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Windows ML is Microsoft’s Windows-supported framework for running machine-learning inference locally inside Windows applications. It is powered by ONNX Runtime: an application loads a compatible model, sends it input data, and receives predictions or generated output. CPU execution is the baseline; supported GPUs and NPUs can be used through execution providers when the Windows release, device, drivers, model operators, and provider all line up.
That makes Windows ML a developer framework, not a consumer AI feature that automatically makes every model run on every PC. You may need to convert a model to ONNX, acquire a hardware-specific provider, package the runtime, and handle fallback when a preferred accelerator is unavailable.
What Windows ML does—and what it does not do
Windows ML supplies a Windows-oriented path for adding local AI inference to an application. Microsoft describes it as powered by ONNX Runtime and designed to work with ONNX models produced by workflows involving PyTorch, TensorFlow or Keras, TensorFlow Lite, and scikit-learn. Models created in those ecosystems may require conversion to ONNX before they can use the standard Windows ML workflow.
Inference means applying an already-trained model to new input: classifying an image, transcribing audio, detecting objects, generating an embedding, or producing another model-defined output. Windows ML does not train a model. Training, conversion, quantization, graph optimization, and validation remain separate development tasks, although they affect whether an inference deployment works well.
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Framework versus end-user feature
A Windows ML application still needs a model, input and output handling, user-interface or service code, and a data policy. Installing Windows or Windows ML does not provide a universal catalog of ready-to-use models. The framework gives the application a supported runtime and a way to select execution hardware.
How an ONNX model runs in a Windows application
The normal path is a pipeline. Each stage has a different failure mode, so treating them separately makes deployment easier to diagnose.
- Obtain or convert the model. Start with an ONNX model whose operators, input shapes, data types, and output names are documented. A model from PyTorch, TensorFlow/Keras, TFLite, or scikit-learn may need conversion. Test the converted file against known inputs before integrating it into the app.
- Add the Windows ML and application dependencies. Choose the language and Windows App SDK arrangement, then install the packages required by that path. The complete Windows ML API surface currently documented for .NET requires .NET 8 or later. The relevant C++ setup uses C++20, and the Python setup documents Python 3.10 through 3.13. These are setup requirements, not guarantees that every model or provider supports every version combination.
- Load the model through ONNX Runtime APIs. The app creates a runtime session, reads the model metadata, and prepares tensors or other inputs in the exact shape and type the model expects. ONNX Runtime is responsible for graph execution; Windows ML provides the Windows-supported integration and provider management.
- Select an execution provider. CPU is the documented starting point and the broadest fallback. The app can request a provider for a compatible GPU or NPU, but provider selection must account for the machine, driver, Windows build, model operators, and provider package.
- Submit inputs and read outputs. The app feeds tensors to the session and receives the model’s outputs. Post-processing—such as decoding class IDs, applying confidence thresholds, converting tokens to text, or drawing detection boxes—is application code, not a universal Windows ML operation.
- Validate and monitor fallback. A provider can be installed yet fail for a particular operator, shape, data type, or driver. Keep a CPU path for reliability, log which provider was actually selected, and test representative inputs on every hardware tier you intend to support.
CPU, GPU, and NPU: choosing an execution target
Microsoft’s guidance is qualitative rather than a universal speed ranking. Performance depends on the exact model, device, provider, input size, precision, memory pressure, and workload. A result measured on one laptop cannot be applied to all Windows PCs.
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|---|---|---|
| CPU | General-purpose fallback and the documented first implementation path | Available as the broad compatibility baseline, but throughput and power use vary with the processor and model. |
| GPU | High-throughput image, video, and generative-AI workloads | Requires a compatible provider, graphics hardware, driver, model graph, and Windows configuration. |
| NPU | Sustained on-device inference with an emphasis on battery efficiency | Availability and supported operators depend on the NPU, vendor provider, driver, Windows release, and model. |
Use the table as a workload guide, not a benchmark. For a meaningful comparison, record the model version, device, Windows build, provider version, input dimensions, precision, batch size, warm-up policy, and whether the measurement is latency, throughput, or energy use.
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Execution providers and Windows version requirements
An execution provider is the component that maps ONNX Runtime operations to a particular hardware backend. Windows ML includes CPU and DirectML providers. Other providers in Microsoft’s catalog are acquired separately rather than included in the base runtime.
The current 2.x catalog lists hardware-optimized options including AMD MIGraphX and VitisAI, NVIDIA NvTensorRtRtx, Intel OpenVINO, Qualcomm QNN, and experimental WebGPU. The list and support status are version-sensitive. Check the current provider release, vendor prerequisites, licensing terms, and supported operators before making one a hard dependency.
The Windows 11 24H2 condition
Microsoft’s provider documentation describes catalog-based hardware-optimized providers as available on Windows 11 version 24H2, build 26100, or later, subject to compatible hardware and drivers. This requirement applies to those acquired hardware-optimized providers; it should not be confused with the minimum Windows version your application targets through the Windows App SDK.
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Deployment: self-contained or framework-dependent
Windows ML documents two principal deployment strategies. The choice affects package size, update control, installation prerequisites, and support workload.
| Deployment model | How it works | Trade-off |
|---|---|---|
| Self-contained | The app bundles the Windows ML runtime, including the Windows ML API, ONNX Runtime engine, and DirectML provider. | Larger package, but no dependency on an already installed runtime and direct control over when runtime updates ship. |
| Framework-dependent | The app uses a separately installed Windows App SDK runtime. | Smaller app package and shared servicing updates, but installation depends on that runtime being present and correctly serviced. |
Microsoft estimates the self-contained runtime binaries at approximately 41 MB. That is an approximate Microsoft figure for the listed runtime components, not an independent measurement of a complete application installer. Vendor execution providers are not included in that runtime estimate; they must be acquired separately or bundled by the developer.
Windows ML and DirectML are related, not interchangeable
DirectML is a lower-level machine-learning API built around Direct3D 12. Windows ML uses ONNX Runtime APIs and provides a higher-level route for Windows applications that need model execution and provider management.
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Model compatibility: checks to make before coding
Having an ONNX filename is not enough to establish compatibility. Before selecting a provider, check:
- Opset and operators: confirm that the model’s operators are implemented by ONNX Runtime and by the provider you plan to use.
- Shapes and dynamic dimensions: document required image sizes, sequence lengths, batch limits, and any dynamic-shape behavior.
- Data types: verify whether the graph expects floating-point, integer, quantized, or mixed-precision tensors.
- Inputs and outputs: record names, layout conventions such as channel order, and post-processing requirements.
- Conversion accuracy: compare outputs from the original framework and the ONNX model on representative data.
- Provider fallback: test what happens when an unsupported node remains on the CPU or when provider creation fails entirely.
Conversion and optimization can change numerical results. Establish an application-specific tolerance and test edge cases rather than assuming that a successful session creation proves correctness.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Privacy and performance boundaries
Local inference means the model computation can run on the device. It does not prove that an application is fully offline or that input data never leaves the PC. An app may download models or providers, contact a service for unrelated features, collect telemetry, or implement its own cloud fallback. Review the application’s network behavior, privacy policy, and data-handling code before making a no-upload promise.
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Likewise, Windows ML documentation does not provide one CPU-versus-GPU-versus-NPU speedup that applies to all systems. Publish or rely on a benchmark only with its model, device, provider, Windows build, driver, workload, and measurement method identified.
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Implementation checklist for a Windows ML app
- Define the target matrix: list supported Windows versions, CPU architectures, GPU or NPU families, drivers, and language/runtime versions.
- Choose the model format: use ONNX for the standard path and retain the conversion script and validation set for models originating elsewhere.
- Start with CPU: implement and verify inference before adding an accelerator provider. This isolates model and pre/post-processing bugs from hardware issues.
- Add providers deliberately: install or bundle only the providers that match your target devices and licensing requirements. Detect availability at runtime.
- Measure the real workload: separate cold-start time, warm latency, throughput, memory use, and energy behavior. State all test conditions.
- Select deployment packaging: choose self-contained when runtime independence and update timing matter more than package size; choose framework-dependent when sharing a serviced Windows App SDK runtime is acceptable.
- Plan updates: retest models when updating ONNX Runtime, Windows ML, a provider, a driver, or the Windows build. Provider behavior can change across versions.
- Document privacy: state whether models are bundled, downloaded, or replaced remotely, and whether any input or telemetry leaves the device.
Troubleshooting common failures
The model will not load
Check that the file is valid ONNX, that its opset is supported, and that the app is using the intended architecture and runtime package. If the model came from another framework, repeat conversion and compare outputs before investigating hardware.
The accelerator provider cannot be created
Verify the Windows build, device presence, driver version, provider package, and any vendor prerequisites. On catalog-based hardware-optimized providers, confirm Windows 11 24H2 (build 26100) or newer. Keep CPU as a controlled fallback so the app can distinguish provider setup failure from model failure.
Only part of the graph uses the GPU or NPU
Inspect provider logs and operator support. Unsupported nodes may execute on the CPU, which can introduce transfer overhead and erase the expected benefit. Simplifying the graph, changing data types, or selecting a provider with broader operator coverage may help, but each change requires accuracy testing.
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Results differ after conversion or acceleration
Compare intermediate or final outputs with the original framework using the same inputs, preprocessing, precision, and tolerances. Differences can come from conversion, quantization, unsupported operator substitutions, or floating-point behavior rather than Windows ML itself.
The packaged app works on the developer PC but not another machine
Check whether the deployment is framework-dependent, whether the required Windows App SDK runtime is installed, and whether the target device has the expected driver and provider. A self-contained package removes one runtime-installation dependency but does not bundle every vendor provider automatically.
Quick Recap
What to remember
- Windows ML is a Windows-supported, ONNX Runtime-powered framework for local inference in applications.
- ONNX is the standard model path; models from other frameworks may need conversion.
- CPU is the baseline. GPU and NPU use depends on execution providers, hardware, drivers, Windows versions, and model compatibility.
- Catalog-based hardware-optimized providers currently require Windows 11 24H2, build 26100 or later, plus compatible hardware and drivers.
- Self-contained deployment trades roughly 41 MB of Microsoft-estimated runtime binaries for independence from an installed runtime; framework-dependent deployment trades a smaller package for that dependency.
- DirectML remains supported in sustained engineering, while Microsoft has moved new Windows ONNX Runtime feature development toward Windows ML.
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