How Much of a Real Workload Can LLM-Generated GPU Kernels Actually Reach?
Language models can now write GPU kernels that outperform PyTorch. We evaluate five model configurations on KernelBench level 1 and find that a frontier model produces correct kernels for 91.1% of problems and independently verified speedups on 22 of 56, including three convolutions, with a median of 1.235x. Open-weights models are far behind: the best reaches 30.4% correct with three verified speedups and solves zero convolutions. We then ask a question the literature does not: what fraction of a real model's wall clock do such kernels govern? Profiling seven workloads across three domains, we find the addressable fraction ranges from 8.9% to 58.2%. On transformers, 80-86% of runtime is ...
| Authors | Gaurav Agarwal, Ashish Garg, Isha Singhal |
|---|---|
| Primary category | cs.DC |
| Published | 17 September 2026 |
| Last updated at source | 17 September 2026 |
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