Rohan Paul (@rohanpaul_ai) · 2026/10/14 00:18:18
智能体流量重塑推理负载：长前缀复用与 KV Cache 成为 vLLM 优化核心
智能体（Agent）工作负载具有“长前缀重复、短输出”的独特特征，导致传统以吞吐量为核心的推理优化失效。在 DigitalOcean Open Intelligence Summit 上，Inferact 专家指出，针对此类流量，vLLM 等推理引擎需优先优化前缀缓存（Prefix Caching）和 KV Cache 复用，以避免每轮对话重复支付昂贵的预填充（Prefill）成本。这一观点揭示了从通用聊天到自主代理场景下，算力基础设施瓶颈的转移。
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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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