Kernels are written in the CuTe-DSL.
# For CUDA 12.9:
pip install quack-kernels
# For CUDA 13.1:
pip install 'quack-kernels[cu13]' --extra-index-url https://download.pytorch.org/whl/cu130
# Or using uv (faster):
uv pip install 'quack-kernels[cu13]'- H100 or B200/B300 GPU
- CUDA toolkit 12.9+
- Python 3.12
- 🦆 RMSNorm forward + backward
- 🦆 Softmax forward + backward
- 🦆 Cross entropy forward + backward
- 🦆 Layernorm forward
- 🦆 Hopper gemm + epilogue
- 🦆 Blackwell gemm + epilogue
from quack import rmsnorm, softmax, cross_entropy
[2025-07-10] We have a comprehensive blogpost on how to get memory-bound kernels to speed-of-light, right in the comfort of Python thanks to the CuTe-DSL.
See our blogpost for the details.
To set up the development environment:
pip install -e '.[dev]'
pre-commit install
# For CUDA 13.1:
pip install 'quack-kernels[dev,cu13]' --extra-index-url https://download.pytorch.org/whl/cu130
# Or using uv:
uv pip install 'quack-kernels[dev,cu13]'