cuda-kernels Skill
Provides guidance for writing and benchmarking optimized CUDA kernels for NVIDIA GPUs (H100, A100, T4) targeting HuggingFace diffusers and transformers libraries. Kernels must be kernel-builder/ABI3-compliant: no pybind11, no setup.py, TORCHLIBRARYEXPAND bindings only. Supports models like LTX-Video, Stable Diffusion, Published by huggingface in kernels.
What is cuda-kernels Skill?
Provides guidance for writing and benchmarking optimized CUDA kernels for NVIDIA GPUs (H100, A100, T4) targeting HuggingFace diffusers and transformers libraries. Kernels must be kernel-builder/ABI3-compliant: no pybind11, no setup.py, TORCHLIBRARYEXPAND bindings only. Supports models like LTX-Video, Stable Diffusion, Published by huggingface in kernels. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Documentation
- Data analysis
- Design and media
- Writing
- Documentation use cases
Technical details
- Install or run with Copy skill directory
When to use cuda-kernels Skill
- Use it for documentation.
- Use it for data analysis.
- Use it for design and media.
- Use it for writing.
Built with
Editorial notes
Source
- Creator: huggingface
- Repository: huggingface/kernels
- Skill file: kernel-builder/skills/cuda-kernels/SKILL.md
What it does
Provides guidance for writing and benchmarking optimized CUDA kernels for NVIDIA GPUs (H100, A100, T4) targeting HuggingFace diffusers and transformers libraries. Kernels must be kernel-builder/ABI3-compliant: no pybind11, no setup.py, TORCHLIBRARYEXPAND bindings only. Supports models like LTX-Video, Stable Diffusion,
Skill instructions
CUDA Kernels for Diffusers & Transformers This skill provides patterns and guidance for developing optimized CUDA kernels targeting NVIDIA GPUs (H100, A100, T4) for use with HuggingFace diffusers and transformers libraries. Hard Constraints — Read Before Writing Any Code Kernels MUST build with kernel-builder and meet the Kernel Hub requirements. kernel-builder compiles against the Python limited API (ABI3) so a single binary works for Python 3.9+ across versions. Several patterns that are standard in generic PyTorch-extension tutorials are therefore hard build failures here. Do not use them, even if PyTorch documentation or your training data suggests them. Disallowed patterns — never generate these | ❌ Never use | Why it fails | ✅ Use instead | |---|---|---| | pybind11 in any form: include <torch/extension.h, include <pybind11/..., PYBIND11MODULE(...), py::arg, any py:: symbol | pybind11 is incompatible with the limited API (ABI3); the build does not compile | TORCHLIBRARYEXPAND in t
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Frequently asked questions
What is cuda-kernels?
cuda-kernels is a open-source AI agent skill with Copy skill directory. Provides guidance for writing and benchmarking optimized CUDA kernels for NVIDIA GPUs (H100, A100, T4) targeting HuggingFace diffusers and transformers libraries.
Who is cuda-kernels best for?
cuda-kernels is best for reusing agent instructions, scripts, and references, documentation workflows, data analysis workflows, design and media workflows.
How do I install cuda-kernels?
Install or run cuda-kernels using Copy skill directory. Check cuda-kernels for the latest setup command.
Is cuda-kernels actively maintained?
cuda-kernels may need a closer maintenance check before production use.
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