让下一篇重要论文，不再错过。

原文摘要

AI chips sit mostly idle while serving models, because replies must be fast and each step waits on memory. On two large open models, the math units were busy only about 6-11% of the time. We argue the GPU needs an operating system that keeps urgent work on schedule and fills idle capacity with other useful work. A feature that ships today recovers a quarter of a GPU with work designed to share, and a tenth with real jobs, at 5% slower decode. Our research system does better: one resident kernel runs a whole model faster than vLLM's best for a single request, and shares the GPU at under 2% slowdown. Scaling beyond one hand-built model needs compilers and a few new chip features, which we specify.