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How vLLM Prefix Caching Reduces Multi-Turn Inference Latency

This story is from 2026-09-04. It is preserved in the archive; the latest stories are on the live feed.

vLLM's Prefix Caching mechanism reuses KV states already computed from prior conversation turns, measurably reducing time-to-first-token (TTFT) in multi-turn dialogue scenarios. In measured results from Mingxin FX100 on a 480B-parameter model, enabling tiered KV acceleration reduced TTFT by 26–32%【…

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  1. 2026-09-04 07:15 · DEV Community — AI
    How vLLM Prefix Caching Reduces Multi-Turn Inference Latency

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