hardNewSGLangAnthropicPerplexity

How do you route requests across replicas to maximize prefix-cache hits without unbalancing the fleet?

Least-loaded routing sends a conversation's tenth turn to a replica that has never seen it, and the whole history prefills again. Affinity fixes that and creates hot spots. The router that does both is a scoring function with two terms.

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.

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📐 AI Systems Design🔒 Premium

Request Routing and Load Balancing for LLMsA load balancer for stateless web services spreads requests evenly and is done. A router for LLM replicas has two things a web balancer never had to think about: each replica holds a cache (the KV pages of recent prefixes) that makes some replicas far cheaper than others for a given request, and each request costs a wildly different amount, so counting connections is meaningless. This page builds the router that handles both: prefix-aware placement with load-aware fallback, cost-aware queue estimates, session affinity, and the failure handling when a replica restarts and its cache is gone.

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💻 Coding for Infra

Consistent Hashing and ShardingSplitting work across N servers with a modulo of N moves almost everything when N changes, which for a cache means throwing away almost all of it. Consistent hashing places servers and keys on a ring so adding or removing one moves only its share, and virtual nodes fix the imbalance a small ring otherwise has. In LLM serving the same structure routes requests by prompt prefix so a conversation reaches the replica already holding its cache.

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🚀 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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🕸️ Distributed Training🔒 Premium

Expert Parallelism for MoEA mixture-of-experts layer runs only a few of its experts per token, so the experts can be spread across GPUs and each token shipped to the ranks that hold its chosen experts. That shipping is an all-to-all in each direction, twice per layer per pass, and its cost plus the load imbalance between experts is what expert parallelism is really about.

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UP NEXT ON YOUR JOURNEY

Next in this trackYou stream tokens to clients over HTTP. What can go wrong, and how does the server handle each case?Next in this trackYou need to serve 128k-token contexts. What breaks first, and what do you change?Next in this trackEAGLE, Medusa or a separate draft model: which speculative decoding method do you pick, and why?

FEDITOR'S NOTE

Scored on quantifying what a hit is worth, on the scoring formula that trades affinity for load, and on how the router learns which replica holds which prefix.

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