vLLM on AgentX: up to 106× serving-cost advantage over Opus 5 API pricing
vllm_project · x · 2026-09-09
The vLLM team published a deep dive on optimizing serving for real-world agentic workloads, now a major share of vLLM traffic with multi-turn sessions, long contexts and heavy prefix reuse.
- On SemiAnalysis's public AgentX benchmark, vLLM hits up to 130K tokens per GPU-second on DeepSeek V4 Pro and 376 tokens/s interactivity on MiniMax M3.
- Across DeepSeek V4 Pro, MiniMax M3 and Kimi K3, vLLM delivers a 14.6×–106× serving-cost advantage over Opus 5 API pricing.
- Hard-won lessons: pipeline parallelism wins on cold long prompts but bubbles eat gains on warm agent turns; decode context parallelism matched DEP on DeepSeek V4 but won on Kimi K3—parallelism must follow the attention stack; load balancing doesn't always beat session-sticky routing.
- Everything is reproducible via a public dashboard with configs across GB300 NVL72 and B300.
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