AsariAI's Self-Improving Agents Boost vLLM Throughput by 16%
Startup AsariAI demonstrated a breakthrough in underlying inference optimization using its self-improving agents, performing end-to-end optimization on the full vLLM stack. When running DeepSeek v4 Pro and GLM 5.2 models on B200 chips, it achieved up to a 16% increase in throughput and interactivity. This progress validates the immense potential of using agents to automate the optimization of complex inference systems, offering a new path for large-scale model deployment.
Confirmed
- 要点 AsariAI proposed the concept of "agent as a compiler," using a compound self-improvement mechanism to make the agents smarter and more efficient with each run.
- 要点 While optimizing the DeepSeek v4 Pro model, the agent autonomously learned how to avoid distributed deadlocks.
- 要点 The agent possesses cross-model optimization and experience reuse capabilities, applying previously accumulated knowledge when shifting to optimize GLM 5.2.
Why it matters
- 要点 Manually performing underlying inference optimizations for trillion-parameter large models is extremely challenging. AsariAI's solution validates the feasibility of using agents for automated inference optimization and improving hardware utilization efficiency.
2026-07-30 ~ 2026-07-30 · 5 related posts
Primary sources
- [source] Self-Improving Agents Boost vLLM Inference Throughput by 16% — yisongyue · 2026-07-30
- Self-Improving Agents Boost vLLM Inference Throughput by 16% for Trillion-Param Models — yisongyue · 2026-07-30
- AsariAI's Self-Improving Agents Boost vLLM Throughput by 16% on B200s — yisongyue · 2026-07-30
- [source] Asari Agents Automate Inference Optimization, Generalizing Across Models — teortaxesTex · 2026-07-30
- [source] Agents as Compilers: Generalizing Insights Across Model Optimizations — yisongyue · 2026-07-30