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vLLM Production Serving: PagedAttention, Continuous Batching, Prefix Caching
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vLLM Production Serving: PagedAttention, Continuous Batching, Prefix Caching

Stand up vLLM and measure the three features that make it the de-facto inference server: PagedAttention's KV-cache capacity, continuous batching throughput, and prefix caching speedups. Then write the production spec — server args, Kubernetes deployment, monitoring, autoscaling.

55 min·4 steps·3 domains·Advanced·ncp-genlncp-aionca-genl

What you'll learn

  1. 1
    Load vLLM and inspect PagedAttention capacity
  2. 2
    Continuous batching throughput
  3. 3
    Prefix caching
  4. 4
    Production: server args, Kubernetes, monitoring, autoscaling

Prerequisites

  • Comfortable running Python scripts on a GPU
  • Familiarity with LLM inference concepts (KV cache, batching, decoding)
  • Basic Kubernetes YAML knowledge

Exam domains covered

Model Deployment & Inference OptimizationLLM Integration and DevelopmentGPU Acceleration & Distributed Training

Skills & technologies you'll practice

This advanced-level gpu lab gives you real-world reps across:

vLLMPagedAttentionContinuous BatchingPrefix CachingInferenceKubernetesMonitoringAutoscaling

What you'll measure in this vLLM production-serving lab

vLLM is the default LLM inference server in 2026 for one reason: PagedAttention plus continuous batching plus prefix caching let one GPU serve an order of magnitude more concurrent users than a naive model.generate() loop. In 55 minutes you'll stand up a real vLLM engine, measure each of those three features individually against a baseline, and leave with the complete production spec — CLI args, Kubernetes Deployment YAML with nvidia.com/gpu scheduling and probes, a monitoring stack that includes vLLM-specific signals (TTFT, KV-cache utilization, queue depth), and an autoscaling policy that doesn't fall over on mixed prompt lengths. The mental model you walk away with is that LLM serving is dominated by the KV cache, not compute — and that every knob in your production config is really a KV-cache knob in disguise.

The technical substance is three measurements and one spec. PagedAttention stores per-token K/V in fixed-size blocks (typically 16 tokens) and maps logical positions to physical blocks through a table, so a 50-token generation consumes ~3 blocks instead of a pre-allocated 2048-token slot — you'll pull block_size and max_concurrency off llm.llm_engine.cache_config and see the multiplier directly. Continuous (iteration-level) batching repacks the active set at every decode step, admits newly-arrived requests mid-flight, and evicts finished ones, giving ≥1.3× throughput over a single-request baseline even on a tiny model. Prefix caching reuses KV blocks for shared prompt prefixes — system prompts, RAG retrieved context, few-shot example blocks — which is why chat-heavy and RAG workloads see the biggest wins. By the end you're defending the autoscaling metric by hand: raw QPS is misleading (an 8k-token request is not equivalent to a 100-token one), GPU utilization saturates at 100% long before throughput plateaus, TTFT is a lagging latency signal good only for scale-up triggers, and KV-cache utilization or queue depth is the real saturation signal PagedAttention exposes.

You need comfort running Python on a GPU, familiarity with KV cache / batching / decoding concepts, and basic Kubernetes YAML — no prior vLLM experience assumed. The sandbox is a real NVIDIA GPU pod we provision per session with vLLM, CUDA, and a chat-capable small model preinstalled. Checks enforce live behavior: the engine must actually load and produce a non-trivial response, max_concurrency ≥4, continuous batching throughput ≥1.3× the single-request baseline on ≥16 prompts, prefix-caching warm run ≥1.05× faster than cold with ≥50 shared tokens, and the production spec must include every required CLI flag (--model, --max-model-len, --gpu-memory-utilization), Kubernetes primitive (nvidia.com/gpu, readiness/liveness probes, /v1/completions), ≥4 monitoring metrics with at least one vLLM-specific, and complete autoscaling bounds.

Frequently asked questions

Why does PagedAttention matter for throughput?

Because classical KV caches pre-allocate the maximum sequence length per slot, wasting enormous amounts of VRAM on short generations and fragmenting the free list. PagedAttention stores K/V in fixed-size blocks (typically 16 tokens) and uses a block table to map logical token positions to physical blocks — so a 50-token generation consumes ~3 blocks instead of a full 2048-token slot. The KV cache then holds far more concurrent sequences with the same VRAM, and max_concurrency in the lab's output shows the multiplier directly.

Continuous batching vs dynamic batching — what's the actual difference?

Dynamic batching groups N independent requests into one forward pass and returns N results — the whole batch starts and finishes together. Continuous (iteration-level) batching operates at each decode step: after generating one token for every active sequence, vLLM re-packs the batch, admits newly-arrived requests mid-stream, and evicts sequences that hit EOS. It only makes sense for autoregressive decoding, where responses have wildly different lengths — continuous batching is why LLM serving throughput scales with concurrency instead of collapsing on tail-length variance.

When does prefix caching actually pay off?

When you have a long shared prefix across requests and the model re-runs it each time: long system prompts ('You are a helpful assistant who...'), RAG where the retrieved context dominates, few-shot prompting with a fixed block of examples, and agents that replay a shared policy preamble. vLLM hashes the token sequence and reuses the KV blocks computed for matching prefixes. The lab's Step 3 populates cache_use_cases with exactly these scenarios — it's the list you'd use to decide whether to turn the flag on in your own deployment.

What makes a good vLLM autoscaling metric?

Not raw QPS — an 8k-token request consumes vastly more KV cache than a 100-token one, so counting requests misses the actual saturation signal. KV-cache utilization or queue depth capture the real pressure on PagedAttention, TTFT (time-to-first-token) is a late-binding latency signal good for scale-up triggers, and GPU utilization alone is misleading because it saturates at 100% long before throughput plateaus. The lab's reflection at the end asks you to defend the choice you made, with specific failure modes for each alternative.

Do I need to configure vllm_server_args by hand or is there a default?

There are defaults but they're rarely what you want. --gpu-memory-utilization (how much VRAM vLLM claims for weights + KV cache, usually 0.85–0.95) and --max-model-len (context window ceiling, which trades per-request length for total concurrency) are the two knobs with the biggest impact. --max-num-seqs caps parallel sequences. The lab requires --model, --host, --port, --max-model-len, and --gpu-memory-utilization in your CLI list because those are the non-default-safe ones for production.

What does each grader enforce?

Step 1 requires first_output to be a non-trivial string, kv_cache_info to include max_tokens/max_concurrency/block_size, with block_size >= 8 and max_concurrency >= 4. Step 2 requires ≥16 prompts in the batch, throughput ≥50 tok/s, and ≥1.3× speedup over single-request. Step 3 needs ≥50 shared prefix tokens, warm < cold, speedup ≥1.05×, and ≥3 cache_use_cases. Step 4 validates every required CLI flag, Kubernetes Deployment primitives (nvidia.com/gpu, readiness/liveness probes, /v1/completions endpoint), ≥4 monitoring metrics with at least one vLLM-specific, and a complete autoscaling policy.