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Add blog post: Running a big LLM across multiple GPUs with vLLM #265

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58de3d4
Add blog post: Running a single LLM across two GPUs with vLLM
saiyam1814 Aug 18, 2026
8b1d2a5
Drop draft flag so the post renders on the Cloudflare preview
saiyam1814 Aug 18, 2026
649688c
Rework: big model across multiple GPUs, measured on 4 cards
saiyam1814 Aug 18, 2026
a6ed090
Answer the shard question: fixed at upload, and why 5-10GB is the norm
saiyam1814 Aug 18, 2026
81bd69c
Regenerate feeds for the shard section
saiyam1814 Aug 18, 2026
2eb05a8
Restructure the post into a runbook track and a deep-dive track so ac…
shkatara Aug 23, 2026
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290 changes: 290 additions & 0 deletions components/MoeExpertRoutingAnimation.jsx
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Original file line number Diff line number Diff line change
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const gpus = [0, 1, 2, 3];
const PER_GPU = 32;

export default function MoeExpertRoutingAnimation() {
return (
<figure className="moeroute" aria-labelledby="moeroute-title">
<style>{`
.moeroute {
margin: 2rem 0;
border: 1px solid var(--border-subtle);
border-radius: 8px;
background:
linear-gradient(135deg, rgba(168, 85, 247, 0.1), rgba(5, 202, 255, 0.08)),
var(--bg-card);
box-shadow: var(--card-shadow);
overflow: hidden;
}

.moeroute * { box-sizing: border-box; }
.moeroute__inner { padding: clamp(1rem, 3vw, 1.5rem); }

.moeroute__eyebrow {
margin: 0 0 0.35rem;
color: var(--accent);
font-size: 0.72rem;
font-weight: 800;
text-transform: uppercase;
}

.moeroute__title {
margin: 0;
color: var(--text-primary);
font-size: clamp(1.15rem, 2.5vw, 1.55rem);
line-height: 1.2;
}

.moeroute__subtitle {
max-width: 46rem;
margin: 0.55rem 0 1.35rem;
color: var(--text-secondary);
font-size: 0.95rem;
line-height: 1.6;
}

.moeroute__stage {
display: grid;
gap: 0.8rem;
padding: 1rem;
border: 1px solid var(--border-subtle);
border-radius: 8px;
background:
linear-gradient(var(--border-subtle) 1px, transparent 1px),
linear-gradient(90deg, var(--border-subtle) 1px, transparent 1px),
color-mix(in srgb, var(--bg-elevated) 86%, transparent);
background-size: 42px 42px;
}

.moeroute__token {
padding: 0.55rem 0.7rem;
border: 1px dashed color-mix(in srgb, var(--accent) 50%, var(--border-medium));
border-radius: 8px;
background: color-mix(in srgb, var(--accent) 8%, var(--bg-card));
color: var(--text-primary);
font-size: 0.78rem;
font-weight: 900;
text-align: center;
}

.moeroute__token em {
display: block;
margin-top: 0.15rem;
color: var(--text-muted);
font-size: 0.65rem;
font-style: normal;
font-weight: 800;
}

.moeroute__router {
padding: 0.45rem;
border: 1px solid color-mix(in srgb, #a855f7 45%, var(--border-subtle));
border-radius: 6px;
background: color-mix(in srgb, #a855f7 9%, var(--bg-card));
color: color-mix(in srgb, #a855f7 85%, var(--text-primary));
font-size: 0.72rem;
font-weight: 950;
text-align: center;
}

.moeroute__gpus {
display: grid;
grid-template-columns: repeat(4, minmax(0, 1fr));
gap: 0.55rem;
}

.moeroute__gpu {
min-width: 0;
padding: 0.55rem;
border: 1px solid color-mix(in srgb, var(--c) 42%, var(--border-subtle));
border-radius: 7px;
background: color-mix(in srgb, var(--c) 6%, var(--bg-card));
}

.moeroute__gpu-head {
margin-bottom: 0.4rem;
color: var(--text-primary);
font-size: 0.72rem;
font-weight: 950;
}

.moeroute__gpu-head em {
display: block;
color: var(--text-muted);
font-size: 0.58rem;
font-style: normal;
font-weight: 800;
}

.moeroute__experts {
display: grid;
grid-template-columns: repeat(8, 1fr);
gap: 2px;
}

.moeroute__exp {
aspect-ratio: 1;
border-radius: 2px;
background: color-mix(in srgb, var(--c) 16%, var(--bg-elevated));
}

.moeroute__exp[data-hot='yes'] {
background: color-mix(in srgb, var(--c) 78%, transparent);
animation: moeFire 4s ease-in-out infinite;
animation-delay: var(--d);
}

.moeroute__count {
margin-top: 0.4rem;
color: var(--text-secondary);
font-size: 0.62rem;
font-weight: 850;
text-align: center;
}

.moeroute__note {
display: grid;
grid-template-columns: repeat(3, minmax(0, 1fr));
gap: 0.55rem;
}

.moeroute__cell {
min-width: 0;
padding: 0.65rem;
border: 1px solid var(--border-subtle);
border-radius: 7px;
background: var(--bg-card);
}

.moeroute__k {
display: block;
color: var(--text-muted);
font-size: 0.58rem;
font-weight: 900;
text-transform: uppercase;
}

.moeroute__v {
display: block;
margin-top: 0.18rem;
color: var(--accent);
font-size: 0.9rem;
font-weight: 950;
}

.moeroute__d {
display: block;
margin-top: 0.22rem;
color: var(--text-muted);
font-size: 0.63rem;
line-height: 1.35;
}

.moeroute figcaption {
padding: 0 1.5rem 1.15rem;
color: var(--text-muted);
font-size: 0.78rem;
line-height: 1.45;
}

@keyframes moeFire {
0%, 100% { opacity: 0.25; transform: scale(0.9); }
35%, 55% { opacity: 1; transform: scale(1); }
}

@media (max-width: 700px) {
.moeroute__gpus { grid-template-columns: 1fr 1fr; }
.moeroute__note { grid-template-columns: 1fr; }
}

@media (prefers-reduced-motion: reduce) {
.moeroute__exp[data-hot='yes'] {
animation-duration: 0.001ms !important;
animation-iteration-count: 1 !important;
opacity: 1;
transform: scale(1);
}
}
`}</style>
<div className="moeroute__inner">
<p className="moeroute__eyebrow">Expert routing animation</p>
<h3 className="moeroute__title" id="moeroute-title">
Why a 235B model only does 22B of work per token
</h3>
<p className="moeroute__subtitle">
Every layer of this model has 128 small expert networks, and a tiny router picks just 8 of
them for each token. The other 120 sit still. That is the whole trick of a mixture of
experts: you pay for 235B parameters in memory, but only about 22B of arithmetic per token.
</p>

<div className="moeroute__stage" role="img" aria-label="A token being routed to 8 of 128 experts spread over 4 GPUs">
<div className="moeroute__token">
one token arrives
<em>it has already been through attention for this layer</em>
</div>

<div className="moeroute__router">router scores all 128 experts, keeps the top 8</div>

<div className="moeroute__gpus">
{gpus.map((gpu) => {
// 2 of this GPU's 32 experts are picked, so 8 across 4 GPUs
const hot = [3 + gpu, 18 + ((gpu * 5) % 10)];
return (
<div
className="moeroute__gpu"
key={gpu}
style={{ '--c': gpu % 2 === 0 ? '#a855f7' : '#0098cc' }}
>
<p className="moeroute__gpu-head">
GPU {gpu}
<em>experts {gpu * PER_GPU}-{gpu * PER_GPU + PER_GPU - 1}</em>
</p>
<div className="moeroute__experts" aria-hidden="true">
{Array.from({ length: PER_GPU }, (_, i) => (
<div
className="moeroute__exp"
key={i}
data-hot={hot.includes(i) ? 'yes' : 'no'}
style={{ '--d': `${gpu * 0.25}s` }}
/>
))}
</div>
<p className="moeroute__count">2 of 32 firing</p>
</div>
);
})}
</div>

<div className="moeroute__note">
<div className="moeroute__cell">
<span className="moeroute__k">In memory</span>
<span className="moeroute__v">235B params</span>
<span className="moeroute__d">
All 128 experts per layer must be resident, which is why the model is big
</span>
</div>
<div className="moeroute__cell">
<span className="moeroute__k">Active per token</span>
<span className="moeroute__v">22B params</span>
<span className="moeroute__d">
Only the 8 chosen experts do arithmetic, so it runs like a much smaller model
</span>
</div>
<div className="moeroute__cell">
<span className="moeroute__k">What this costs you</span>
<span className="moeroute__v">a network hop</span>
<span className="moeroute__d">
With expert parallelism the token travels to whichever GPU owns its expert, then the
answer travels back
</span>
</div>
</div>
</div>
</div>
<figcaption>
Counts are from the model config: 128 experts per layer, 8 per token, 94 layers. Splitting 128
experts over 4 GPUs gives 32 each, so on average 2 experts per GPU fire for any given token.
That average is the catch, because routing is not guaranteed to be even.
</figcaption>
</figure>
);
}
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