Self-Improving Agents Boost B200 Inference Throughput by 16% Without Losing Accuracy
yisongyue · x · 2026-08-05
Asari AI introduced a method using self-improving agents (co-inventors) to optimize the AI inference stack end-to-end. Tested on DeepSeek V4 Pro and GLM-5.2 running on NVIDIA B200s, the system improved throughput and interactivity by 16% across multiple max-concurrency levels.
The optimization spans the entire inference stack, including kernels, schedulers, and load-balancers. To ensure speed gains don't compromise correctness, the system uses stringent distribution-level correctness checks rather than standard benchmarks.
Additionally, Artificial Analysis announced the Endpoint Accuracy Index to measure how well serverless API endpoints preserve an open-weights model's native accuracy, helping developers choose providers based on accuracy, not just price and speed.
Related event: Asari AI's Self-Optimizing Agents Boost LLM Inference Throughput(2 posts)→
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