
AI-windows-whl
Pre-compiled Python whl for Flash-attention, SageAttention, NATTEN, xFormer etc
Stars: 147

AI-windows-whl is a curated collection of pre-compiled Python wheels for difficult-to-install AI/ML libraries on Windows. It addresses the common pain point of building complex Python packages from source on Windows by providing direct links to pre-compiled `.whl` files for essential libraries like PyTorch, Flash Attention, xformers, SageAttention, NATTEN, Triton, bitsandbytes, and other packages. The goal is to save time for AI enthusiasts and developers on Windows, allowing them to focus on creating amazing things with AI.
README:

A curated collection of pre-compiled Python wheels for difficult-to-install AI/ML libraries on Windows.
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Table of Contents
This repository was created to address a common pain point for AI enthusiasts and developers on the Windows platform: building complex Python packages from source. Libraries like flash-attention
, xformers
are essential for high-performance AI tasks but often lack official pre-built wheels for Windows, forcing users into a complicated and error-prone compilation process.
The goal here is to provide a centralized, up-to-date collection of direct links to pre-compiled .whl
files for these libraries, primarily for the ComfyUI community and other PyTorch users on Windows. This saves you time and lets you focus on what's important: creating amazing things with AI.
Follow these simple steps to use the wheels from this repository.
- Python for Windows: Ensure you have a compatible Python version installed (PyTorch currently supports Python 3.9 - 3.12 on Windows). You can get it from the official Python website.
To install a wheel, use pip
with the direct URL to the .whl
file. Make sure to enclose the URL in quotes.
# Example of installing a specific flash-attention wheel
pip install "https://huggingface.co/lldacing/flash-attention-windows-wheel/blob/main/flash_attn-2.7.4.post1+cu128torch2.7.0cxx11abiFALSE-cp312-cp312-win_amd64.whl"
[!TIP] Find the package you need in the Available Wheels section below, find the row that matches your environment (Python, PyTorch, CUDA version), and copy the link for the
pip install
command.
Here is the list of tracked packages.
The foundation of everything. Install this first from the official source.
- Official Install Page: https://pytorch.org/get-started/locally/
For convenience, here are direct installation commands for specific versions on Linux/WSL with an NVIDIA GPU. For other configurations (CPU, macOS, ROCm), please use the official install page.
This is the recommended version for most users.
CUDA Version | Pip Install Command |
---|---|
CUDA 12.9 | pip install torch torchvision --index-url https://download.pytorch.org/whl/cu129 |
CUDA 12.8 | pip install torch torchvision --index-url https://download.pytorch.org/whl/cu128 |
CUDA 12.6 | pip install torch torchvision --index-url https://download.pytorch.org/whl/cu126 |
CPU only | pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu |
CUDA Version | Pip Install Command |
---|---|
CUDA 12.8 | pip install torch==2.7.1 torchvision==0.22.1 torchaudio==2.7.1 --index-url https://download.pytorch.org/whl/cu128 |
CUDA 12.6 | pip install torch==2.7.1 torchvision==0.22.1 torchaudio==2.7.1 --index-url https://download.pytorch.org/whl/cu126 |
CUDA 11.8 | pip install torch==2.7.1 torchvision==0.22.1 torchaudio==2.7.1 --index-url https://download.pytorch.org/whl/cu118 |
CPU only | pip install torch==2.7.1 torchvision==0.22.1 torchaudio==2.7.1 --index-url https://download.pytorch.org/whl/cpu |
Use these for access to the latest features, but expect potential instability.
PyTorch 2.9 (Nightly)
CUDA Version | Pip Install Command |
---|---|
CUDA 12.9 | pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cu130 |
CUDA 12.8 | pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cu128 |
CUDA 12.6 | pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cu126 |
Torchaudio
Package Version | PyTorch Ver | CUDA Ver | Download Link |
---|---|---|---|
2.8.0 |
2.9.0 |
12.8 |
Link |
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High-performance attention implementation.
- Official Repo: Dao-AILab/flash-attention
- Pre-built Sources: lldacing's HF, Wildminder's HF, mjun0812 GitHub
Package Version | PyTorch Ver | Python Ver | CUDA Ver | CXX11 ABI | Download Link |
---|---|---|---|---|---|
2.8.3 |
2.9.0 |
3.12 |
12.8 |
✓ | Link |
2.8.3 |
2.8.0 |
3.12 |
12.8 |
✓ | Link |
2.8.2 |
2.9.0 |
3.12 |
12.8 |
✓ | Link |
2.8.2 |
2.8.0 |
3.10 |
12.8 |
✓ | Link |
2.8.2 |
2.8.0 |
3.11 |
12.8 |
✓ | Link |
2.8.2 |
2.8.0 |
3.12 |
12.8 |
✓ | Link |
2.8.2 |
2.7.0 |
3.10 |
12.8 |
✗ | Link |
2.8.2 |
2.7.0 |
3.11 |
12.8 |
✗ | Link |
2.8.2 |
2.7.0 |
3.12 |
12.8 |
✗ | Link |
2.8.1 |
2.8.0 |
3.12 |
12.8 |
✓ | Link |
2.8.0.post2 |
2.8.0 |
3.12 |
12.8 |
✓ | Link |
2.7.4.post1 |
2.8.0 |
3.10 |
12.8 |
✓ | Link |
2.7.4.post1 |
2.8.0 |
3.12 |
12.8 |
✓ | Link |
2.7.4.post1 |
2.7.0 |
3.10 |
12.8 |
✗ | Link |
2.7.4.post1 |
2.7.0 |
3.11 |
12.8 |
✗ | Link |
2.7.4.post1 |
2.7.0 |
3.12 |
12.8 |
✗ | Link |
2.7.4 |
2.8.0 |
3.10 |
12.8 |
✓ | Link |
2.7.4 |
2.8.0 |
3.11 |
12.8 |
✓ | Link |
2.7.4 |
2.8.0 |
3.12 |
12.8 |
✓ | Link |
2.7.4 |
2.7.0 |
3.10 |
12.8 |
✗ | Link |
2.7.4 |
2.7.0 |
3.11 |
12.8 |
✗ | Link |
2.7.4 |
2.7.0 |
3.12 |
12.8 |
✗ | Link |
2.7.4 |
2.6.0 |
3.10 |
12.6 |
✗ | Link |
2.7.4 |
2.6.0 |
3.11 |
12.6 |
✗ | Link |
2.7.4 |
2.6.0 |
3.12 |
12.6 |
✗ | Link |
2.7.4 |
2.6.0 |
3.10 |
12.4 |
✗ | Link |
2.7.4 |
2.6.0 |
3.11 |
12.4 |
✗ | Link |
2.7.4 |
2.6.0 |
3.12 |
12.4 |
✗ | Link |
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Another library for memory-efficient attention and other optimizations.
- Official Repo: facebookresearch/xformers
- PyTorch Pre-built Index: https://download.pytorch.org/whl/xformers/
[!NOTE] PyTorch provides official pre-built wheels for xformers. You can often install it with
pip install xformers
if you installed PyTorch correctly. If that fails, find your matching wheel at the index link above.
ABI3 version, any Python 3.9-3.12
Package Version | PyTorch Ver | CUDA Ver | Download Link |
---|---|---|---|
0.0.32.post2 |
2.8.0 |
12.8 |
Link |
0.0.32.post2 |
2.8.0 |
12.9 |
Link |
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- Official Repo: thu-ml/SageAttention
- Pre-built Sources: woct0rdho's Releases, Wildminder's HF
Package Version | PyTorch Ver | Python Ver | CUDA Ver | Download Link |
---|---|---|---|---|
2.1.1 |
2.5.1 |
3.9 |
12.4 |
Link |
2.1.1 |
2.5.1 |
3.10 |
12.4 |
Link |
2.1.1 |
2.5.1 |
3.11 |
12.4 |
Link |
2.1.1 |
2.5.1 |
3.12 |
12.4 |
Link |
2.1.1 |
2.6.0 |
3.9 |
12.6 |
Link |
2.1.1 |
2.6.0 |
3.10 |
12.6 |
Link |
2.1.1 |
2.6.0 |
3.11 |
12.6 |
Link |
2.1.1 |
2.6.0 |
3.12 |
12.6 |
Link |
2.1.1 |
2.6.0 |
3.12 |
12.6 |
Link |
2.1.1 |
2.6.0 |
3.13 |
12.6 |
Link |
2.1.1 |
2.7.0 |
3.10 |
12.8 |
Link |
2.1.1 |
2.8.0 |
3.12 |
12.8 |
Link |
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[!NOTE] Only supports CUDA >= 12.8, therefore PyTorch >= 2.7.
Package Version | PyTorch Ver | Python Ver | CUDA Ver | Download Link |
---|---|---|---|---|
2.2.0.post2 |
2.5.1 |
>3.9 |
12.4 |
Link |
2.2.0.post2 |
2.6.0 |
>3.9 |
12.6 |
Link |
2.2.0.post2 |
2.7.1 |
>3.9 |
12.8 |
Link |
2.2.0.post2 |
2.8.0 |
>3.9 |
12.8 |
Link |
2.2.0.post2 |
2.9.0 |
>3.9 |
12.8 |
Link |
2.2.0 |
2.7.1 |
3.9 |
12.8 |
Link |
2.2.0 |
2.7.1 |
3.10 |
12.8 |
Link |
2.2.0 |
2.7.1 |
3.11 |
12.8 |
Link |
2.2.0 |
2.7.1 |
3.12 |
12.8 |
Link |
2.2.0 |
2.7.1 |
3.13 |
12.8 |
Link |
2.2.0 |
2.8.0 |
3.9 |
12.8 |
Link |
2.2.0 |
2.8.0 |
3.10 |
12.8 |
Link |
2.2.0 |
2.8.0 |
3.11 |
12.8 |
Link |
2.2.0 |
2.8.0 |
3.12 |
12.8 |
Link |
2.2.0 |
2.8.0 |
3.13 |
12.8 |
Link |
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- Official Repo: thu-ml/SpargeAttn
- Pre-built Sources: woct0rdho's Releases
Package Version | PyTorch Ver | CUDA Ver | Download Link |
---|---|---|---|
0.1.0.post1 |
2.7.1 |
12.8 |
Link |
0.1.0.post1 |
2.8.0 |
12.8 |
Link |
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- Official Repo: : mit-han-lab/nunchaku
Package Version | PyTorch Ver | Python Ver | Download Link |
---|---|---|---|
1.0.0 |
2.5 |
3.10 |
Link |
1.0.0 |
2.5 |
3.11 |
Link |
1.0.0 |
2.5 |
3.12 |
Link |
1.0.0 |
2.6 |
3.10 |
Link |
1.0.0 |
2.6 |
3.11 |
Link |
1.0.0 |
2.6 |
3.12 |
Link |
1.0.0 |
2.6 |
3.13 |
Link |
1.0.0 |
2.7 |
3.10 |
Link |
1.0.0 |
2.7 |
3.11 |
Link |
1.0.0 |
2.7 |
3.12 |
Link |
1.0.0 |
2.7 |
3.13 |
Link |
1.0.0 |
2.8 |
3.10 |
Link |
1.0.0 |
2.8 |
3.11 |
Link |
1.0.0 |
2.8 |
3.12 |
Link |
1.0.0 |
2.8 |
3.13 |
Link |
1.0.0 |
2.9 |
3.10 |
Link |
1.0.0 |
2.9 |
3.11 |
Link |
1.0.0 |
2.9 |
3.12 |
Link |
1.0.0 |
2.9 |
3.13 |
Link |
0.3.2 |
2.5 |
3.10 |
Link |
0.3.2 |
2.5 |
3.11 |
Link |
0.3.2 |
2.5 |
3.12 |
Link |
0.3.2 |
2.6 |
3.10 |
Link |
0.3.2 |
2.6 |
3.11 |
Link |
0.3.2 |
2.6 |
3.12 |
Link |
0.3.2 |
2.7 |
3.10 |
Link |
0.3.2 |
2.7 |
3.11 |
Link |
0.3.2 |
2.7 |
3.12 |
Link |
0.3.2 |
2.8 |
3.10 |
Link |
0.3.2 |
2.8 |
3.11 |
Link |
0.3.2 |
2.8 |
3.12 |
Link |
0.3.2 |
2.9 |
3.12 |
Link |
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Neighborhood Attention Transformer.
- Official Repo: SHI-Labs/NATTEN
- Pre-built Source: lldacing's HF
Package Version | PyTorch Ver | Python Ver | CUDA Ver | Download Link |
---|---|---|---|---|
0.17.5 |
2.6.0 |
3.10 |
12.6 |
Link |
0.17.5 |
2.6.0 |
3.11 |
12.6 |
Link |
0.17.5 |
2.6.0 |
3.12 |
12.6 |
Link |
0.17.5 |
2.7.0 |
3.10 |
12.8 |
Link |
0.17.5 |
2.7.0 |
3.11 |
12.8 |
Link |
0.17.5 |
2.7.0 |
3.12 |
12.8 |
Link |
0.17.3 |
2.4.0 |
3.10 |
12.4 |
Link |
0.17.3 |
2.4.0 |
3.11 |
12.4 |
Link |
0.17.3 |
2.4.0 |
3.12 |
12.4 |
Link |
0.17.3 |
2.4.1 |
3.10 |
12.4 |
Link |
0.17.3 |
2.4.1 |
3.11 |
12.4 |
Link |
0.17.3 |
2.4.1 |
3.12 |
12.4 |
Link |
0.17.3 |
2.5.0 |
3.10 |
12.4 |
Link |
0.17.3 |
2.5.0 |
3.11 |
12.4 |
Link |
0.17.3 |
2.5.0 |
3.12 |
12.4 |
Link |
0.17.3 |
2.5.1 |
3.10 |
12.4 |
Link |
0.17.3 |
2.5.1 |
3.11 |
12.4 |
Link |
0.17.3 |
2.5.1 |
3.12 |
12.4 |
Link |
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Triton is a language and compiler for writing highly efficient custom deep-learning primitives. Not officially supported on Windows, but a fork provides pre-built wheels.
- Windows Fork: woct0rdho/triton-windows
-
Installation:
pip install -U "triton-windows<3.5"
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A lightweight wrapper around CUDA custom functions, particularly for 8-bit optimizers, matrix multiplication (LLM.int8()), and quantization functions.
- Official Repo: bitsandbytes-foundation/bitsandbytes
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- Nodes: ComfyUI-RadialAttn
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All wheel information in this repository is managed in the wheels.json
file, which serves as the single source of truth. The tables in this README are automatically generated from this file.
This provides a stable, structured JSON endpoint for any external tool or application that needs to access this data without parsing Markdown.
You can access the raw JSON file directly via the following URL:
https://raw.githubusercontent.com/wildminder/AI-windows-whl/main/wheels.json
Example using curl
:
curl -L -o wheels.json https://raw.githubusercontent.com/wildminder/AI-windows-whl/main/wheels.json
The file contains a list of packages
, each with its metadata and an array of wheels
, where each wheel object contains version details and a direct download url
.
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Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have found a new pre-built wheel or a reliable source, please fork the repo and create a pull request, or simply open an issue with the link.
This repository is simply a collection of links. Huge thanks to the individuals and groups who do the hard work of building and hosting these wheels for the community:
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
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Free-LLM-Collection is a curated list of free resources for mastering the Legal Language Model (LLM) technology. It includes datasets, research papers, tutorials, and tools to help individuals learn and work with LLM models. The repository aims to provide a comprehensive collection of materials to support researchers, developers, and enthusiasts interested in exploring and leveraging LLM technology for various applications in the legal domain.

XiaoXinAir14IML_2019_hackintosh
XiaoXinAir14IML_2019_hackintosh is a repository dedicated to enabling macOS installation on Lenovo XiaoXin Air-14 IML 2019 laptops. The repository provides detailed information on the hardware specifications, supported systems, BIOS versions, related models, installation methods, updates, patches, and recommended settings. It also includes tools and guides for BIOS modifications, enabling high-resolution display settings, Bluetooth synchronization between macOS and Windows 10, voltage adjustments for efficiency, and experimental support for YogaSMC. The repository offers solutions for various issues like sleep support, sound card emulation, and battery information. It acknowledges the contributions of developers and tools like OpenCore, itlwm, VoodooI2C, and ALCPlugFix.
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AI-windows-whl
AI-windows-whl is a curated collection of pre-compiled Python wheels for difficult-to-install AI/ML libraries on Windows. It addresses the common pain point of building complex Python packages from source on Windows by providing direct links to pre-compiled `.whl` files for essential libraries like PyTorch, Flash Attention, xformers, SageAttention, NATTEN, Triton, bitsandbytes, and other packages. The goal is to save time for AI enthusiasts and developers on Windows, allowing them to focus on creating amazing things with AI.

azhpc-images
This repository contains scripts for installing HPC and AI libraries and tools to build Azure HPC/AI images. It streamlines the process of provisioning compute-intensive workloads and crafting advanced AI models in the cloud, ensuring efficiency and reliability in deployments.

Aidan-Bench
Aidan Bench is a tool that rewards creativity, reliability, contextual attention, and instruction following. It is weakly correlated with Lmsys, has no score ceiling, and aligns with real-world open-ended use. The tool involves giving LLMs open-ended questions and evaluating their answers based on novelty scores. Users can set up the tool by installing required libraries and setting up API keys. The project allows users to run benchmarks for different models and provides flexibility in threading options.

llm-chatbot-python
This repository provides resources for building a chatbot backed by Neo4j using Python. It includes instructions on running the application, setting up tests, and installing necessary libraries. The chatbot is designed to interact with users and provide recommendations based on data stored in a Neo4j database. The repository is part of the Neo4j GraphAcademy course on building chatbots with Python.
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weave
Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack
LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

VisionCraft
The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

kaito
Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT
PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

tabby
Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

spear
SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick
Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.