Windows ML Brings On Device AI to Windows 11 with Dynamic Execution Providers

Microsoft has opened the door for AI features to run natively across Windows PCs with the general availability of Windows ML, a system-managed ONNX Runtime and hardware abstraction layer that lets developers ship AI-enabled apps without bundling vendor runtimes or hand-tuning builds for every silicon variant. This release — now integrated into the Windows App SDK and targeting Windows 11 version 24H2 and later — promises smaller installers, automatic execution-provider selection for CPU/GPU/NPU, and a single on-device inferencing path that scales from lightweight perceptual models to more demanding generative scenarios when the hardware is present.

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Background / Overview​

Windows ML is Microsoft’s renewed push to make on-device AI practical at scale across the Windows ecosystem. The platform bundles a shared copy of the ONNX Runtime into the OS-level runtime and introduces an Execution Provider (EP) model plus a lightweight hardware abstraction layer (HAL) that can automatically detect device silicon and download the most appropriate EP at runtime. That design allows a single app binary to run on millions of devices while benefiting from vendor-specific optimizations when available.
This is not merely an incremental SDK update: Windows ML is positioned as the foundational inferencing layer for Windows AI Foundry and the broader Windows AI platform, which includes tooling such as the AI Toolkit for Visual Studio Code and Foundry Local for curated, optimized models. Microsoft has aligned the release with ecosystem partners — AMD, Intel, NVIDIA, and Qualcomm — who provide the EPs that surface native acceleration for GPUs and NPUs.
Key headline facts verified in Microsoft’s documentation:

• Supported OS: Windows 11 version 24H2 (build 26100) or later.
• Windows App SDK: Windows ML is distributed via the Windows App SDK (1.8.1 or newer).
• Core runtime: A system-managed ONNX Runtime with included CPU and DirectML providers, plus dynamically downloadable vendor EPs (AMD Vitis AI, Intel OpenVINO, Qualcomm QNN, NVIDIA TensorRT for RTX).

Where developer and platform responsibilities end and begin is central: Windows ML handles runtime distribution and EP management, but developers are still responsible for converting, profiling, and optimizing models for device constraints (quantization, operator support, AOT where appropriate). Microsoft’s guidance and tooling reflect that shared responsibility.

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What Windows ML actually is — architecture and mechanisms​

A hardware abstraction layer for ML inferencing​

At its core, Windows ML is a runtime that sits between applications and vendor acceleration stacks. It accomplishes three things:

• Provides a single, system-managed ONNX Runtime so apps need not bundle ORT copies.
• Exposes an Execution Provider Catalog and APIs to detect, download, and register vendor-specific EPs dynamically. This allows the runtime to pick the best backend (CPU, GPU, NPU) at runtime.
• Integrates with Windows AI Foundry and dev tools to make model conversion (PyTorch/TensorFlow → ONNX), quantization, profiling, and deployment more straightforward.

The practical upshot: developers can ship a single installer that remains relatively small while relying on Windows to deliver the most appropriate vendor-optimized binaries to users as they run the app.

Execution Providers (EPs): vendor-optimized backends​

The EP model is central to how Windows ML achieves performance parity with vendor SDKs. Microsoft includes CPU and DirectML providers in the shipped runtime, while vendors publish EPs that the system can download when needed:

• AMD: Vitis AI EP for Ryzen AI/APU acceleration.
• Intel: OpenVINO EP for Core Ultra and integrated accelerators.
• Qualcomm: QNN EP for Snapdragon X-series NPUs.
• NVIDIA: TensorRT for RTX EP for GeForce RTX and RTX PRO GPUs.

EPs are distributed as separate packages and can be updated independently from the OS, enabling faster vendor optimizations without inflating every app package. That separation also means EP availability varies by device OEM, driver version, and silicon generation.

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Windows ML and Windows AI Foundry: model catalogs and tooling​

Windows ML is intentionally part of a broader developer experience. The Windows AI Foundry offers:

• Foundry Local: a curated model catalog of optimized community and open models tuned for Windows silicon.
• AI Toolkit for Visual Studio Code: conversion, quantization, and profiling helpers to prepare models for ONNX and Windows ML.
• AI Dev Gallery: interactive samples and conversion recipes for common tasks.

This tooling aims to shorten the path from research/experiment to product-grade on-device experiences, but the process remains iterative: profiling, quantizing (e.g., QDQ/FP8/FP4), and testing on representative hardware are still required for reliable deployment.

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Supported hardware, partners and real-world claims​

Microsoft, NVIDIA, Qualcomm, Intel, and AMD are publicly collaborating on Windows ML EPs and have provided early performance numbers. The claims fall into two categories: architectural capability (which is verifiable) and vendor performance improvements (which are workload-dependent and vendor-supplied).
What’s verified:

• Windows ML supports x64 and ARM64 Windows 11 24H2 devices and can choose between CPU, GPU, and NPU presences.
• EPs from the major silicon vendors are available for dynamic download/registration through the ExecutionProviderCatalog APIs.

Vendor performance claims (directional, verify with your model):

• NVIDIA’s TensorRT for RTX EP reports “over 50% faster” inference throughput compared with DirectML on specific RTX 5090 benchmarks (vendor-provided). NVIDIA’s technical blog and Windows developer materials present microbenchmarks showing >50% speedups on selected workloads; these are useful signals but workload-dependent and should be validated against the actual models you intend to ship.
• Qualcomm and Intel have public statements describing optimized EPs for their NPUs/XPU stacks and emphasize power/efficiency tradeoffs (NPUs for low-power scenarios; GPUs for peak throughput). These claims align with the platform’s design to let apps opt into power profiles, but exact gains vary by hardware generation and model architecture.

Caveat: vendor benchmarks are valid starting points, not guarantees. Always benchmark using your model graphs, batch sizes, and target datasets on the target SKU(s). If you rely on a specific speed or latency SLA, perform device-level profiling with the AI Toolkit and the vendor EPs to confirm behavior.

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Who’s already adopting Windows ML​

Microsoft named several ISVs planning to adopt Windows ML for in-app inferencing: Adobe, McAfee, Reincubate, Wondershare, Topaz Labs and other creative/security vendors have been cited as early adopters or testers. Use cases highlighted include:

• Adobe: semantic search, scene detection and real-time tagging inside Premiere Pro and After Effects.
• McAfee: on-device detection of deepfakes and scam content.
• Topaz Labs / Lightricks and others: image and video enhancement using vendor EPs for faster on-device processing. NVIDIA has called out Topaz as a rapid integrator in its TensorRT for RTX posts.

These real-world trials underline the practical benefits — especially in latency-sensitive or privacy-critical features — but also expose a range of engineering trade-offs for ISVs moving from cloud-first models to hybrid/local architectures.

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Developer experience: how to get started and common migration steps​

Developers can begin today by updating projects to the Windows App SDK and using the Windows ML APIs. Microsoft’s get-started guidance shows the common flow:

• Install Windows App SDK 1.8.1+ and initialize Windows ML in your app.
• Convert your model to ONNX (AI Toolkit for VS Code and ONNX tooling can help).
• Register and ensure EPs via the ExecutionProviderCatalog (EnsureAndRegisterAllAsync or more selective APIs).
• Profile and iterate: quantize where possible, AOT-compile if helpful, and pick power/performance targets through Windows ML device policy APIs.

Practical tips:

• Start with a baseline CPU/DirectML run and compare across EPs to understand performance and memory trade-offs.
• Keep an eye on operator coverage: some model ops or custom kernels may need conversion or fall back to CPU.
• Use the AI Toolkit profiling to generate representative traces — this is crucial for NPUs where memory layout and supported ops matter.

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Security, privacy and operational considerations​

Running models locally addresses important privacy and latency concerns, but it also changes the operational surface:

• Attack surface and supply chain: Windows ML’s dynamic EP download mechanism reduces app bundle sizes but introduces runtime dependency downloads controlled by the OS. That reduces per-app packaging complexity but centralizes trust in OS-level distribution. Enterprises must review EP trust, update cadence, and how Microsoft/partners publish EP packages.
• Model provenance and integrity: apps that automatically pull models (e.g., Foundry Local) should validate signatures and enforce policy for which models can be executed. Local inference does not remove the need for data governance, model evaluation, and content filtering.
• Version drift and reproducibility: because EPs are updated independently, behavior (latency, numerical outputs, even slight operator semantics) may change with EP updates. Production apps that require deterministic outputs or backward-compatible scoring should pin and test EP and ONNX Runtime versions during validation cycles.

Enterprises and security-conscious developers will want integration points for:

• Auditing and policy controls over which EPs or models a device can download.
• A staging and validation pipeline to check new EP/model combos before broad rollout.
• Failover strategies if EP download fails (e.g., graceful CPU fallback and user notification).

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Performance variability and benchmarking advice​

On-device AI is inherently heterogeneous. Performance depends on:

• Model architecture (transformer vs diffusion vs CNN), operator support, and quantization level.
• Memory constraints (GPU VRAM or NPU local memory), batching strategy, and concurrency with other system workloads (GPU used by rendering, compositing, etc.).
• Driver/EP maturity — early EP releases often optimize specific kernels first; holistic gains come over multiple releases.

Benchmarking guidance:

• Profile using your model with realistic inputs and steps (warm-up, batching, sequence lengths).
• Measure latency, throughput, power consumption (where possible), and memory footprint.
• Test across representative SKUs (discrete RTX GPUs, integrated Ryzen AI/APU, Intel Core Ultra with XPU, Snapdragon X-series).
• Validate numerical fidelity after quantization; establish fallback thresholds if accuracy drops below acceptable limits.

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Risks, limitations and open questions​

Windows ML reduces distribution friction but does not remove several hard problems:

• Not all Windows 11 devices will have NPU EPs or modern NPUs; many machines will still rely on CPU/GPU. Device coverage depends on OEM hardware, drivers, and vendor EP availability. Expect heterogeneous behavior in the field.
• Vendor performance claims are workload-dependent. NVIDIA’s “50% faster” TensorRT numbers come from manufacturer benchmarks on specific hardware and workloads; real-world gains for individual models will vary and should be validated. Treat such numbers as directional, not prescriptive.
• Model size vs. device footprint: large generative models still strain consumer devices; Windows ML facilitates on-device inferencing but cannot magically make a 70B-parameter model fit on a low-end NPU. Practical deployment will often require quantization, distillation, or model partitioning strategies.
• Update and rollback policies: because EPs can be updated independently, app teams and IT must plan for rollbacks or version constraints to avoid regressions across fleets.

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Practical recommendations for ISVs and IT teams​

• Adopt a test matrix mentality: combine OS version (24H2+), EP sets, and hardware SKUs in CI to catch regressions early.
• Build fallback modes (CPU/DirectML) and graceful degradation paths for features when an optimal EP is not present.
• Integrate EP and model signature checks into deployment pipelines; treat the EP catalog like another third-party dependency.
• Use the AI Toolkit and Foundry Local for prototyping, but validate optimizations on final target hardware before shipping.

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The strategic significance for the Windows ecosystem​

Windows ML marks a pragmatic pivot from cloud-only AI experiences to a hybrid-first reality where inferencing lives as close as possible to the user while training and large-scale orchestration remain cloud-centric. For Microsoft, the architecture delivers three strategic wins:

• It helps ISVs reduce distribution complexity and app sizes by offloading EP management to the OS.
• It strengthens Windows’ value proposition for generative and perceptual AI experiences by ensuring developers can reasonably target a large installed base with optimized vendor backends.
• It deepens partnerships with silicon vendors and makes Windows the integration surface where vendor innovation (e.g., TensorRT for RTX) directly benefits apps and users.

This does not eliminate competition — cloud inference, hybrid orchestration, and specialized server deployments remain critical for heavy-duty workloads or centralized governance — but it substantially lowers the barrier for everyday apps to ship snappy, private, and offline-capable AI features.

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Conclusion​

Windows ML is a meaningful, platform-level attempt to normalize on-device AI for the Windows ecosystem. By combining a system-managed ONNX Runtime, a dynamic Execution Provider model, and developer tooling within Windows AI Foundry, Microsoft has created a pragmatic architecture that balances developer ergonomics with vendor-level performance. The release is already attracting interest from creative and security ISVs and backed by EP support from AMD, Intel, NVIDIA, and Qualcomm — but the practical success for any given app will hinge on careful device testing, model optimization, and operational controls.
The platform’s biggest strengths are reduced app overhead, improved latency/privacy for on-device inference, and simplified access to vendor accelerations. Its principal risks are hardware heterogeneity, reliance on vendor-supplied performance claims (which must be validated), and operational complexity introduced by dynamic EP updates. For developers and IT teams, the immediate next steps are straightforward: adopt the Windows App SDK 1.8.1+, convert and profile models for ONNX, and embed EP-aware testing into CI pipelines to make on-device AI reliable at scale.
In short: Windows ML makes it substantially easier to bring AI to local machines — but it replaces distribution headaches with a new set of engineering disciplines. Those who build the discipline — profiling, validation, and controlled rollouts — will capture the benefits of local AI: responsiveness, privacy, and new product experiences on devices every day.

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Source: TechSpot Windows ML debuts for all developers, enabling AI apps to run directly on local hardware