vLLM session targets prefix caching for agent inference workloads

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Yifan Qiao of Inferact will present vLLM on agentic production workloads at DigitalOcean's Open Intelligence Summit, Serve track.

Agent traffic resends a long, nearly identical prefix on every turn and asks for a short tool call back, so prefix caching and KV-cache reuse matter more than raw tokens per second.

A cache miss on a long prefix forces a full prefill on each turn of a long task.

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Rohan Paul

@rohanpaul_ai

Agent traffic is a strange workload for an inference server.

Each turn resends a long, nearly identical prefix (system prompt, tool schemas, history) and asks for a short tool call back.

At DigitalOcean’s Open Intelligence Summit, Yifan Qiao of Inferact is giving a Serve track session on running vLLM on agentic production workloads.

That shape of traffic is why prefix caching and KV-cache reuse matter more for agents than raw tokens per second. A cache miss on a long prefix means paying for the full prefill again, on every turn of a long task.

DigitalOcean@digitalocean

Who gets to shape the future of AI? Today we're bringing together some of the biggest voices in open models, data, and agents to answer that question, including @rbren_dev of @OpenHandsDev, @zlxi02 of @inferact, @GerryFPatia of @boltdotnew, @vinaygo of @Amplitude_HQ and @latkins of @arcee_ai. The Open Intelligence Summit, co-hosted with @nvidia. Watch live, starting at 1 PM PT: Keynote: http://youtube.com/live/IhWSgZlVizQ Build: http://youtube.com/live/eN0TqmeBNhw Serve: http://youtube.com/live/fkT-MhcAm-4 Run: http://youtube.com/live/QMrY1xZ-xGg

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Source: Rohan Paul · x.comPublished Oct 13, 12:18 PM ET

vLLM session targets prefix caching for agent inference workloads

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