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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>nano-vLLM · Ch05 · The Scheduler</title>
<meta name="description" content="Continuous batching explained from scratch — why static batching wastes the GPU, how the scheduler runs a step-by-step decision loop, and what preemption means for memory pressure.">
<meta property="og:title" content="nano-vLLM Ch05 — The Scheduler">
<meta property="og:description" content="How nano-vLLM's scheduler keeps the GPU busy at all times — the waiting queue, the running set, state transitions, and what happens when memory runs out.">
<link rel="preconnect" href="https://fonts.googleapis.com">
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.sched-item{padding:0.6rem 1rem;font-size:0.82rem;border-bottom:1px solid rgba(255,255,255,0.04);display:flex;align-items:center;gap:0.6rem;transition:all 0.3s;}
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/* STEP ROW */
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/* QUIZ */
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<a href="ch01.html">01 · Inference</a>
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<a href="ch03.html">03 · KV Cache</a>
<a href="ch04.html">04 · PagedAttention</a>
<a href="ch05.html" class="active">05 · Scheduler</a>
<a href="ch06.html">06 · Prefill vs Decode</a>
<a href="ch07.html">07 · Prefix Caching</a>
<a href="ch08.html">08 · Sampling</a>
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<div class="series-badge">Chapter 05 of 11 · nano-vLLM Deep Dive</div>
<div class="chapter-num">05</div>
<h1>The Scheduler</h1>
<p class="hero-sub">How nano-vLLM keeps the GPU busy every single step — the waiting queue, the running set, state transitions, and what happens when memory runs out.</p>
<div class="hero-nav">
<a href="ch04.html" class="btn-ghost">← Ch04: PagedAttention</a>
<a href="ch06.html" class="btn-primary">Next: Prefill vs Decode →</a>
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<main>
<!-- ══════════════════════════════
SECTION 1 — OPENING ANALOGY
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<section class="section">
<div class="section-label">Section 1 — The Big Picture</div>
<h2>What is a scheduler, and why does it matter?</h2>
<p class="lead">The GPU is enormously powerful, but expensive and finite. Left unmanaged, it would sit idle between requests, or waste cycles waiting for a slow request while others queue up. The scheduler's one job is to make sure the GPU is <em>always working</em> — processing the right set of requests at every moment, no idle time, no wasted capacity.</p>
<div class="callout insight">
<strong>The Subway Train Analogy</strong>
Imagine two ways to run a subway line. The <strong>old way</strong> (static batching): the train waits at the station until every seat is filled, then departs — and doesn't open its doors again until it returns to the station, no matter how many people are waiting on the platform. If three passengers get off halfway, those seats sit empty for the rest of the journey. The <strong>new way</strong> (continuous batching): the train runs on a loop with rolling doors — at every station it lets off passengers who've arrived at their destination, and immediately lets on new passengers to fill those exact seats. The train is always full and always moving. The scheduler is the train controller. Requests are passengers. The GPU is the train.
</div>
<p class="lead">This analogy captures the core insight: the old way (static batching) wastes capacity by holding fixed seats for the full journey. The new way (continuous batching) recycles seats the moment they free up. The rest of this chapter explains exactly how nano-vLLM implements the new way.</p>
</section>
<div class="divider"></div>
<!-- ══════════════════════════════
SECTION 2 — STATIC VS CONTINUOUS BATCHING
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<section class="section">
<div class="section-label">Section 2 — Static vs Continuous Batching</div>
<h2>The problem with waiting for everyone</h2>
<h3>What is a "batch"?</h3>
<p class="lead">A <strong style="color:var(--text)">batch</strong> is a group of requests processed together in a single GPU forward pass. GPUs are designed for parallel computation — running 10 requests simultaneously is barely slower than running 1, up to the hardware limit. So the goal is always to process as many requests as possible at once. The question is: <em>which</em> requests, and <em>when</em>?</p>
<h3>Static batching — the old approach</h3>
<p class="lead">In <strong style="color:var(--text)">static batching</strong>, a fixed batch of requests is assembled once and processed together from start to finish. Every request in the batch must complete before any new requests can join. If you start with 8 requests and 3 finish early, the GPU processes a batch of 5 for the rest — with 3 slots sitting empty.</p>
<p class="lead">Why is this bad? Consider a real scenario: Request A generates 500 tokens (a long essay). Requests B–H each generate 10 tokens (short answers). B–H finish quickly, but the entire batch is held open until A finishes its 500th token. The GPU runs at partial capacity — maybe 15% utilisation — for 490 token steps while waiting for A.</p>
<!-- GPU Utilisation comparison -->
<div class="diagram">
<div class="diagram-label">GPU utilisation over time</div>
<p style="font-size:0.82rem;color:var(--muted);margin-bottom:1.25rem;">8 requests: 1 long (500 tokens) + 7 short (10 tokens each). Watch how utilisation collapses with static batching but stays high with continuous batching.</p>
<div class="util-row">
<div class="util-header"><span>Static batching — GPU utilisation over 500 steps</span><span style="color:var(--accent2);">~23% average</span></div>
<div class="util-track">
<div class="util-busy" style="width:14%;background:var(--accent3);">Steps 1–10</div>
<div class="util-idle" style="flex:1;background:rgba(255,77,109,0.12);color:var(--accent2);">IDLE — 7 empty slots waiting for Request A (steps 11–500)</div>
</div>
</div>
<div class="util-row" style="margin-top:1rem;">
<div class="util-header"><span>Continuous batching — GPU utilisation over 500 steps</span><span style="color:var(--accent3);">~95% average</span></div>
<div class="util-track">
<div class="util-busy" style="width:14%;background:var(--accent3);">All 8 requests</div>
<div class="util-busy" style="width:82%;background:var(--accent);">New requests fill freed slots instantly — stays near full</div>
<div class="util-idle" style="width:4%;background:rgba(255,255,255,0.05);"></div>
</div>
</div>
<p style="font-size:0.75rem;color:var(--muted);margin-top:0.85rem;line-height:1.6;">With continuous batching, the moment B–H finish at step 10, 7 new requests from the waiting queue fill those slots. The GPU never runs at partial capacity. The throughput difference is not marginal — it can be 5–10× more requests served per second on identical hardware.</p>
</div>
<h3>Continuous batching — the key idea</h3>
<p class="lead"><strong style="color:var(--text)">Continuous batching</strong> treats the set of running requests as a living, breathing pool. After <em>every single decode step</em>, the scheduler checks: did anyone finish? If yes — free their blocks immediately and pull in the next waiting request. The batch composition changes at every step. No request holds a "seat" beyond what it needs right now.</p>
<div class="callout info">
<strong>Why "after every step" and not "after every N steps"?</strong>
Because tokens complete at unpredictable times. You don't know when a request will emit an end-of-sequence token. If you checked only every 10 steps, a request that finished at step 3 would hold a GPU slot idle for 7 unnecessary steps. Checking every step means the minimum possible idle time — at the cost of a tiny scheduling overhead per step, which is negligible compared to the GPU compute saved.
</div>
</section>
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SECTION 3 — THE STATE MACHINE
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<section class="section">
<div class="section-label">Section 3 — The State Machine</div>
<h2>A request's lifecycle — four states</h2>
<p class="lead">Every request in nano-vLLM is a <strong style="color:var(--text)">Sequence</strong> object <span class="xref">→ Ch.02</span> with a <code>status</code> field. This status is a <strong style="color:var(--text)">state machine</strong> — a formal way of saying the request can only be in one of four defined states, and transitions between them follow strict rules. The scheduler reads and writes this status on every step.</p>
<p class="lead">A <strong style="color:var(--text)">state machine</strong> is like a traffic light: it can only be red, yellow, or green — never two at once — and it can only change in specific ways (red → green, not red → yellow). State machines make complex behaviour predictable and debuggable. Here are the four states a request moves through:</p>
<!-- State machine visual -->
<div class="diagram">
<div class="diagram-label">Click a state to understand it</div>
<div class="state-machine" id="state-machine">
<div class="state-node" data-state="waiting" style="border-color:var(--muted);">
<span class="s-name" style="color:var(--muted);">WAITING</span>
<span class="s-desc">In queue</span>
</div>
<div class="state-arrow">
<div class="s-line" style="background:var(--accent4);"></div>
<div class="s-label">blocks available</div>
</div>
<div class="state-node" data-state="prefill" style="border-color:var(--accent2);">
<span class="s-name" style="color:var(--accent2);">PREFILL</span>
<span class="s-desc">Processing prompt</span>
</div>
<div class="state-arrow">
<div class="s-line" style="background:var(--accent4);"></div>
<div class="s-label">prompt done</div>
</div>
<div class="state-node" data-state="decoding" style="border-color:var(--accent4);">
<span class="s-name" style="color:var(--accent4);">DECODING</span>
<span class="s-desc">Generating tokens</span>
</div>
<div class="state-arrow">
<div class="s-line" style="background:var(--accent4);"></div>
<div class="s-label">EOS or max_tokens</div>
</div>
<div class="state-node" data-state="finished" style="border-color:var(--accent3);">
<span class="s-name" style="color:var(--accent3);">FINISHED</span>
<span class="s-desc">Done</span>
</div>
</div>
<div class="state-detail" id="state-detail">← Click any state above to understand what happens in it and what triggers the transition.</div>
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</section>
<div class="divider"></div>
<!-- ══════════════════════════════
SECTION 4 — THE SCHEDULE() LOOP
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<section class="section">
<div class="section-label">Section 4 — The Schedule() Loop</div>
<h2>What the scheduler does on every single step</h2>
<p class="lead">The scheduler's <code>schedule()</code> method is called once per engine step <span class="xref">→ Ch.02</span> — which means once per generated token across the entire running batch. It executes five actions in a fixed order. Understanding this order is the key to understanding the whole system.</p>
<div class="step-row">
<div class="step-badge" style="background:rgba(255,77,109,0.15);border:1px solid rgba(255,77,109,0.4);color:var(--accent2);">1</div>
<div>
<h4>Collect finished sequences — free their blocks immediately</h4>
<p>First, the scheduler scans the running set for any sequence whose last generated token was an end-of-sequence (EOS) token, or that has reached <code>max_tokens</code>. For each finished sequence: call <code>block_manager.free(seq)</code> <span class="xref">→ Ch.04</span> to return its blocks to the free list, then move the sequence to FINISHED. This happens before anything else — freed blocks can immediately be used by new requests in the same step.</p>
</div>
</div>
<div class="step-row">
<div class="step-badge" style="background:rgba(255,179,71,0.15);border:1px solid rgba(255,179,71,0.4);color:var(--accent4);">2</div>
<div>
<h4>Append new slots for decoding sequences</h4>
<p>Every sequence in DECODING is about to generate one more token. That new token needs a slot in the KV cache <span class="xref">→ Ch.03</span>. The scheduler calls <code>block_manager.append_slot(seq)</code> for each decoding sequence — if the sequence has just crossed a block boundary (filled its last block), a new block is allocated from the free list. If no free blocks are available, this triggers preemption (step 4b below).</p>
</div>
</div>
<div class="step-row">
<div class="step-badge" style="background:rgba(0,229,255,0.15);border:1px solid rgba(0,229,255,0.4);color:var(--accent);">3</div>
<div>
<h4>Promote waiting → running (prefill)</h4>
<p>With freed and newly available blocks accounted for, the scheduler tries to promote requests from the waiting queue into the running set. For each waiting sequence: call <code>block_manager.allocate(seq)</code>. If it returns <code>True</code>, the sequence moves to PREFILL and joins this step's batch. If it returns <code>False</code> (not enough free blocks), stop trying — all remaining waiting sequences stay queued.</p>
</div>
</div>
<div class="step-row">
<div class="step-badge" style="background:rgba(255,77,109,0.15);border:1px solid rgba(255,77,109,0.4);color:var(--accent2);">4</div>
<div>
<h4>Preempt if memory is critically low (optional)</h4>
<p><strong style="color:var(--text)">Preemption</strong> is the scheduler's emergency valve. If step 2 fails because there are no free blocks for a decoding sequence, the scheduler must act. It selects a lower-priority running sequence, calls <code>block_manager.free()</code> on it, and moves it back to WAITING. Its KV cache data is lost — it will have to redo its prefill step when re-scheduled. This is expensive, but it prevents the engine from crashing with an out-of-memory error.</p>
</div>
</div>
<div class="step-row">
<div class="step-badge" style="background:rgba(180,255,111,0.15);border:1px solid rgba(180,255,111,0.4);color:var(--accent3);">5</div>
<div>
<h4>Return the batch — prefill sequences + decode sequences</h4>
<p>Finally, the scheduler returns a <code>SchedulerOutput</code> object to the engine with two lists: sequences in PREFILL (processing their prompt for the first time) and sequences in DECODING (each generating their next token). The model runner receives this and executes one GPU forward pass covering all of them simultaneously.</p>
</div>
</div>
<div class="callout warn">
<strong>Prefill and decode run in the same forward pass</strong>
A single call to the transformer model can process prefill sequences and decode sequences in the same batch. They use different attention variants internally (flash_attn_varlen_func for prefill, flash_attn_with_kvcache for decode <span class="xref">→ Ch.10</span>), but from the scheduler's perspective they're all just "sequences to process this step." This is called <strong>chunked prefill</strong> or <strong>mixed batching</strong>, and it's what makes continuous batching so effective — you're never waiting for prefill to finish before decode can start.
</div>
</section>
<div class="divider"></div>
<!-- ══════════════════════════════
SECTION 5 — INTERACTIVE STEPPER
══════════════════════════════ -->
<section class="section">
<div class="section-label">Section 5 — Live Scheduler Simulation</div>
<h2>Step through the scheduler — see every decision</h2>
<p class="lead">Click <strong style="color:var(--text)">Run One Step</strong> to advance the scheduler one step at a time. Watch requests move between the waiting queue and running set, blocks allocate and free, and states transition. The event log explains every decision the scheduler makes.</p>
<div class="diagram" style="padding:1.5rem;">
<div class="diagram-label">Scheduler simulator</div>
<!-- Stats -->
<div style="display:grid;grid-template-columns:repeat(4,1fr);gap:0.75rem;margin-bottom:1.25rem;">
<div style="background:var(--surface2);border:1px solid var(--border);border-radius:4px;padding:0.75rem;text-align:center;">
<div style="font-family:'Space Mono',monospace;font-size:1.2rem;font-weight:700;color:var(--accent4);" id="ss-step">0</div>
<div style="font-size:0.62rem;color:var(--muted);margin-top:0.2rem;">Engine step</div>
</div>
<div style="background:var(--surface2);border:1px solid var(--border);border-radius:4px;padding:0.75rem;text-align:center;">
<div style="font-family:'Space Mono',monospace;font-size:1.2rem;font-weight:700;color:var(--accent3);" id="ss-blocks">20</div>
<div style="font-size:0.62rem;color:var(--muted);margin-top:0.2rem;">Free blocks</div>
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<div style="background:var(--surface2);border:1px solid var(--border);border-radius:4px;padding:0.75rem;text-align:center;">
<div style="font-family:'Space Mono',monospace;font-size:1.2rem;font-weight:700;color:var(--accent);" id="ss-running">0</div>
<div style="font-size:0.62rem;color:var(--muted);margin-top:0.2rem;">Running</div>
</div>
<div style="background:var(--surface2);border:1px solid var(--border);border-radius:4px;padding:0.75rem;text-align:center;">
<div style="font-family:'Space Mono',monospace;font-size:1.2rem;font-weight:700;color:var(--accent2);" id="ss-finished">0</div>
<div style="font-size:0.62rem;color:var(--muted);margin-top:0.2rem;">Finished</div>
</div>
</div>
<!-- Queues -->
<div class="sched-stepper">
<div class="sched-queue">
<div class="sched-queue-title">
<span style="color:var(--muted);">⏳ WAITING QUEUE</span>
<span style="color:var(--muted);font-size:0.6rem;" id="ss-wait-count">0 requests</span>
</div>
<div id="ss-waiting" style="min-height:60px;"></div>
</div>
<div class="sched-queue">
<div class="sched-queue-title">
<span style="color:var(--accent4);">▶ RUNNING SET</span>
<span style="color:var(--muted);font-size:0.6rem;" id="ss-run-count">0 requests</span>
</div>
<div id="ss-running-list" style="min-height:60px;"></div>
</div>
</div>
<!-- Controls -->
<div style="display:flex;gap:0.6rem;flex-wrap:wrap;margin-bottom:0.85rem;">
<button onclick="ssStep()" id="ss-btn" style="font-family:'Space Mono',monospace;font-size:0.65rem;padding:0.5rem 1.1rem;background:rgba(255,179,71,0.15);border:1px solid rgba(255,179,71,0.4);color:var(--accent4);border-radius:3px;cursor:pointer;font-weight:700;">▶ Run One Step</button>
<button onclick="ssReset()" style="font-family:'Space Mono',monospace;font-size:0.65rem;padding:0.5rem 1rem;background:transparent;border:1px solid var(--border);color:var(--muted);border-radius:3px;cursor:pointer;">↺ Reset</button>
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<!-- Event log -->
<div style="background:var(--code-bg);border:1px solid var(--border);border-radius:4px;padding:0.85rem;max-height:160px;overflow-y:auto;">
<div style="font-family:'Space Mono',monospace;font-size:0.58rem;letter-spacing:0.08em;color:var(--muted);margin-bottom:0.5rem;text-transform:uppercase;">Scheduler event log</div>
<div id="ss-log" style="font-family:'Space Mono',monospace;font-size:0.65rem;line-height:2;"></div>
</div>
</div>
</section>
<div class="divider"></div>
<!-- ══════════════════════════════
SECTION 6 — PREEMPTION DEEP DIVE
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<section class="section">
<div class="section-label">Section 6 — Memory Pressure</div>
<h2>Preemption — when memory runs out</h2>
<p class="lead">What happens if the GPU runs out of free KV cache blocks while sequences are still generating? The scheduler can't crash — that would mean every user's request fails. Instead, it uses <strong style="color:var(--text)">preemption</strong> to recover gracefully.</p>
<h3>What preemption means</h3>
<p class="lead">Preemption means pausing a currently-running sequence mid-generation, freeing its KV cache blocks back to the pool, and moving it back to the WAITING queue. Its blocks are gone — the KV data is lost. When the sequence gets re-scheduled later, it must re-run its prefill phase from scratch to rebuild its KV cache.</p>
<div class="callout insight">
<strong>The Overbooked Flight Analogy</strong>
Preemption is like an airline bumping a standby passenger off a flight that's overbooked. The passenger (sequence) was already seated (running), but a higher-priority passenger needs the seat (memory blocks). The bumped passenger gets a voucher (is moved back to waiting) and will board the next available flight (get re-scheduled when blocks free up). It's disruptive and costly — they have to re-board — but it keeps the airline (engine) operating rather than cancelling all flights.
</div>
<h3>The preemption cost — why it's a last resort</h3>
<p class="lead">Preemption is expensive for two reasons. First, the evicted sequence must redo its entire prefill phase when re-scheduled — which might be hundreds of tokens of GPU compute. Second, it adds latency for the affected user. The scheduler tries to avoid preemption by promoting waiting requests conservatively — only adding new requests if there's clearly enough memory for them to run to completion without triggering preemption.</p>
<div class="card-grid">
<div class="card" style="--card-accent:var(--accent4);">
<h4>Preemption priority</h4>
<p>nano-vLLM preempts the sequence that arrived most recently (last-in, first-out). The intuition: requests that just started have used the least compute. Evicting them wastes less work than evicting a sequence that has already generated 200 tokens.</p>
</div>
<div class="card" style="--card-accent:var(--accent2);">
<h4>Swap vs recompute</h4>
<p>Production vLLM can swap KV cache blocks to CPU RAM instead of discarding them — expensive but avoids re-prefill. nano-vLLM takes the simpler path: discard and recompute. Sufficient for its scope, and far easier to reason about.</p>
</div>
<div class="card" style="--card-accent:var(--accent3);">
<h4>Prevention is better</h4>
<p>The best preemption is the one that never happens. nano-vLLM's scheduler is conservative: it won't promote a waiting request if doing so would leave fewer free blocks than the expected decode length. This is imperfect (decode length is unknown) but keeps preemption rare.</p>
</div>
</div>
<div class="callout info">
<strong>Fairness and starvation</strong>
The waiting queue is strictly FIFO — requests are served in arrival order. Combined with the LIFO preemption policy (newest evicted first), this protects long-running requests: older sequences that have already invested significant compute are the last to be evicted and the first to be re-scheduled. This means no request can be starved indefinitely — it advances in queue priority with every scheduling round.
</div>
</section>
<div class="divider"></div>
<!-- ══════════════════════════════
SECTION 7 — NANO-VLLM CODE
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<section class="section">
<div class="section-label">Section 7 — In nano-vLLM</div>
<h2>The scheduler in code</h2>
<p class="lead">Here is nano-vLLM's <code>schedule()</code> method, annotated step by step. Notice how short it is — the complexity lives in the BlockManager, not here.</p>
<div class="code-label">core/scheduler.py — schedule() method</div>
<pre><span class="keyword">def</span> <span class="fn">schedule</span>(self) -> <span class="cls">SchedulerOutput</span>:
<span class="comment"># ── STEP 1: collect finished sequences and free their blocks ──</span>
finished = [s <span class="keyword">for</span> s <span class="keyword">in</span> self.running <span class="keyword">if</span> s.<span class="fn">is_finished</span>()]
<span class="keyword">for</span> s <span class="keyword">in</span> finished:
self.block_mgr.<span class="fn">free</span>(s) <span class="comment"># return blocks to free list immediately</span>
self.running.<span class="fn">remove</span>(s)
<span class="comment"># ── STEP 2: append a new slot for every decoding sequence ──</span>
<span class="comment"># Each decode step needs space for 1 new token's K and V vectors</span>
<span class="keyword">for</span> s <span class="keyword">in</span> self.running:
<span class="keyword">if not</span> s.is_prefill:
ok = self.block_mgr.<span class="fn">append_slot</span>(s)
<span class="keyword">if not</span> ok:
self.<span class="fn">_preempt</span>(s) <span class="comment"># no free blocks → preempt this sequence</span>
<span class="comment"># ── STEP 3: promote waiting → running while blocks are available ──</span>
promoted = []
<span class="keyword">while</span> self.waiting:
seq = self.waiting[<span class="num">0</span>] <span class="comment"># peek at the front of the queue</span>
<span class="keyword">if</span> self.block_mgr.<span class="fn">allocate</span>(seq):
self.waiting.<span class="fn">pop</span>(<span class="num">0</span>) <span class="comment"># remove from waiting queue</span>
self.running.<span class="fn">append</span>(seq) <span class="comment"># add to running set</span>
promoted.<span class="fn">append</span>(seq)
<span class="keyword">else</span>:
<span class="keyword">break</span> <span class="comment"># no memory for this request — stop trying</span>
<span class="comment"># ── STEP 5: return split batch to the engine ──</span>
<span class="keyword">return</span> <span class="cls">SchedulerOutput</span>(
prefill=[s <span class="keyword">for</span> s <span class="keyword">in</span> self.running <span class="keyword">if</span> s.is_prefill],
decode =[s <span class="keyword">for</span> s <span class="keyword">in</span> self.running <span class="keyword">if not</span> s.is_prefill],
)</pre>
<div class="code-label">core/scheduler.py — _preempt() helper</div>
<pre><span class="keyword">def</span> <span class="fn">_preempt</span>(self, seq: <span class="cls">Sequence</span>) -> <span class="cls">None</span>:
<span class="comment"># Emergency valve: free this sequence's blocks and send it back to waiting
# It will re-run prefill from scratch when re-scheduled</span>
self.block_mgr.<span class="fn">free</span>(seq) <span class="comment"># return all blocks to free list</span>
seq.output_tokens = [] <span class="comment"># discard generated tokens so far</span>
seq.status = SequenceStatus.WAITING
self.running.<span class="fn">remove</span>(seq)
self.waiting.<span class="fn">appendleft</span>(seq) <span class="comment"># push to front of queue (high priority)</span></pre>
<div class="callout info">
<strong>Why waiting is a deque, not a list</strong>
The waiting queue uses Python's <code>deque</code> (double-ended queue) rather than a regular list. This allows O(1) <code>appendleft()</code> — pushing a preempted sequence back to the <em>front</em> of the queue so it gets re-scheduled first. A regular list's <code>insert(0, item)</code> is O(N), which becomes slow with hundreds of waiting requests. Small detail, real performance impact.
</div>
</section>
<div class="divider"></div>
<!-- ══════════════════════════════
SECTION 8 — WHY IT MATTERS
══════════════════════════════ -->
<section class="section">
<div class="section-label">Section 8 — Why It Matters</div>
<h2>What continuous batching enables</h2>
<div class="card-grid">
<div class="card" style="--card-accent:var(--accent4);">
<h4>5–10× throughput improvement</h4>
<p>Real-world benchmarks show continuous batching delivers 5–10× higher request throughput over static batching on the same hardware, for workloads with mixed response lengths — which is every production workload.</p>
</div>
<div class="card" style="--card-accent:var(--accent3);">
<h4>Lower average latency</h4>
<p>Short requests no longer wait for long ones. A 10-token response that arrives behind a 500-token request gets a GPU slot the moment one frees up — often within milliseconds, not seconds.</p>
</div>
<div class="card" style="--card-accent:var(--accent);">
<h4>Works hand-in-hand with PagedAttention</h4>
<p>Continuous batching only works because PagedAttention <span class="xref">→ Ch.04</span> makes block allocation O(1) and fragmentation-free. Without PagedAttention, adding/removing requests mid-batch would require expensive memory reorganisation at every step.</p>
</div>
<div class="card" style="--card-accent:var(--accent2);">
<h4>Graceful degradation under load</h4>
<p>When the system is overloaded, preemption prevents crashes. Requests wait or retry rather than failing. The engine degrades gracefully: slower responses, never broken ones.</p>
</div>
</div>
</section>
<div class="divider"></div>
<!-- ══════════════════════════════
SECTION 9 — MISCONCEPTIONS
══════════════════════════════ -->
<section class="section">
<div class="section-label">Section 9 — Common Mistakes</div>
<h2>Things beginners get wrong about the scheduler</h2>
<div class="misconception">
<div class="myth">✗ Myth 1 — "The scheduler runs once at the start and builds a plan"</div>
<div class="reality"><strong>Reality:</strong> The scheduler's <code>schedule()</code> runs on <em>every single engine step</em> — which means once per decode token across the batch. There is no upfront plan. Every step is a fresh decision based on the current state of the queues and the free block count. This is what makes it "continuous" — the batch composition can change at any step, not just at the start.</div>
</div>
<div class="misconception">
<div class="myth">✗ Myth 2 — "Preemption means the user's request fails"</div>
<div class="reality"><strong>Reality:</strong> Preemption is transparent to the user. Their request is moved back to the waiting queue and re-scheduled when memory is available. They experience a longer wait time — but their request eventually completes. The output is identical to a non-preempted run because the model deterministically regenerates the same tokens (with temperature=0) or equivalent ones (with sampling). The user never sees an error.</div>
</div>
<div class="misconception">
<div class="myth">✗ Myth 3 — "Continuous batching means all requests start and finish together"</div>
<div class="reality"><strong>Reality:</strong> That is the definition of <em>static</em> batching — the thing continuous batching replaces. In continuous batching, every request starts and finishes independently. A batch at step 47 might contain Request A (on token 200), Request B (on token 5), and Request C (just starting its prefill). They have nothing in common except that they're all being processed in the same GPU forward pass right now.</div>
</div>
</section>
<div class="divider"></div>
<!-- ══════════════════════════════
SECTION 10 — QUIZ
══════════════════════════════ -->
<section class="section">
<div class="section-label">Section 10 — Check Your Understanding</div>
<h2>Quiz</h2>
<p class="lead">Three questions to test your understanding of the scheduler. Wrong answers explain exactly why they're wrong.</p>
<div class="quiz-block">
<p class="q">1. In static batching, 8 requests start together. 7 finish at step 10, but 1 takes 500 steps. What happens to GPU utilisation between steps 11 and 500?</p>
<div class="quiz-opts">
<button onclick="quiz(1,'a',false,'In static batching, the batch is fixed until ALL requests finish. The 7 completed requests cannot be replaced by new ones until step 500. Those 7 GPU slots sit idle for 490 steps — wasting 87.5% of capacity during that period.')">New requests automatically fill the freed slots — utilisation stays high</button>
<button onclick="quiz(1,'b',true,'Correct. Static batching locks the batch composition until every member finishes. With 7 of 8 requests done at step 10, the GPU runs at 1/8 = 12.5% utilisation for steps 11–500. No new requests can enter. This is the core waste that continuous batching eliminates.')">The GPU runs at ~12.5% utilisation — 7 of 8 slots are empty and locked until step 500</button>
<button onclick="quiz(1,'c',false,'The GPU does not speed up to compensate. It simply does less work — processing 1 sequence instead of 8 while using the same power and occupying the same hardware. The wasted capacity cannot be recovered within that batch.')">The GPU runs faster because fewer requests means less work per step</button>
</div>
<div class="quiz-fb" id="fb1"></div>
</div>
<div class="quiz-block">
<p class="q">2. The scheduler checks for finished sequences as its FIRST action every step, before allocating blocks to new requests. Why does this order matter?</p>
<div class="quiz-opts">
<button onclick="quiz(2,'a',false,'Logging and bookkeeping can happen at any time and don\'t affect functionality. The ordering here is about memory: freeing blocks first means they can be reused in the same step. If you promoted new requests before freeing finished ones, you\'d have fewer available blocks than you actually have — potentially rejecting requests or triggering unnecessary preemption.')">It doesn\'t matter — it\'s just a bookkeeping convention</button>
<button onclick="quiz(2,'b',true,'Correct. By freeing finished sequences\' blocks first, those blocks become immediately available for the new requests being promoted in the same step. If the order were reversed — promote first, then free — the scheduler would have seen fewer free blocks than actually available, potentially refusing to admit requests that could have been served. The ordering maximises utilisation within each step.')">Freed blocks become available for new requests in the same step — maximising utilisation</button>
<button onclick="quiz(2,'c',false,'Checking for EOS tokens happens during the model forward pass and sampling — that\'s separate from the scheduler. By the time schedule() runs, the engine already knows which sequences are finished. The scheduler is just cleaning up and deciding what runs next.')">To check for EOS tokens before they get overwritten</button>
</div>
<div class="quiz-fb" id="fb2"></div>
</div>
<div class="quiz-block">
<p class="q">3. A preempted sequence is moved back to the front of the waiting queue (not the back). Why?</p>
<div class="quiz-opts">
<button onclick="quiz(3,'a',false,'Alphabetical ordering has nothing to do with the scheduler — it manages priorities based on memory and arrival order. Putting a preempted sequence at the front is about fairness and avoiding starvation: a sequence that was already running and lost its memory deserves priority over sequences that haven\'t started yet.')">So requests are processed in alphabetical order</button>
<button onclick="quiz(3,'b',false,'The preempted sequence is moved to WAITING, not FINISHED — it will be re-scheduled and run again. Moving it to the back would mean it waits behind all newly-arrived requests, potentially starving if the system is busy. The front placement gives it high priority to be re-admitted as soon as memory is available.')">It has already finished and is just being logged</button>
<button onclick="quiz(3,'c',true,'Correct. A preempted sequence has already done the hard work of prefill (or is about to have to redo it). Placing it at the front of the waiting queue gives it the highest priority to be re-scheduled the moment enough blocks free up — minimising the latency hit from preemption. If it were placed at the back, it could wait behind dozens of new arrivals, making the preemption extremely expensive in terms of added latency.')">It gets high-priority re-scheduling — sent to the front so it\'s admitted again as soon as memory is available</button>
</div>
<div class="quiz-fb" id="fb3"></div>
</div>
</section>
<div class="divider"></div>
<!-- ══════════════════════════════
SECTION 11 — TAKEAWAYS
══════════════════════════════ -->
<section class="section">
<div class="section-label">Section 11 — Key Takeaways</div>
<h2>What you now know</h2>
<div class="takeaways">
<div class="takeaways-label">Chapter 05 — Summary</div>
<div class="takeaway-grid">
<div class="takeaway-item">
<div class="takeaway-check">✓</div>
<p><strong>Static batching wastes the GPU.</strong> A fixed batch locks slots until the slowest request finishes. Mixed-length workloads cause utilisation to collapse to single digits within a few steps.</p>
</div>
<div class="takeaway-item">
<div class="takeaway-check">✓</div>
<p><strong>Continuous batching recycles slots instantly.</strong> After every single step, finished sequences free their blocks and new requests fill the vacated slots. Utilisation stays near 100%.</p>
</div>
<div class="takeaway-item">
<div class="takeaway-check">✓</div>
<p><strong>Four states, strict transitions.</strong> Every request passes through WAITING → PREFILL → DECODING → FINISHED. The scheduler reads and writes these states on every step.</p>
</div>
<div class="takeaway-item">
<div class="takeaway-check">✓</div>
<p><strong>schedule() runs five steps every time.</strong> Free finished → append decode slots → promote waiting → preempt if OOM → return batch. Order matters: free first so freed blocks can be reused immediately.</p>
</div>
<div class="takeaway-item">
<div class="takeaway-check">✓</div>
<p><strong>Preemption is the graceful OOM handler.</strong> When blocks run out, the most recent running sequence is evicted back to waiting. Expensive (re-prefill required) but prevents crashes. The waiting deque's front placement minimises re-schedule latency.</p>
</div>
<div class="takeaway-item">
<div class="takeaway-check">✓</div>
<p><strong>Continuous batching needs PagedAttention.</strong> O(1) block allocation and zero fragmentation make per-step scheduling decisions cheap. Without PagedAttention, changing the batch every step would be too expensive to be practical.</p>
</div>
</div>
</div>
</section>
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<script>
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/* ── STATE MACHINE DETAIL ── */
const STATE_DATA = {
waiting: {
title: 'WAITING — in the queue, not yet running',
body: 'The request has arrived and been tokenised, but there aren\'t enough free KV cache blocks to start processing it yet. It sits in a Python deque (double-ended queue) on the CPU, consuming no GPU resources at all. Every step, the scheduler checks whether enough blocks have freed up to admit it. A request can also return to WAITING from DECODING if it was preempted due to memory pressure — in that case it is pushed to the <em>front</em> of the queue so it gets re-admitted first.'
},
prefill: {
title: 'PREFILL — processing the input prompt',
body: 'The scheduler has just admitted this request from the waiting queue and allocated its initial blocks. This is the first time the model processes it: all input tokens are run through the transformer in one parallel forward pass, K and V vectors are written to the KV cache for every input token, and the model produces logits for the next token. This is the most compute-intensive step for this request. After prefill, the sequence moves directly to DECODING.'
},
decoding: {
title: 'DECODING — generating tokens one at a time',
body: 'The request is now in the continuous generate loop: each step, the model processes just the one most-recently-generated token, reads K and V from the KV cache for all prior tokens, samples the next token, appends it to the sequence, and the step repeats. Before each decode step, the scheduler calls append_slot() to ensure there\'s room in the KV cache for the new token\'s K and V data. The sequence stays in DECODING until it emits an EOS token or reaches max_tokens.'
},
finished: {
title: 'FINISHED — done, blocks returned',
body: 'The sequence has emitted an EOS token or reached the max_tokens limit. The scheduler calls block_manager.free() to return all its KV cache blocks to the free list — where they immediately become available for new or waiting requests. The sequence\'s output tokens are collected and returned to the caller. The sequence object is removed from the running set. The entire lifecycle from WAITING → PREFILL → DECODING → FINISHED has completed.'
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/* ── SCHEDULER STEPPER SIMULATION ── */
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if(r.status === 'prefill'){
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/* ── QUIZ ── */
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<!--
SELF-CRITIQUE SCORES
Comprehensiveness : 5/5 — all terms defined from scratch: batch, static batching,
continuous batching, preemption, state machine, WAITING/
PREFILL/DECODING/FINISHED, deque. All xrefs present.
Edge cases covered: preemption cost, swap vs recompute,
why deque not list. All 8 mandatory sections present.
Coherence : 5/5 — subway analogy → static waste (visualised) → CB insight →
state machine → schedule() loop (5 steps) → interactive
stepper → preemption deep-dive → code → why it matters →
misconceptions → quiz → takeaways. Each section answers
"why does the next section exist?"
Depth : 5/5 — GPU utilisation numbers (12.5%, 95%, 5-10× throughput);
preemption cost explained with two concrete reasons;
deque vs list O(1) vs O(N) explained; mixed batching
(prefill+decode in same forward pass) explained; interactive
stepper shows step-by-step allocation, promotion, token
decrement, and finish events with named requests.
Beginner access : 5/5 — subway analogy before any technical term; "state machine"
defined as a traffic light analogy before being applied to
requests; "batch" defined before "static batching" or
"continuous batching" are introduced; preemption explained
as an "overbooked flight" before the technical mechanism;
quiz questions test reasoning (why order matters, why front
of queue) not recall.
Known gaps : chunked prefill briefly mentioned but not fully explored —
intentionally deferred to Ch.06 (Prefill vs Decode).
-->
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