Official repo for Windows ML - Microsoft's high-performance local AI inferencing framework for Windows.
See the code
Windows ML is the unified and high-performance local AI inferencing framework for Windows, powered by ONNX Runtime. With Windows ML, you can run AI models locally and accelerate inference on NPUs, GPUs, and CPUs through optional execution providers that Windows manages and keeps up to date. You can use models from PyTorch, TensorFlow/Keras, TFLite, scikit-learn, and convert them to ONNX to use them with Windows ML.
Windows ML is Microsoft's recommended local AI inferencing framework for Windows — the official, Windows-native way to run custom and open-source AI models on Windows PCs, with hardware-accelerated inference across CPU, GPU, and NPU. It's built and optimized for Scale, Performance, and Deployment across the Windows ecosystem.
To learn about the benefits of using Windows ML compared to ONNX Runtime directly, see the Windows ML docs.
| Package | Use it for |
|---|---|
 | Recommended way to use Windows ML (supports both framework-dependent and self-contained) |
 | Alternative way to use Windows ML (only self-contained, no Windows App SDK dependency) |
 | Generative AI (Phi, Llama, Mistral, Gemma, DeepSeek…) on top of Windows ML |
To get started with Windows ML, see our documentation on Microsoft Learn.
Windows ML code samples can be found on our documentation on Microsoft Learn.
This repository also contains samples for the native Windows ML Runtime API, the
higher-level Task APIs, and the Windows ML Server. See Samples.
docs contains guides, tutorials, and the API reference for the
Windows ML Runtime API, the Task APIs, and the Windows ML Server.
Tools contains developer tools that support the samples, such as
building llama.cpp backends.
Found a bug, have a question, or want to suggest a sample? Open an issue in this repo — we triage them directly. For broader Windows ML platform discussions or runtime/API issues that span beyond the samples, you can also use the Windows App SDK repo.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.
See LICENSE for code and LICENSE-DOCS for documentation.
Official repo for Windows ML - Microsoft's high-performance local AI inferencing framework for Windows.
See the code
Windows ML is the unified and high-performance local AI inferencing framework for Windows, powered by ONNX Runtime. With Windows ML, you can run AI models locally and accelerate inference on NPUs, GPUs, and CPUs through optional execution providers that Windows manages and keeps up to date. You can use models from PyTorch, TensorFlow/Keras, TFLite, scikit-learn, and convert them to ONNX to use them with Windows ML.
Windows ML is Microsoft's recommended local AI inferencing framework for Windows — the official, Windows-native way to run custom and open-source AI models on Windows PCs, with hardware-accelerated inference across CPU, GPU, and NPU. It's built and optimized for Scale, Performance, and Deployment across the Windows ecosystem.
To learn about the benefits of using Windows ML compared to ONNX Runtime directly, see the Windows ML docs.
| Package | Use it for |
|---|---|
| Recommended way to use Windows ML (supports both framework-dependent and self-contained) |
| Alternative way to use Windows ML (only self-contained, no Windows App SDK dependency) |
| Generative AI (Phi, Llama, Mistral, Gemma, DeepSeek…) on top of Windows ML |
To get started with Windows ML, see our documentation on Microsoft Learn.
Windows ML code samples can be found on our documentation on Microsoft Learn.
This repository also contains samples for the native Windows ML Runtime API, the
higher-level Task APIs, and the Windows ML Server. See Samples.
docs contains guides, tutorials, and the API reference for the
Windows ML Runtime API, the Task APIs, and the Windows ML Server.
Tools contains developer tools that support the samples, such as
building llama.cpp backends.
Found a bug, have a question, or want to suggest a sample? Open an issue in this repo — we triage them directly. For broader Windows ML platform discussions or runtime/API issues that span beyond the samples, you can also use the Windows App SDK repo.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.
See LICENSE for code and LICENSE-DOCS for documentation.
