---
# Model card metadata following Hugging Face specification: 
# https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Documentation: https://huggingface.co/docs/hub/model-cards
license: apache-2.0
tags:
  - flash-attention
  - nvidia
  - blackwell
  - windows
  - prebuilt-wheels
  - python
  - machine-learning
  - deep-learning
---

# Flash-Attention 2.7.4 Prebuilt Wheels for NVIDIA Blackwell (cu128) on Windows

This repository provides prebuilt wheels for **Flash-Attention 2.7.4** optimized for NVIDIA Blackwell GPUs (cu128 and cu129) on Windows systems.
These wheels are compatible with Python 3.10 and 3.11, enabling seamless integration for high-performance attention mechanisms in deep learning workflows.

## Available Wheels
- `flash_attn-2.7.4.post1-cp310-cp310-win_amd64.whl` (Python 3.10) * pytorch 2.7 cu128
- `flash_attn-2.7.4.post1-cp311-cp311-win_amd64.whl` (Python 3.11) * pytorch 2.7 cu128

- `flash_attn-2.7.4.post1-cp310-cp310-win_amd64.whl` (Python 3.10) * pytorch 2.8 cu129

## Compatibility
The prebuilt wheels are designed for NVIDIA Blackwell GPUs but have been tested and confirmed compatible with previous-generation NVIDIA GPUs, including:
- NVIDIA RTX 5090
- NVIDIA RTX 3090

## Installation
To install, use pip with the appropriate wheel for your Python version:

```bash
pip install flash_attn-2.7.4.post1-cp310-cp310-win_amd64.whl
# or
pip install flash_attn-2.7.4.post1-cp311-cp311-win_amd64.whl