vLLM x AgentX: Optimizing for Real-World Agentic Serving
vLLM x AgentX: Optimizing for Real-World Agentic Serving
摘要
How vLLM optimizes KV cache management, parallelism, scheduling, and P/D disaggregation for agentic workloads, validated on SemiAnalysis AgentX with up to 130K tokens per GPU-second and a 14.6x-106x serving-cost advantage over Opus 5.
How vLLM optimizes KV cache management, parallelism, scheduling, and P/D disaggregation for agentic workloads, validated on SemiAnalysis AgentX with up to 130K tokens per GPU-second and a 14.6x-106x serving-cost advantage over Opus 5.
译自 vLLM · 官方博客 · 录于 二〇二六年九月九日