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Check out the documentation for more information.
SageAttention Wheels (CUDA 13.x) 🚀
Prebuilt SageAttention 2.2.0 wheels compiled for Linux x86_64 with CUDA 13.x support.
This repository provides ready-to-use binary wheels for different Python and PyTorch versions, optimized for modern NVIDIA GPUs (Ada / Hopper / Ampere).
📦 Available Wheels
| Python Version | PyTorch Version | CUDA | File |
|---|---|---|---|
| 3.11 | 2.10 | cu13 | sageattention-2.2.0-python3.11-pytorch2.10-cu13-linux_x86_64.whl |
| 3.12 | 2.10 | cu13 | sageattention-2.2.0-python3.12-pytorch2.10-cu13-linux_x86_64.whl |
| 3.12 | 2.11 | cu13 | sageattention-2.2.0-python3.12-pytorch2.11-cu13-linux_x86_64.whl |
| 3.13 | 2.11 | cu13 | sageattention-2.2.0-python3.13-pytorch2.11-cu13-linux_x86_64.whl |
⚡ Requirements
- Linux x86_64
- NVIDIA GPU (Ada / Ampere / Hopper tested)
- CUDA 13.x runtime / toolkit
- PyTorch matching wheel version
- Python version matching wheel
🧠 Installation
1. Create virtual environment
python3.12 -m venv venv source venv/bin/activate
2. Install PyTorch (CUDA 13)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu130
3. Install SageAttention wheel
pip install sageattention-2.2.0-python3.12-pytorch2.11-cu13-linux_x86_64.whl
🧪 Quick Test
python -c "import torch; print(torch.cuda.is_available())" python -c "import sageattention; print('SageAttention loaded successfully')"
🚀 Notes
- Wheels are precompiled for performance
- Must match Python + PyTorch versions exactly
- CUDA 13.x required
- Optimized for sm_80+ GPUs
⚠️ Troubleshooting
CUDA not found: export CUDA_HOME=/opt/cuda export PATH=$CUDA_HOME/bin:$PATH export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
💬 Support
Matrix Network
@aimiko:mochiart.moe
📜 License
Refer to upstream SageAttention repository. This repo contains only prebuilt binaries.