#agentic

2 posts

vLLM x AgentX: Optimizing for Real-World Agentic Serving

Sep 8, 2026·24 min read

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.

Serving Agentic Workloads at Scale with vLLM x Mooncake

May 6, 2026·10 min read

How vLLM integrates Mooncake Store as a distributed KV cache for agentic workloads, reusing shared prefixes across turns and instances to improve throughput, TTFT, end-to-end latency, and multi-GPU scaling.