Abstract: Helion, PyTorch’s domain-specific language (DSL) for performance portable machine learning kernels, heavily relies on autotuning for performance. Currently Helion searches utilize the Likelihood-Free Bayesian Optimization (LFBO) to find the most performant configs. LFBO is a strong baseline which works well, but it still grinds through hundreds of compile-and-benchmark cycles per kernel. To this end, we introduce an LLM-guided autotuner that matches LFBO-level kernel performance (geomean 1.009X) while benchmarking ~10X fewer configurations in ~6.7X less wall-clock time. For the handful of kernels where the LLM trails by >5%, a hybrid strategy (LLM seeding followed by LFBO refinement) closes the gap while remaining ~3X cheaper than the full LFBO search. Finally, the result is largely LLM model-independent — Opus-4.8, gpt-5.5, and Sonnet-4.6 perform within a couple percent of each other — showing that LLM-guided autotuning is a practical approach to dramatically faster kernel tuning at production quality.