README.md

metadata

license: unlicense
tags:
  - wheels
  - pip
  - flash-attention
  - gpu
  - build-cache
pretty_name: GPU Build Wheel Cache

gpu-wheels

Personal cache of prebuilt Python wheels for packages that are slow to compile from source (flash-attn, etc.), so rented GPU instances (vast.ai and similar) don't have to recompile from scratch every time.

Compiling flash-attn from source can take 45-90 minutes. If a new instance has the exact same torch version, CUDA version, Python version, and C++ ABI as a wheel already in this repo, installing from the cached wheel takes seconds instead.

Repo layout

<package>/torch<torch_version>-py<python_version>-cxx11abi<True|False>/<wheel_filename>.whl

Example:

flash-attn/torch2.12.0+cu130-py3.12-cxx11abiTrue/flash_attn-2.8.3.post1-cp312-cp312-linux_x86_64.whl

The folder name is the compatibility key. A wheel only works on an environment matching all of: package version, torch version (incl. CUDA suffix), Python version, and cxx11abi flag.

Available wheels

Package	Torch	CUDA	Python	cxx11abi	GPU built on	Path
flash-attn 2.8.3.post1	2.12.0	13.0	3.12	True	RTX 3090 (sm86)	flash-attn/torch2.12.0+cu130-py3.12-cxx11abiTrue/

CUDA kernel wheels are generally GPU-arch-agnostic across NVIDIA GPUs (they embed multiple SM targets), so a wheel built on one GPU normally works on others — the torch/CUDA/Python/ABI match is what matters, not the specific GPU model.

Usage: install a cached wheel

pip install huggingface_hub

python3 -c "
from huggingface_hub import hf_hub_download
path = hf_hub_download(
    repo_id='DanielTobi0/gpu-wheels',
    repo_type='dataset',
    filename='flash-attn/torch2.12.0+cu130-py3.12-cxx11abiTrue/flash_attn-2.8.3.post1-cp312-cp312-linux_x86_64.whl',
)
print(path)
"

pip install <path printed above>

Or in one line once you know the filename:

pip install "$(python3 -c "from huggingface_hub import hf_hub_download; print(hf_hub_download(repo_id='DanielTobi0/gpu-wheels', repo_type='dataset', filename='flash-attn/torch2.12.0+cu130-py3.12-cxx11abiTrue/flash_attn-2.8.3.post1-cp312-cp312-linux_x86_64.whl'))")"

Before installing, check your new instance's versions match the folder name:

python3 -c "import torch; print(torch.__version__, torch.version.cuda, torch._C._GLIBCXX_USE_CXX11_ABI)"

If they don't match, the wheel likely won't install (or worse, may install but be ABI-incompatible) — build fresh instead and add the new combo to this repo (see below).

Adding a new wheel after a fresh build

1. Build normally (e.g. pip install flash-attn --no-build-isolation).
2. Locate the built wheel. With uv, it's cached under ~/.cache/uv/sdists-v9/pypi/<package>/<version>/*/​*.whl. With plain pip, add --no-clean -v or build explicitly with pip wheel <package> --no-build-isolation -w /tmp/wheelhouse.
3. Record your environment's compatibility key:

   python3 -c "import torch; print(f'torch{torch.__version__}-py{__import__(\"platform\").python_version()[:4]}-cxx11abi{torch._C._GLIBCXX_USE_CXX11_ABI}')"
   

4. Upload:

   from huggingface_hub import HfApi
   api = HfApi()
   api.upload_file(
       path_or_fileobj="/path/to/built.whl",
       path_in_repo="<package>/<compat-key>/<wheel_filename>.whl",
       repo_id="DanielTobi0/gpu-wheels",
       repo_type="dataset",
   )
   

5. Add a row to the table above.

Notes

• This repo is private — wheels may be built against specific local paths/configs and aren't intended for public redistribution.
• Wheels are large (100-300MB+ for CUDA extensions); this is a personal cache, not a package index.