AI RESEARCH
Optimizing PyTorch Inference with LLM-Based Multi-Agent Systems
arXiv CS.AI
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ArXi:2511.16964v2 Announce Type: replace-cross Maximizing performance on available GPU hardware is an ongoing challenge for modern AI inference systems. Traditional approaches include writing custom GPU kernels and using specialized model compilers to tune high-level code for specific GPU targets. Recent work shows that LLM-based multi-agent systems can effectively perform such tuning, often outperforming existing compilers and eliminating the need for manual kernel development. However, the dynamics of multi-agent systems for this task remain unexplored.