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Most Windows ML failures are compatibility failures before they are coding failures. A computer can have a modern GPU or NPU and still be unable to use the execution provider your app selected because the Windows build, processor architecture, API/package version, device generation, or driver does not meet that provider’s requirements. Start by proving those prerequisites, then choose an appropriate deployment mode, measure latency and throughput separately, and collect an ETW trace if the behavior remains unexplained.
Windows ML is Microsoft’s Windows-supported, ONNX Runtime-based framework for local inference. CPU, GPU, and NPU execution is mediated by execution providers, so the presence of hardware is only the beginning of a compatibility check.
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What Windows ML compatibility actually depends on
Windows ML sits between an ONNX model and the hardware that executes it. The execution provider is the component that maps supported model operations to a CPU, GPU, NPU, or another accelerator. A provider can be unavailable even when the corresponding device appears in Windows, and a provider can be present without accelerating every operation in a model.
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Keep these variables together when investigating an issue:
- Windows edition, release, and full build number.
- Application architecture (x64 or ARM64) and the Windows ML API and package versions used to build it.
- The provider selected by the application and whether it is included with Windows ML or acquired on demand.
- The exact GPU or NPU model and its installed driver version.
- Whether the model’s operators are supported by that provider.
- Whether the target is low latency for one request or high throughput for many requests.
- Whether the app is self-contained or depends on a shared framework runtime.
Recording all seven items prevents a common mistake: changing a driver or model while the actual mismatch is an unsupported Windows build or package version.
Inventory the Windows device before changing the app
Check the Windows release and architecture
Verify the release and build on the machine where the failure occurs, not only on the development PC. Compare that build with the requirements shown in Microsoft’s Windows ML execution-provider matrix. Also verify that the app’s architecture matches the binaries and provider packages available for the device. An x64 build and an ARM64 build can have different provider availability even when they run the same model.
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Microsoft’s FAQ gives a quick hardware check: press Ctrl+Shift+Esc to open Task Manager, select Performance, and look for the GPU and NPU entries. Record the model names shown there. If no NPU entry appears, do not infer that an NPU provider is merely disabled; first confirm that the hardware and Windows build expose one.
The Task Manager view identifies hardware, but it does not prove that a Windows ML provider can use it. Continue with the provider matrix and driver check.
Check the provider’s current requirements
The provider matrix gives separate requirements and support notes for examples such as AMD, NVIDIA, Qualcomm, and WebGPU. Do not carry an old minimum driver number or hardware-generation rule from one provider to another. Requirements change by provider, operating-system build, API/package version, device generation, and driver.
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WebGPU deserves extra caution: Microsoft’s provider page labels it experimental and gives it a separate minimum API package version and GPU requirements. It is not a stable, general-purpose fallback for a missing provider. Use the matrix entry that applies to the app’s current Windows ML version and target hardware.
Understand which providers are built in and which are downloaded
Windows ML includes CPU and DirectML providers. Other vendor-specific providers listed by Microsoft are downloaded on demand. That distinction changes the diagnosis:
| Provider situation | What it means | What to check |
|---|---|---|
| CPU provider | Included with Windows ML and generally available as the baseline execution path. | Confirm the app can create a CPU session and that the model loads with the API/package version being used. |
| DirectML provider | Included provider for supported Windows GPU execution. | Check Windows support, GPU driver, device compatibility, and model-operator support; do not assume every GPU operation will run on the device. |
| Hardware-optimized vendor provider | Acquired on demand rather than assumed to be present in the base installation. | Verify that dynamic acquisition is supported on the OS build, that the package is available for the architecture, and that the device and driver satisfy the current matrix entry. |
| WebGPU provider | Experimental according to Microsoft’s provider documentation. | Use only when its stated API-package and GPU requirements are met, and treat behavior as experimental rather than as a fallback guarantee. |
Before shipping a downloaded provider, read its current support and licensing terms in the provider documentation. Availability and licensing are separate decisions: a provider may be technically installable but unsuitable for your distribution or update policy.
Choose self-contained or framework-dependent deployment deliberately
A runtime mismatch often looks like a provider failure because the app starts with a different Windows ML runtime than the developer tested. Microsoft’s deployment guidance describes two modes.
| Deployment mode | Strengths | Costs and failure modes | Best fit |
|---|---|---|---|
| Self-contained | Bundles the Windows ML runtime binaries with the app, giving the developer tighter version control and making the app less dependent on what is installed globally. | Increases package size. Microsoft estimates about 41 MB for the listed runtime binaries; that is a page estimate, not a guaranteed current download size. The app must be rebuilt and redistributed when you intentionally change the bundled runtime. | Controlled installations, reproducible testing, and products that cannot assume a shared runtime is installed. |
| Framework-dependent | Uses a shared system runtime and can receive updates through Windows App SDK servicing. | Fails or behaves differently when the required runtime is absent, too old, or different from the version used during testing. The shared runtime’s servicing schedule affects the app. | Managed fleets where the required runtime is centrally installed and update behavior is acceptable. |
When comparing the two, record the exact runtime version, architecture, package version, installation state, and update source on the failing machine. Do not solve a framework-dependent installation failure by silently switching deployment modes without deciding who will install and service the runtime.
Fix a provider that does not appear or does not accelerate
- Confirm the requested provider is supported by the app’s Windows ML/API version. A provider listed in current documentation may require a newer package than the one referenced by an older app.
- Verify the OS build. For the downloadable provider catalog, check whether the machine is running Windows 11 version 24H2, build 26100, or later, then read the provider’s additional requirements.
- Verify architecture. Ensure the app and provider package target the same supported architecture, x64 or ARM64.
- Identify the hardware in Task Manager. Use the Performance page to record the GPU or NPU model rather than relying on a marketing family name.
- Check the driver using the guidance for that device and provider. Install or update the appropriate GPU/NPU driver, then reboot if the vendor or Windows requires it. Do not use a generic driver minimum from another provider as proof of compatibility.
- Check whether acquisition is required. CPU and DirectML are included; a vendor provider may need to be downloaded on demand. A missing downloaded package is not the same condition as a broken built-in CPU provider.
- Test a CPU session separately. If the model loads and runs on CPU, the model and basic runtime path are working; focus the next investigation on the accelerator provider, driver, or unsupported operators.
- Record the provider actually selected. An app can request one provider while falling back to another, depending on its configuration and API version. Capture the app’s provider-selection output or diagnostic information rather than inferring selection from GPU utilization alone.
If the provider still cannot be created after these checks, preserve the exact Windows build, package versions, hardware identifiers, driver version, and provider documentation entry used for the comparison. Those details are needed for a useful support case.
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Fix unexpectedly slow inference
Define the performance target first
Low latency and high throughput are different objectives. Latency is the time for one request, including input preparation and output handling. Throughput is the number of completed requests over time. A change that lowers single-request latency can reduce throughput, and a configuration that increases throughput can make an individual request wait longer.
Microsoft’s older Windows ML performance and memory guidance recommends experiments and measurements because results vary by hardware, model, and scenario. There is no official universal percentage uplift that can be applied to every Windows ML workload.
Use a repeatable measurement
- Warm up the session before timing production requests so model loading and one-time initialization are not mixed into steady-state inference.
- Measure model loading separately from inference.
- Measure input preparation, the session call, and output conversion separately when possible.
- Report a distribution of latency, such as median and tail observations, rather than a single favorable run.
- Measure throughput with the intended request concurrency and batch shape.
- Repeat the same test on the target Windows build, driver, provider, model, and power configuration.
- Compare CPU and accelerator sessions using the same inputs and output requirements.
Record memory use alongside timing. A faster run that causes paging or forces frequent allocation may be worse for a sustained service.
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Account for session synchronization
GPU session calls synchronize. Starting multiple calls on one GPU session should therefore not be assumed to create parallel throughput; synchronization can serialize work or add coordination overhead. Test the concurrency pattern you intend to ship instead of equating more tasks with more GPU throughput.
CPU sessions can support concurrency, but the application must manage its own state, buffers, and bindings carefully. Shared mutable input or output buffers can introduce contention or incorrect results even when the provider itself supports concurrent execution. Use separate state or buffers where the API and workload require it, and measure the resulting memory and throughput trade-off.
Reduce excessive memory use
The performance article documents an important LearningModel API behavior: each LearningModel and LearningModelSession instance holds a copy of the model in memory. Dispose of those objects when they are no longer needed so their memory can be released.
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Multiple sessions can be useful for concurrency, but every additional session increases the model-memory cost. Decide whether the throughput gain justifies that cost using the actual model and request pattern. A single shared session may reduce memory while a small pool of sessions may improve CPU concurrency; neither choice should be generalized without measurement.
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This guidance is specific to the LearningModel and LearningModelSession APIs described by that older article. Do not silently apply its object-lifetime details to every newer Windows ML API. First identify the API surface in the app, then follow that API’s lifetime and disposal rules.
Memory troubleshooting checklist
- Count model and session instances created during startup and request processing.
- Check whether sessions are recreated for every request instead of reused where safe.
- Dispose sessions and model objects when a workload or tenant is retired.
- Track input, output, and binding-buffer lifetimes; retained buffers can hide as model memory.
- Measure memory with the same concurrency used in production.
- Compare a single-session design with a measured session pool rather than assuming the pool is faster.
Resolve runtime and package mismatches
Symptoms of a mismatch include a provider missing only on some machines, an app that works from a development environment but not after packaging, or a model that loads under one package version and fails under another. Verify the complete version chain: Windows build, Windows ML API/package, app architecture, deployment mode, installed or bundled runtime, provider package, and driver.
For a self-contained app, inspect the shipped runtime and confirm that the installer did not omit architecture-specific files. For a framework-dependent app, verify that the required shared runtime is installed before launch and that servicing has not replaced it with an incompatible version. Microsoft’s deployment page explains that framework-dependent apps can receive updates through Windows App SDK servicing; that benefit also means the shared runtime is part of your compatibility surface.
When reproducing a mismatch, test a clean machine or a clean application environment. A developer workstation can hide missing-runtime problems because it has SDKs, cached provider packages, or newer drivers that the target machine lacks.
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DirectML remains supported, but Microsoft’s DirectML guidance states: “DirectML is in maintenance mode.” The same guidance says new Windows ONNX Runtime feature development is directed toward Windows ML for Windows-based deployments.
This matters most when maintaining a legacy application that calls a DirectML API directly. Existing DirectML code can remain a valid supported path, but you should not expect it to receive the same pace of new feature development as the Windows ML path. For a new Windows ONNX Runtime deployment, evaluate the current Windows ML provider guidance, package requirements, and measured workload results before committing to a legacy integration.
Capture ETW logs when ordinary checks do not explain the failure
Microsoft’s Windows ML logging guide describes an Event Tracing for Windows (ETW) and Windows Performance Recorder (WPR) workflow. Traces and rundown logs can expose model-loading and inference errors, the provider selected, and hardware or driver compatibility problems that are not visible in a normal exception message.
The workflow requires administrator privileges to install Windows Performance Toolkit and to start and stop tracing. Plan the capture around a minimal reproducible case:
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errors- Reduce the app to the smallest model and input sequence that still reproduces the problem, if that reduction does not change the failure.
- Record the Windows build, architecture, API/package versions, provider request, hardware model, and driver version before tracing.
- Install or enable the Windows Performance Toolkit with administrator rights as described in Microsoft’s logging guide.
- Start the WPR/ETW capture, launch the app, and reproduce the provider-selection, model-load, or inference failure once or a small number of times.
- Stop the trace and collect the associated rundown information.
- Inspect provider selection, model load, inference errors, and hardware/driver compatibility events.
- Share logs only through the appropriate Microsoft support route and review them for sensitive model, input, file-path, or application data first.
A trace is most useful when it contains one clear reproduction and a precise timeline. A long capture containing unrelated workloads can make provider and timing events harder to interpret.
Compare viable providers or deployment choices with the same criteria
Do not select an execution provider because its name implies a faster accelerator. Compare the choices on the dimensions below and fill them with measurements from the target workload.
| Comparison axis | Questions to answer |
|---|---|
| Windows and API availability | Does the provider support this Windows release/build and the app’s API/package version? |
| Device and driver | Is the exact GPU/NPU model listed as compatible, and is the required driver installed? |
| Model and operator support | Can the provider execute the model’s operators, or will unsupported work fall back or fail? |
| Latency | What are warm and cold timings, and what is the tail latency for the real input shape? |
| Throughput and concurrency | How does the provider behave at the intended number of simultaneous requests? |
| Memory | How much memory does one session use, and what is the cost of additional sessions or buffers? |
| Package and update model | Is the provider included, downloaded on demand, self-contained, or dependent on a shared runtime? |
| Licensing | Do the provider’s current licensing terms permit the planned distribution? |
These criteria prevent an apparent speed win from masking a package-size, update, memory, or licensing problem.
An end-to-end troubleshooting sequence
Use this order when a Windows ML app fails to load, selects the wrong provider, or performs below its target:
- Freeze the reproduction. Note the model, input shape, expected result, Windows build, architecture, API/package versions, provider request, hardware, driver, and deployment mode.
- Check hardware visibility. Open Task Manager with Ctrl+Shift+Esc, choose Performance, and record GPU and NPU entries.
- Match the provider matrix. Read the current provider entry for the exact device and driver. For downloadable providers, verify the Windows 11 24H2 build 26100-or-later catalog requirement and any additional conditions.
- Separate built-in from downloaded paths. Test the included CPU provider and, where supported, DirectML. Confirm that any vendor provider required by the app is actually acquired.
- Verify deployment. Confirm whether the app is self-contained or framework-dependent, then verify the bundled or shared runtime version and architecture.
- Measure the stated goal. Run repeatable latency and throughput tests, including warm-up, concurrency, and memory observations.
- Fix lifecycle issues. Reuse or dispose model/session objects appropriately, and control buffers and bindings for concurrent CPU work.
- Review legacy assumptions. If the app depends on DirectML-specific development, account for its maintenance-mode status and evaluate the current Windows ML path for new feature needs.
- Trace the unresolved case. Capture ETW/WPR logs with administrator privileges and a minimal reproduction, then inspect provider, model-load, inference, and hardware/driver events.
This sequence keeps expensive changes, such as replacing hardware, at the end. The documented evidence supports compatibility checks, provider and driver selection, deployment configuration, measurement, and logging; it does not establish that a hardware upgrade or repair utility is necessary for a generic Windows ML problem.
Quick Recap
Practical conclusions for developers and IT teams
- A visible GPU or NPU is not proof that the desired Windows ML provider is available.
- The Windows build, architecture, API/package version, device generation, and driver must be evaluated together.
- CPU and DirectML are included providers; other listed providers may require on-demand acquisition and separate licensing review.
- Self-contained deployment improves version control at the cost of package size, while framework-dependent deployment reduces bundling but relies on a correctly installed shared runtime.
- Latency, throughput, synchronization, session count, and memory must be measured on the target workload; there is no universal Windows ML performance percentage.
- LearningModel and LearningModelSession memory guidance is API-specific, and additional sessions trade memory for possible concurrency.
- DirectML is supported but in maintenance mode, with new Windows ONNX Runtime feature work directed toward Windows ML.
- ETW/WPR traces are the escalation path when provider selection, model loading, or driver behavior cannot be explained from ordinary logs.
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