谷歌新论文：用Agent理解瓶颈，将LLM基础设施优化从暴力搜索变为精准定位

New Google paper shows LLM infrastructure optimization does not have to be a giant brute-force search if an agent can understand why the system is slow.

The key shift is from “search everything” to “understand the bottleneck, then search the tiny part that matters.”

Instead of asking, “Which of these 100 configurations is fastest?”, this work asks, “What is actually causing the slowdown?” That one change dramatically shrinks the search.

Its Analyzer Agent reads profiler traces and classifies the bottleneck as compute, memory, or communication, then a Proposal Agent retrieves engineering knowledge and generates three targeted ways to map data, model, and sequence parallelism across TPU chips.

Across eight production workloads spanning dense and MoE models, pre-training, post-training, serving, and TPU systems from 2 to 2,048 chips, the human-validated production configuration appeared in PROMPTS' first batch every time.

In seven of eight cases, it was also the first configuration tested, and the agent's top-ranked proposal matched what engineers ultimately adopted in 87.5% of cases.