prefix caching

The same 2,000 tokens are prefilled a thousand times an hour. Caching their KV by content turns that into one prefill and a table lookup, and a radix tree is what makes multi-turn and branching agents share it too.

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.

Two hundred milliseconds is about four thousand prompt tokens of prefill on a full node, before any queueing. Every design choice is about not spending that budget twice.

Both engines cache prefixes, so the choice is not about whether the feature exists. It is about how much of your traffic shares a prefix, what the cache eviction does under pressure, and which tuning model your team can actually operate.

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.

A 70B model's KV cache is 320 KB per token, and a 30k-token conversation is 10 GB that GPU memory cannot keep between turns. The three tiers with their bandwidths, the break-even where reloading a prefix beats recomputing it, the eviction and lookup design, and the traffic shape where the tier is worth its complexity.