從零到 GPU：構建與擴展生產級 CUDA Kernel 實戰指南
Original: From Zero to GPU: A Guide to Building and Scaling Production-Ready CUDA Kernels
As the architecture and scale of deep learning models (such as large language models, or LLMs) continue to expand, standard PyTorch…
Hugging Face 推出實用指南，協助開發者克服自訂 GPU 算子的開發門檻。文章深入探討如何從零開始撰寫 CUDA Kernel，並介紹如何利用 OpenAI Triton 簡化開發流程。最後，指南展示了如何將這些自訂算子無縫整合至 PyTorch 中，並透過 Profiling 工具進行效能調優，以達到生產環境的擴展需求。
As the architecture and scale of deep learning models (such as large language models, or LLMs) continue to expand, standard PyTorch operators sometimes fall short of meeting extreme performance requirements. To eliminate memory bandwidth bottlenecks and achieve operator fusion, developing custom GPU kernels has become an essential skill for AI engineers and researchers. This guide from Hugging Face aims to walk developers through building, optimizing, and scaling production-grade CUDA kernels from scratch.
Free shows the 3-line summary; Pro unlocks the full deep summary (~300 words) so you never have to click through.
See Pro plans →Want the original English / full article?
Read on Hugging Face Blog →Summaries are AI-generated; the original article is authoritative.