Key Points:
- Windows ML is now generally available for production use, allowing developers to deploy AI models directly on Windows devices.
- This new technology enables developers to build AI experiences that are more responsive, private, and cost-effective, and can run on a wide range of Windows hardware.
- Microsoft has collaborated with silicon partners, including AMD, Intel, NVIDIA, and Qualcomm, to optimize Windows ML for their latest CPUs, GPUs, and NPUs.
Microsoft announced that Windows ML, a built-in AI inferencing runtime, is now available for production use. This technology enables developers to deploy AI models directly on Windows devices, allowing for more responsive, private, and cost-effective AI experiences. Windows ML is optimized for the latest hardware from Microsoft’s silicon partners, including AMD, Intel, NVIDIA, and Qualcomm. This collaboration ensures that Windows ML can fully leverage the strengths of each partner’s CPUs, GPUs, and NPUs for AI workloads.
Windows ML is compatible with ONNX Runtime (ORT), allowing developers to utilize familiar ORT APIs and enabling easy transition for existing production workloads. The technology also integrates smoothly with current models and workflows, making it easier for developers to use their existing ONNX models or convert and optimize their source PyTorch models. With Windows ML, developers can assign device policies to optimize for low power, high performance, or specify the silicon used for a model.
Microsoft has worked closely with its silicon partners to ensure that Windows ML can fully leverage their latest CPUs, GPUs, and NPUs for AI workloads. For example, AMD has integrated Windows ML support across their Ryzen AI platform, enabling developers to harness the power of AMD silicon via AMD’s dedicated Vitis AI execution provider on NPU, GPU, and CPU. Intel has also collaborated with Microsoft on Windows ML, empowering developers to effortlessly deploy their custom AI models and applications across CPUs, GPUs, and NPUs on Intel Core Ultra processor-powered PCs.
NVIDIA and Qualcomm have also partnered with Microsoft to optimize Windows ML for their hardware. NVIDIA’s TensorRT for RTX EP enables AI models to be executed on NVIDIA GeForce RTX and RTX PRO GPUs using NVIDIA’s dedicated Tensor Core libraries for maximum performance. Qualcomm Technologies and Microsoft have worked together to optimize Windows ML AI models and apps for the Snapdragon X Series NPU using the Qualcomm Neural Network Execution Provider (QNN EP) as well as GPU and CPU through integration with ONNX Runtime EPs.
The adoption of Windows ML is expected to accelerate the proliferation of local AI capabilities across a broad spectrum of applications. Leading software app developers, such as Adobe, BUFFERZONE, Dot Inc., McAfee, Reincubate, Topaz Labs, and Wondershare, are already working on adopting Windows ML in their upcoming releases. By leveraging Windows ML, these software partners can focus on building unique AI-powered features without worrying about hardware differences.
To get started with Windows ML, developers can update their project to use the latest Windows App SDK and call the Windows ML APIs to initialize EPs, load any ONNX model, and start inferencing in just a few lines of code. Microsoft has also provided a range of tools and resources, including the AI Toolkit for VS Code and the AI Dev Gallery, to help developers build and deploy AI-powered apps easily.
The general availability of Windows ML marks a significant milestone in the development of local AI capabilities on Windows devices. With its ability to run AI models directly on Windows devices, Windows ML is expected to usher in a new wave of innovation in AI-enhanced Windows apps. As Microsoft continues to collaborate with its silicon partners and software developers, the possibilities for local AI on Windows devices are likely to expand, enabling new and exciting experiences for users.
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