arXiv:2605.23215v1 Announce Type: new Abstract: LLM-based agents for GPU kernel generation are advancing rapidly, yet their progress is fundamentally constrained by the benchmarks they optimize against. Existing benchmarks are poorly aligned with production inference frameworks: they evaluate kernels on a single GPU with synthetic inputs, ignore the surrounding compilation stack, and reward replicating known optimizations rather than discovering new ones. The resulting reward signals are misleading: agents learn to generate kernels that score well in sandboxes but introduce interface incompati
The rapid advancement of LLM-based agents generates an immediate need for better benchmarking that reflects real-world production environments and incentives for novel optimization.
Improved GPU kernel generation directly influences the efficiency and cost of AI inference, impacting the scalability and economic viability of AI models across industries.
Current benchmarks for GPU kernel generation are fundamentally flawed, fostering solutions that perform well in sandboxes but fail in production, necessitating a shift towards more sophisticated evaluation methods.
- · AI compute infrastructure providers
- · GPU manufacturers
- · AI model developers
- · Cloud service providers
- · Companies relying on current suboptimal kernel generation
- · Developers focused solely on synthetic benchmark scores
- · Providers of inefficient AI inference solutions
The call for better benchmarks will accelerate the development of more production-aligned kernel generation methods.
More efficient GPU utilization will lead to reduced operational costs for AI inference and enable larger, more complex models to be deployed.
The ability to discover novel optimizations through better benchmarking could create entirely new competitive advantages in AI hardware and software for specific applications.
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Read at arXiv cs.LG