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Prefix Caching and KV Reuse

Most 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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RELATED CONCEPTS

The KV Cache PagedAttention Request Routing and Load Balancing for LLMs Chunked Prefill Prefill vs Decode KV Cache Sizing

LESSONS THAT TEACH THIS

Inference EngineeringBenchmarks lie unless the length distribution is real14 min→Foundations of AI InfrastructureThe KV cache is what actually decides how many users fit14 min→Inference EngineeringContiguous KV allocation wastes most of the memory you paid for13 min→

PRACTICE THIS IN REAL QUESTIONS

LLM Inference & ServingEvery request shares a 2,000-token system prompt. How does prefix caching exploit that, and how does the radix tree work?→Open-Weights Models & Serving EnginesYou are serving an agent product with long shared prompts. vLLM or SGLang?→LLM Inference & ServingvLLM, SGLang or TensorRT-LLM: which engine do you pick for a new deployment, and what would change your mind?→LLM Inference & ServingHow do you route requests across replicas to maximize prefix-cache hits without unbalancing the fleet?→Open-Weights Models & Serving EnginesYour agent product's tool-call success rate fell from 94 to 61 percent after a deploy. Debug it.→Open-Weights Models & Serving EnginesAn SGLang deployment underperforms. Tune it against the project's own published targets.→