Prefix caching cut your benchmark's latency in half. Why might production see none of that?

A benchmark that replays one prompt measures the cache and reports a number production will never see. What the hit rate actually depends on, the eviction behaviour that erodes it under load, and the measurement that predicts the real gain.

Updated Sep 2026 · Grounded in real AI infrastructure interview loops and written to a senior-engineer editorial bar, with every number worked and every diagram hand-built.

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The concepts behind this question

Ranked by how closely each one overlaps this question's topic, so the first card is the thing to read if the answer above moved too fast.

Foundational

🧮 Open Weights & Serving Engines

SGLang Server Arguments That MatterSGLang's tuning model is different from vLLM's in one way that matters: it exposes the scheduler's aggressiveness and the static memory fraction as direct knobs, and its own documentation gives target values for the runtime signals those knobs move. That makes tuning it a measurement loop rather than guesswork. Aim for a queue of a hundred to a couple of thousand requests, token usage above 0.9, and five to eight gigabytes of free GPU memory after startup, then adjust the flags that move each one.

Foundational

🧮 Open Weights & Serving Engines

Serving Benchmarks That Do Not LieMost published serving numbers are not comparable to each other and not predictive of production, because they differ in the input distribution, the concurrency, whether the cache was warm, and which of several very different metrics is being reported. A benchmark that supports a decision has to fix all four, report a distribution rather than a mean, and be run against the traffic shape you actually serve. The single most useful discipline is to compute the bandwidth bound first, so you know what fraction of the possible you achieved.

Foundational

🧮 Open Weights & Serving Engines

vLLM Server Arguments That MatterA vLLM deployment is mostly decided by a dozen flags, and the ones that matter fall into four groups: how the model is split across GPUs, how memory is divided between weights and cache, how requests are batched, and which specialized backends the model needs. Getting the first two wrong produces an engine that will not start or that runs out of memory under load. Getting the third wrong produces an engine that starts, serves, and misses its latency target by a wide margin.

Advanced

🚀 Inference & Serving🔒 Premium

Prefix Caching and KV ReuseMost requests to a production LLM share a prefix: the same system prompt, the same few-shot examples, the same conversation up to the latest turn. Prefix caching keeps the KV blocks for those tokens resident and skips their prefill, so a 4,000-token system prompt costs compute once instead of once per request. Radix trees make the lookup cheap, block-aligned hashing makes it safe, and the hit rate is what decides whether it is a 2x or a 10x win. The interview question is how you would route to make it hit.

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UP NEXT ON YOUR JOURNEY

Next in this trackWould you split prefill and decode onto separate pools for a 753B model? Work the decision.Next in this trackA multi-node vLLM deployment starts, forms a cluster, and hangs on the first request. Debug it.Next in this trackYour product forecasts 12,800 output tokens per second at peak. Size the fleet.

FEDITOR'S NOTE

Scored on identifying prompt repetition as the benchmark artefact, on measuring the real prefix-sharing rate, and on eviction under memory pressure as the production failure.

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