Rohan Paul (@rohanpaul_ai) · 10/14/2026, 00:18:18
Agent Traffic Reshapes Inference Workloads: Prefix Caching and KV Reuse Become Critical for vLLM
Agentic workloads exhibit a unique 'long prefix, short output' pattern that invalidates traditional throughput-centric inference optimizations. At the DigitalOcean Open Intelligence Summit, Inferact experts highlighted that vLLM must prioritize Prefix Caching and KV Cache reuse to avoid redundant prefill costs per turn. This shift underscores how the computational bottleneck moves from raw token generation to context management in autonomous agent scenarios.
SOURCE COVERAGEFull Original Report
Rohan Paul (@rohanpaul_ai) · 2026-10-13 16
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.
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