How to Roll Out Continuous Batching and Paged Attention in Production

Most teams read about continuous batching and paged attention, flip a flag, and hope for the best. This runbook walks through the sequence that keeps GPUs saturated without regressing tail latency.

Step 1 — Baseline your workload

Before touching the serving config, capture p50/p95/p99 latency, tokens/sec, and GPU utilization under a representative traffic mix. Separate prefill-heavy requests (long prompts, short outputs) from decode-heavy ones (short prompts, long outputs). Without that split, later gains look like noise.

Step 2 — Enable paged attention

Switch the KV cache to a paged allocator (vLLM, TensorRT-LLM, or SGLang all ship one). Set block size to 16 tokens as a starting point and watch for fragmentation drop in the allocator metrics. Expect a 2–4x throughput lift on mixed-length traffic.

Step 3 — Turn on continuous batching

Replace static batching with iteration-level scheduling. Cap the maximum batch by GPU memory, not by request count, and set a scheduling policy that admits new requests every decode step.

Step 4 — Disaggregate prefill and decode

Once batching is stable, split prefill and decode onto separate replica pools. Prefill is compute-bound and scales with FLOPs; decode is memory-bandwidth-bound. Sizing them independently often cuts cost 30–50%.

Step 5 — Add cache tiering

Spill cold KV blocks from VRAM to host RAM, then to NVMe. Treat it like a CPU cache hierarchy — measure hit rate at each layer and evict on LRU with a recency bias for the current session.

Step 6 — Validate with a shadow deployment

Mirror 5% of live traffic to the new stack for 48 hours. Compare token-level output distributions, not just latency. Roll forward only when quality diffs stay inside your evaluation tolerance.

If you want executive-level oversight on rollouts like this, Silverberry's AI Consulting Services can pair the runbook with governance guardrails.