Windows ML GA: On-Device AI Runtime for Windows 11 with ONNX and Execution Providers

Microsoft has made Windows ML generally available, delivering a built-in, on-device AI inference runtime for Windows 11 that aims to let developers run ONNX models across CPUs, GPUs and NPUs without requiring cloud trips for every inference.

Background: why this release matters​

Windows ML (Windows Machine Learning) was first shown publicly at Microsoft Build 2025 and moved from preview to general availability in late September 2025. The runtime is shipped as part of the Windows App SDK (starting with v1.8.1) and explicitly targets devices running Windows 11, version 24H2 (build 26100) or later. That combination—an OS-level API surface for ML together with a system-wide ONNX Runtime and managed hardware execution providers—represents a deliberate pivot by Microsoft toward local AI on Windows devices.
This shift reflects two converging trends. First, modern PCs increasingly include heterogeneous silicon—high-performance GPUs, power-efficient NPUs in mobile and laptop platforms, and ever-faster CPUs—allowing serious model inference on-device. Second, enterprises and consumers are demanding lower-latency, privacy-preserving AI experiences that do not automatically stream sensitive data to cloud services. Windows ML attempts to sit squarely in the middle: a unified runtime and tooling layer that abstracts the hardware while keeping the data local by default.

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Overview of what Windows ML delivers​

Windows ML is more than a single DLL—it's a platform-level approach to local inference that combines several elements:

• A shared ONNX Runtime supplied by the OS, so applications no longer need to package a private copy of ONNX Runtime with every install.
• Mechanisms to dynamically download and register execution providers (EPs) for different silicon (for example, NVIDIA’s TensorRT for RTX, Intel’s OpenVINO EP, Qualcomm’s QNN EP), so apps don’t need to ship vendor-specific binaries.
• Two API layers exposed to developers: a high-level ML Layer for rapid integration and generative AI loop helpers, and a runtime/ONNX layer for low-level, fine-grained control.
• Integration into the Windows App SDK and developer tooling such as Visual Studio and AI-focused extensions for VS Code, plus conversion/optimization helpers for moving models from PyTorch, TensorFlow, or TFLite into ONNX.

Key practical implications for developers: smaller app footprints (no bundled runtimes or EPs), simpler deployment of hardware-optimized inference, and an OS-managed path for getting the latest vendor optimizations. For end users, the promise is faster features (lower latency), improved privacy (data remains on-device when possible), and a more consistent behavior across a wide range of Windows hardware.

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Technical underpinnings — how Windows ML actually works​

ONNX as the lingua franca​

Windows ML relies on ONNX as its canonical model format. Developers convert models from PyTorch, TensorFlow or other frameworks to ONNX and deploy them via the Windows ML APIs. ONNX provides a standardized operator set and broad tooling, which is what enables Windows ML to act as a hardware abstraction layer: the OS can forward a model to whichever vendor execution provider is most appropriate.

System-wide ONNX Runtime and execution providers​

Instead of bundling ONNX Runtime and multiple EPs with each app, Windows ML provides a system-managed ONNX Runtime and supports dynamic EP management. Execution providers—small, vendor-maintained components that implement optimized kernels for specific silicon—are registered on the device and can be updated independently. This architecture has three immediate advantages:

• Apps shrink because they don’t package large vendor libraries.
• Hardware-specific improvements reach users faster because partners can ship EP updates independently.
• The runtime can orchestrate splitting work between CPU/GPU/NPU where possible for efficiency.

Vendor acceleration: TensorRT, OpenVINO, QNN and more​

Hardware partners have collaborated on specialized EPs:

• NVIDIA supplies a TensorRT for RTX EP optimized for GeForce/RTX GPUs and engineered to use just-in-time engine builds that are tuned to the specific GPU in seconds, offering substantial throughput gains for many workloads.
• Intel exposes an OpenVINO-backed EP to accelerate inference across Intel CPU, GPU and NPU stacks.
• Qualcomm provides a QNN EP for Snapdragon X Series NPUs and accompanying optimizations for Copilot+ Snapdragon Windows PCs.
• Other silicon partners (AMD and various OEMs) will provide EPs or integrate with the system runtime to offer vendor-specific acceleration.

Those EPs are the performance-critical layer: the Windows ML runtime handles model loading and dispatch, while the execution provider implements the fastest kernels for a given operation on the target silicon.

Tooling: conversion, optimization and profiling​

Microsoft released developer tooling—an AI Toolkit for VS Code and integration with the Windows App SDK—covering:

• Model conversion templates (PyTorch/TensorFlow → ONNX).
• Quantization and optimization steps (INT8, FP16/FP8 where supported).
• Ahead-of-time (AOT) model compilation options, profiling, and memory/compute tradeoff tuning.

The goal is to let teams move from prototype to product faster while taking advantage of vendor-specific accelerations without writing low-level GPU or NPU code.

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Getting started: practical steps for developers​

• Ensure the target device runs Windows 11, version 24H2 (build 26100) or later.
• Update project dependencies to Windows App SDK v1.8.1 or newer.
• Convert your trained model to ONNX (or use an ONNX-exportable architecture).
• Integrate Windows ML via the ML Layer for fast development or the Runtime Layer for advanced control.
• Use the AI Toolkit (VS Code) or Visual Studio templates to profile and select execution providers; test across the hardware you intend to support.
• For C#, C++ or Python projects, follow the Windows ML migration guidance to adopt the shared ONNX Runtime and remove bundled runtimes.

These steps are intentionally short; Microsoft’s documentation provides code snippets and samples to shorten the friction further. The migration path from standalone ONNX Runtime to the Windows-supplied runtime is also documented to minimize surprises.

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Where Windows ML will be used first: real-world scenarios​

Windows ML’s design favors features that benefit from local inference:

• Creative tools and pro editing — Real-time scene detection, semantic search, noise reduction, audio tagging and faster local render pre-checks in video and photo editors.
• Productivity features — Local document summarization, on-device OCR and natural language features embedded in office apps or custom LOB (line-of-business) software.
• Security and enterprise — On-device deepfake detection, scam identification and telemetry preprocessing that reduces data leakage risk.
• Gaming and audiovisual — Real-time AI upscaling, adaptive assets and live commentary or captioning with low latency.
• Edge applications — Healthcare diagnostics tools, industrial inspection and local analytics where connectivity is unreliable or data privacy rules prevent cloud transmission.

Several ISVs—graphic and media tool vendors, security companies and specialized photo/video editing firms—already piloted integration during previews and reported significant adoption speedups.

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Strengths and immediate benefits​

• Performance and latency: On-device inference removes network round-trips. Coupled with vendor EPs, some workloads can run orders of magnitude faster compared to cloud round-trips for small models and significantly faster than unaccelerated local inference for large models.
• Privacy and compliance: Keeping inference local reduces the surface area for data exfiltration and simplifies compliance for sensitive data use cases.
• Smaller app sizes: By using a system-wide runtime and dynamic EPs, apps avoid bundling multiple large binaries, reducing download/install sizes.
• Consistency across hardware: The runtime’s EP orchestration aims to provide a consistent developer API even as the underlying hardware varies, reducing platform fragmentation work.
• Faster delivery of optimizations: Vendors can ship EP improvements independently, so end-users can benefit from performance gains without app updates.

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Risks, limitations and technical caveats​

While the architecture is sound, several practical and strategic risks deserve attention:

• Model size and memory pressure: Large modern generative models still strain RAM and VRAM on many devices. Windows ML eases deployment, but it does not eliminate the physical limits of a device’s memory and thermal envelope.
• Hardware fragmentation: Execution providers solve many issues, but heterogeneity remains. Not every device has an NPU or an RTX-class GPU, and behaviors will vary. Testing across the lowest-end to highest-end target hardware remains essential.
• Dependency and supply-chain concerns: The runtime dynamically downloads EPs. That reduces app bloat but adds a new supply-chain vector—if EP distribution or update mechanisms are compromised, an attacker could potentially push malicious binaries. Enterprises will need to validate update controls and trust anchors.
• Versioning and determinism: Automatic updates to ONNX Runtime or EPs can change numerical behavior subtly. For regulated or safety-critical apps, this non-determinism is a liability; teams must adopt robust CI and validation to catch regressions when the system runtime updates.
• Learning curve for developers: Although tooling exists, teams new to ONNX or hardware-accelerated inference will encounter a learning curve—quantization strategies, operator support mismatches and runtime profiling remain specialized tasks.
• Licensing and IP considerations: Shipping models or optimized engines may bring licensing constraints (model licenses, third-party libraries) and potential intellectual property exposure on device. Enterprises should adopt clear policies on model distribution and protection.
• Unclear limits for large generative models: While smaller transformer-based models and many vision models scale well, inference for very large LLMs or multimodal models may still require cloud resources or a split-processing approach; Windows ML’s roadmap for efficient model partitioning across device and cloud remains nascent.

When any claim about performance or "orders of magnitude" improvements is quoted, it’s important to validate it against the specific benchmark, GPU/NPU architecture, model and settings used for measurement. Performance wins cited by vendors often use favorable conditions (newest GPUs, optimized kernels, ideal quantization) and may not represent the average end-user device.

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Security and operational considerations for enterprises​

Enterprises evaluating Windows ML should plan for:

• Controlled EP distribution: Ensure corporate devices trust only vetted EPs; use enterprise update policies to delay or review vendor EP updates when needed.
• Model governance: Treat models like code—version them, test them against representative data, and maintain rollback plans.
• Monitoring and fallback: Build telemetry to detect regression or performance anomalies after runtime updates, and implement fallback paths if an EP behaves unexpectedly.
• Regulatory compliance: For industries with strict data residency or audit requirements, verify whether on-device processing meets local regulations and document the system behavior.

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Competitive context: Windows ML vs Apple Core ML and others​

Apple’s Core ML has been a notable leader in on-device ML for years, tightly integrated with iOS and macOS hardware (including Apple’s specialized Neural Engines). Windows ML takes a different tack: rather than building around a single silicon ecosystem, it focuses on heterogeneous, vendor-provided EPs and an OS-managed runtime to support a wide, diverse PC ecosystem.
That tradeoff means:

• Apple: Tight vertical integration, often best-per-model optimizations for Apple silicon, a single vendor stack that enables deep control.
• Windows: Broad reach across billions of Windows devices with many possible accelerators, relying on vendor collaboration to deliver optimized execution providers.

For developers, the choice will depend on target user base: create for Apple when the user set is Apple-centric and vertical optimizations matter; choose Windows ML for broad reach across laptops, desktops and Copilot+ PCs where local NPUs and RTX-class GPUs coexist.

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Early third-party feedback and real-world benchmarks​

Independent developer feedback from early adopters—media and AI tooling companies—reports concrete benefits: less engineering time spent on bundling and targeting multiple EPs, and tangible throughput gains when using vendor EPs (for example, NVIDIA’s TensorRT for RTX shows substantial speedups versus more generic runtimes in vendor tests). However, those benchmarks are environment-specific. Real-world gains will vary by device class, model architecture and runtime configuration.
A word of caution: vendor benchmark claims (for example, >50% throughput improvements on certain RTX hardware) are plausible and have been demonstrated in controlled tests. Those numbers should not be treated as universal; developers should run representative benchmarks on their actual target hardware to form realistic expectations.

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Developer advice: practical tips and gotchas​

• Profile early and often. Measure memory usage, latency and throughput on the specific hardware families you intend to support.
• Keep an eye on opset compatibility. ONNX opset differences can cause model conversion headaches—validate conversions end-to-end.
• Test EP fallbacks. Build logic to gracefully fall back to CPU inference or alternative EPs if a preferred EP is unavailable.
• Use quantization carefully. Quantization reduces memory and increases throughput, but it can degrade quality; tune and validate for each model class.
• Plan for runtime updates. Treat the system ONNX Runtime and EPs as a dependency that can change independently; include regression tests as part of release processes.
• Prepare data governance docs. If your app processes PII or regulated data, document how Windows ML keeps data on-device and what telemetry (if any) is emitted.

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The broader impact: what Windows ML could unlock​

Windows ML lowers technical barriers for getting AI into a much wider set of Windows apps. Expect a wave of innovation in several areas:

• Everyday productivity apps gaining offline summarization, smarter search and contextual assistance without cloud costs.
• Media tools adding fast local AI-powered auto-edit and enhancement features accessible to non-pro users.
• Security software incorporating real-time, on-device detection for manipulated content or phishing attempts.
• Specialized vertical apps (medical imaging, industrial inspection) benefiting from low-latency, local model execution without requiring continuous connectivity.

If vendor EP ecosystems mature and model partitioning between device and cloud becomes seamless, hybrid workflows (local UX + cloud-heavy refinement) will become the norm, giving developers the best of both worlds: responsiveness and scale.

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Conclusion: a pragmatic leap toward local-first AI on Windows​

Windows ML is a pragmatic, infrastructure-level move that recognizes the reality of heterogeneous PC silicon and the demand for privacy and low-latency AI. By centralizing the ONNX runtime at the OS level and delegating per-silicon optimization to vendor-maintained execution providers, Microsoft has created a scalable path for local AI on Windows devices. The approach reduces app complexity, offers performance benefits when vendor EPs are available, and provides enterprises with the opportunity to process sensitive data locally.
That said, it is not a silver bullet. Model size limits, memory constraints, hardware diversity, supply-chain and update governance, and developer education remain real hurdles. Organizations should treat Windows ML as a powerful new option in their toolbox—very well suited to many use cases, but requiring deliberate engineering and governance to realize its full potential safely and reliably.
Caveats: some vendor performance numbers and early ISV anecdotes reflect controlled testbeds and specific hardware; developers and IT teams should benchmark using representative workloads and devices. Teams with strict determinism or certification requirements will need to manage runtime and EP updates carefully to avoid unexpected behavior.
Windows ML's arrival marks a meaningful step in making Windows a first-class platform for on-device AI—one that is poised to reshape productivity software, creative tools, and enterprise applications as vendors, ISVs and developers iterate on models and delivery practices in the months ahead.

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Source: WebProNews Microsoft Launches Windows ML for Local AI on Devices