rack."],terms:[{term:"Scale-up domain",def:"Up to 256 RDUs on the 800G scale-up network, 16 per rack."}],duration:16,camera:{pos:[12,108,121],look:[12,0,11]},shift:.11,dies:16,tokens:!0,memoryFlow:!1,gpuLane:!1},{id:"parallelism",label:"Parallelism",title:"Parallelism at multiple levels",kicker:"Pipeline · tensor · expert — at once",body:["Pipeline parallelism keeps different stages of execution moving in sequence.","Tensor parallelism splits a single operation across chips at the same time.","Expert parallelism distributes sparse model workloads efficiently across the architecture."],duration:24,camera:{pos:[12,136,85],look:[12,0,9]},shift:.11,dies:16,tokens:!1,memoryFlow:!1,gpuLane:!1,parallelism:"pipeline"},{id:"scale",label:"256 RDUs",title:"256 RDUs across racks",kicker:"Only 16 per rack — thousands of tokens in parallel",body:["At full scale, 256 RDUs work 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