Databricks sweeps NVIDIA’s Kernel ranking with AI, securing first place in four categories across 235 tests, with token costs totaling roughly $70,000.
1 hours ago
Beating AI Briefing: The Databricks team announced that they integrated GPT-6 Astra and Opus 5 into an automated optimization loop, enabling the models to continuously rewrite, test, and refine GPU kernels, ultimately claiming first place across all four tracks of the NVIDIA SOL-ExecBench benchmark. The benchmark includes 235 GPU kernel tasks covering basic operators, complex fused operators, low-precision computing, and real-world large language model inference. The team stated that the total token cost for the entire process was approximately $70,000. Built on KDA and Humanize, the system allows AI to autonomously write kernels, run correctness and performance tests, and iteratively modify them based on results. The KDA team recently applied a similar method to Kimi Delta Attention, achieving 2.96x the speed of Moonshot AI’s official FlashKDA on the NVIDIA B300. Both results are different applications of the same agent kernel optimization system. The Kimi project proved that a highly complex attention kernel can be rewritten by AI, while Databricks’ work extended this approach to hundreds of distinct kernels.
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