---
license: other
library_name: flash-attn
tags:
  - flash-attention
  - pytorch
  - cuda
  - windows
  - comfyui
  - custom-wheel
  - inference
---

# FlashAttention 2.8.3 — Windows Wheel for PyTorch 2.12 / CUDA 13

A community-built Windows wheel for **FlashAttention 2.8.3**, compiled from the official FlashAttention v2.8.3 source.

This build was created because upgrading to **PyTorch 2.12.1+cu130** can leave existing Windows FlashAttention builds incompatible.

## Compatibility

| Component | Tested configuration |
|---|---|
| Operating System | Windows 11 x64 |
| Python | 3.12 |
| PyTorch | 2.12.1+cu130 |
| CUDA Toolkit | 13.0.88 |
| FlashAttention | 2.8.3 |
| MSVC | Visual Studio 2022 / MSVC 19.44 |
| GPU | NVIDIA GeForce RTX 4070 Ti SUPER |
| GPU Architecture | SM89 / Compute Capability 8.9 |
| C++11 ABI | TRUE |
| Build System | Ninja 1.13.2 |

## Download

Download `flash_attn-2.8.3-cp312-cp312-win_amd64.whl` from the **Files** tab above.

## Installation

After downloading the wheel:

    python -m pip install flash_attn-2.8.3-cp312-cp312-win_amd64.whl --no-deps

If using a specific Python installation:

    D:\apps\Python\Python312\python.exe -m pip install flash_attn-2.8.3-cp312-cp312-win_amd64.whl --no-deps

## Verification

The compiled extension was tested by executing an actual FlashAttention CUDA kernel on an NVIDIA GeForce RTX 4070 Ti SUPER.

Test result:

    SUCCESS - Flash Attention CUDA kernel executed
    Output: torch.Size([1, 128, 8, 128]) torch.float16 cuda:0
    GPU: NVIDIA GeForce RTX 4070 Ti SUPER

The build was subsequently tested with ComfyUI:

    PyTorch: 2.12.1+cu130
    Device: NVIDIA GeForce RTX 4070 Ti SUPER
    Using Flash Attention

ComfyUI successfully started with FlashAttention enabled.

## Build Information

Source:

**FlashAttention v2.8.3**

Build configuration:

    CUDA_HOME=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0
    FLASH_ATTENTION_FORCE_CXX11_ABI=TRUE
    FLASH_ATTN_CUDA_ARCHS=89
    MAX_JOBS=4
    NVCC_THREADS=2
    DISTUTILS_USE_SDK=1

Built using:

    python -m pip wheel . --no-build-isolation --no-deps -w .\wheels

## Important — Read Before Installing

⚠️ **This is an unofficial community build.**

It is **not an official FlashAttention release** and is not affiliated with or endorsed by the FlashAttention developers.

The wheel has been tested on the configuration listed above.

In particular, this build was compiled for:

**NVIDIA SM89 / Ada Lovelace**

Other GPU architectures, Python versions, PyTorch versions, or CUDA versions may not be compatible.

Use at your own risk.

If this works on your hardware, please report your configuration so compatibility can be expanded.

## Why does this exist?

An existing Windows FlashAttention 2.8.3 installation may stop working after upgrading PyTorch.

For example, a previously working build may produce:

    ImportError: DLL load failed while importing flash_attn_2_cuda:
    The specified procedure could not be found.

This wheel was rebuilt against:

**PyTorch 2.12.1+cu130 / CUDA 13.0**

and verified with an actual CUDA kernel execution.

## SHA-256

    2d858cd3b3b1aacd48d5edec6e85db933eaef23ef2b47a81873968e4f8f6d913

## Credits

FlashAttention:
https://github.com/Dao-AILab/flash-attention

Original project by Tri Dao and contributors.

This repository contains a community-built Windows binary based on FlashAttention v2.8.3.