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Microsoft’s Windows AI platform is a developer stack, not one model or a feature reserved for Copilot+ PCs. Its current umbrella, Microsoft Foundry on Windows, brings together ready-made Windows AI APIs, Foundry Local for running open-source models on-device, and Windows ML for deploying custom models. Which route fits depends on your model, target hardware, and the particular API’s availability.
What is Microsoft Foundry on Windows?
Microsoft Foundry on Windows is Microsoft’s current umbrella for developer tools and runtimes that support AI workloads on Windows devices. It is distinct from Microsoft Foundry, the separately named cloud offering. Microsoft’s terminology has shifted: its documentation identifies “Windows AI Foundry” as an older 2025 umbrella term, so check that a guide or sample matches the current product names and APIs.
The umbrella is useful for understanding the platform, but the three components serve different purposes. You do not need to adopt all of them for an application.
| Path | Best fit | What it provides |
|---|---|---|
| Windows AI APIs | Apps that need a supported, ready-to-use AI capability | Windows-provided language and vision capabilities, backed by local models on supported devices. |
| Foundry Local | Developers who want to run supported open-source models locally | A local model runtime with an SDK and an OpenAI-compatible API, according to Microsoft’s overview. |
| Windows ML | Developers deploying their own models | A runtime for on-device inference, including custom ONNX models, across supported CPUs, GPUs, and NPUs. |
These options are all oriented toward Windows development, but they are not interchangeable: the built-in APIs trade some model control for convenience, while local-model and custom-model paths put more responsibility on the developer to choose and validate a model and its supported execution path.
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What are Windows AI APIs?
Windows AI APIs expose ready-made capabilities—such as language and vision features—so an application can use supported AI functionality without selecting and packaging a model in the same way it would for a bring-your-own-model workflow. Microsoft says these APIs use Windows-local models on supported devices. Their availability is not uniform: the specific API, Windows build, hardware, and release channel all matter.
Some APIs or hardware paths may be limited to Copilot+ PCs, while Microsoft documentation also describes supported GPUs and recommended CPU specifications for certain APIs. Microsoft has said some Windows AI APIs are available on CPU and GPU beyond Copilot+ PCs in preview. Treat that as API-specific availability, not a promise that every Windows AI API runs on every PC. Check the requirements for the exact API and target device.
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Where Phi Silica fits
Phi Silica is a Microsoft small language model optimized for local Windows use. Microsoft Support describes it as running on Copilot+ PCs and being accessible to applications through Windows AI APIs. It is one model in the ecosystem—not another name for Microsoft Foundry on Windows, Windows AI APIs, or the whole Windows AI platform.
Does Windows AI require a Copilot+ PC?
No—not for every Windows AI development path. Microsoft’s Copilot+ developer guidance describes the category as using an NPU capable of more than 40 trillion operations per second (TOPS). That is the Copilot+ hardware baseline in that guidance, not a universal threshold for building Windows AI applications.
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Some Windows AI capabilities are tied to Copilot+ hardware, and Phi Silica is documented for Copilot+ PCs. Other APIs have documented GPU or recommended CPU paths, and Microsoft has described some of those broader paths as previews. Windows ML is intended to support inference across CPUs, GPUs, and NPUs, but that does not guarantee that a particular model or execution provider works on every processor.
- Choose a Copilot+ PC when the API or model you need specifically requires that class of device, or when your target users are expected to have its NPU.
- Consider a supported GPU or CPU device when the chosen API lists that hardware path and its availability suits your app’s release plans.
- Validate the actual target configuration when using Windows ML or local models: check model compatibility, runtime and execution-provider support, and the required Windows build.
How do I run AI models locally on Windows?
First decide whether you want a Windows-provided capability or control over the model. “Local” describes where inference runs; it does not by itself identify the API, guarantee support on a particular device, or mean that every part of an application operates offline.
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- For a ready-made capability: identify the Windows AI API that provides the feature your app needs. Follow that API’s documentation for its Windows build, hardware, SDK, and release-channel requirements.
- For an open-source model: evaluate Foundry Local and confirm that the model you want is currently supported. Microsoft describes an SDK and OpenAI-compatible API, but model and SDK details can change.
- For a custom model: evaluate Windows ML for on-device inference. Confirm that your model format and the target device’s CPU, GPU, or NPU execution path are supported.
- Test on the intended device class and Windows build: do not infer availability from a platform-level name. Check current API-specific documentation and validate the release channel your users will run.
How should developers choose among the three paths?
The main trade-off is integration effort versus control. A Windows AI API gives an app a supported capability without requiring the developer to choose a model for that capability. Foundry Local and Windows ML allow more control over model selection or deployment, but developers must verify model support and the relevant runtime and hardware path.
- Start with Windows AI APIs when a built-in capability meets the product requirement and its hardware and availability limits match your audience.
- Start with Foundry Local when you specifically want a supported open-source model to execute locally through its runtime and SDK.
- Start with Windows ML when you need to deploy a custom model and want an on-device inference route spanning supported processor types.
Before committing, verify the exact API or model, required Windows version and release channel, and target hardware. The platform’s broad CPU/GPU/NPU story describes intended paths, not blanket compatibility for every combination.
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