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What: We share experiments and tooling to standardise kernel writing for agentic coding.
We present an end-to-end experiment benchmarking 6 harnesses across 10 models on CUDA and Metal kernel writing. We compare agent cost, kernel latency, VRAM usage, and end inference performance, and show how the Kernels Hub enables distribution at scale.
We demo two tools:
Kernels Hub: Infrastructure for writing, maintaining, and distributing reproducible kernels in the PyTorch ecosystem.
HF Skills: A library for defining and evaluating agent skills for ML tasks like kernel writing.
Why: Beyond agentic hype, kernel writing is a fundamental problem requiring robust evaluation to scale the community. High-performance kernels demand rare expertise in memory coalescing, warp-level primitives, and hardware-specific optimization. In practice, builders optimize for the highest market-share hardware, leaving a massive matrix of model×hardware combinations unserved, For example: edge inference with ExecuTorch, local LLMs on Metal via vLLM, classic ML at scale on Intel. This talk is technical, intended for kernel writers and PyTorch builders who want to use agents robustly.
Ben Burtenshaw is an MLE in the Hugging Face open source community team, specializing in agents, LLMs, and fine-tuning. He leads the development of open-source educational initiatives like the Agents Course, the MCP Course, and the LLM Course, which bridge the gap between complex... Read More →
Wednesday April 8, 2026 15:25 - 15:50 CEST Master Stage
