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An “AI PC” label or a 40-TOPS NPU does not prove that an app will run its AI feature on that NPU. To verify it, check the app’s exact local-AI requirements, confirm that Windows recognizes the NPU and meets the required build, run the feature with its intended model, and watch Task Manager during the task. A successful result with no NPU activity may still be local CPU/GPU execution—or cloud processing—so you must identify the execution path rather than infer it from the hardware specification.
What you are actually trying to prove
There are three separate questions, and answering one does not answer the others:
- Can the app run the feature locally? The model and runtime may execute on the PC’s CPU, GPU or NPU.
- Can the app use this PC’s NPU? That depends on the app, model format, execution provider, driver, operating-system build, architecture and available memory.
- Did this particular run use the NPU? Live telemetry or app diagnostics are needed; an “AI PC” badge is not evidence of dispatch.
Windows ML supports NPU, GPU and CPU inference, so an app can work offline without using the NPU. Conversely, an app can advertise an NPU requirement yet fall back to another device when a provider, driver or model is unavailable. Microsoft describes Windows ML as “the unified and high-performance local AI inferencing framework for Windows, powered by ONNX Runtime” (Windows ML overview). Treat “local,” “offline,” “Copilot+” and “NPU-accelerated” as different claims.
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Check the app before checking the PC
Find the requirement for the exact feature
Open the developer’s current system-requirements page and identify the specific AI function you intend to use. A video editor’s transcription, a meeting app’s background effects and a local chatbot can require different providers and models. Look for separate statements about:
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- Windows edition, version and build;
- x64 or Arm64 architecture;
- supported NPU vendor or model and any stated minimum TOPS;
- execution provider or runtime, such as Windows ML or another ONNX Runtime provider;
- model file, format, quantization and download requirements;
- minimum system memory and storage; and
- whether the feature is on-device, cloud-assisted or cloud-only.
Microsoft’s guidance for Windows AI APIs is per API: most require a Copilot+ PC with an NPU, while some can also use a GPU or CPU on non-Copilot+ systems. Therefore, a vendor statement that an app is “AI PC ready” is insufficient unless it names the supported hardware and software path. The Windows AI APIs getting-started guide is a useful example of why requirements must be checked feature by feature.
Separate cloud and local modes
Read the app’s privacy or processing-status text. “Works without an internet connection” is stronger evidence of local execution than a generic AI label, but it still does not identify the processor. If the app uploads audio, images, prompts or documents, the task is not fully local even when a local pre-processing step exists. Record the exact mode and model you will test; changing either can change the hardware used.
Confirm that Windows recognizes an NPU
Use Windows Studio Effects as a hardware-and-driver signal
- Open Settings > Bluetooth & devices > Cameras.
- Select the front-facing camera.
- Look for Windows Studio Effects. The controls may also be available from Quick Settings.
Microsoft lists Windows 11 version 22H2 or later (build 22623.885 or newer), a supported NPU and an OEM-installed Studio Effects driver for this feature. The Windows Studio Effects documentation states that the manufacturer must opt in and install the driver. Seeing the controls is a useful signal that Windows has a supported NPU configuration; it is not certification that a third-party app can use the NPU.
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Open Settings > System > About to note the processor and architecture. Then open Settings > Windows Update > Update history or type winver in the Start menu to record the Windows version and build. Compare those exact values with the app’s requirements. If the NPU is absent from the app’s documentation, compatibility remains unconfirmed even when the PC is sold as a Copilot+ PC.
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Microsoft defines Copilot+ PCs around an NPU capable of more than 40 trillion operations per second (TOPS). That figure is a category threshold, not a universal application-compatibility guarantee. Vendor, runtime, driver, supported operations, model precision and memory can all be limiting factors.
Understand the numbers and version requirements
| Value | What it establishes | What it does not establish |
|---|---|---|
| More than 40 TOPS | Microsoft’s current Copilot+ PC NPU capability category. | That every app, model or execution provider supports the NPU, or that the app will achieve a particular speed. |
| Windows 11 24H2, build 26100 or later | Minimum stated by current Windows ML documentation for hardware-optimized NPU execution providers (and specific GPU hardware). | That an app built for Windows ML has the correct provider, model or driver on your machine. |
| Windows 11 22H2, build 22623.885 or later | Listed minimum for Windows Studio Effects, together with a supported NPU and manufacturer-installed driver. | Compatibility with unrelated AI applications. |
Windows ML documentation updated September 8, 2026, distinguishes hardware-optimized providers from CPU and DirectML GPU support. A PC below 24H2 may still run local CPU or supported GPU inference, but it may not satisfy an NPU provider’s requirement. DirectML is now in sustained engineering rather than active development; avoid assuming that an older setup guide reflects the current Windows AI stack.
Run a controlled local test
Prepare the app and model
- Install the latest app version allowed by your organization or license.
- Install the app’s required runtime or provider and update the NPU, chipset and graphics drivers from the PC manufacturer or component vendor.
- Download or select the exact model specified for local use. If the app offers “small,” “fast,” “quantized” or “cloud” variants, note which one is selected.
- Disconnect the network only if the app’s documentation says the feature supports offline operation and you want to verify that claim. Otherwise, leave the network state unchanged and inspect whether the app reports cloud processing.
- Use a task long enough to observe—such as a multi-minute transcription or a batch of images—rather than a one-second preview.
Many NPUs favor lower-bit integer formats such as INT8. A model may therefore need conversion, quantization or an optimized package before an NPU provider can accept it. Microsoft’s Copilot+ developer guidance covers model formats and NPU diagnostics in its Copilot+ PCs developer guide. A model that loads successfully can still be running on CPU or GPU.
Watch Task Manager while the task runs
- Start the app’s local AI operation.
- Press Ctrl+Shift+Esc to open Task Manager.
- Choose Performance and select NPU, if that device appears.
- Observe utilization, graphs and device details during the active inference—not before it starts or after it has finished.
Microsoft says Task Manager can display NPU resource usage on devices with NPUs. Sustained activity that rises and falls with the tested operation is evidence that the NPU handled work in that session. The app’s own logs, diagnostics or a provider-selection screen are stronger confirmation because Task Manager alone does not identify which process requested every brief burst.
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Repeat to avoid a false negative
A short task can finish between graph refreshes. Repeat it with a larger input, keep Task Manager visible, and watch for several seconds before and after the model begins. Close unrelated AI workloads that could generate NPU activity. If the NPU graph remains flat, record CPU and GPU utilization as well, then inspect the app’s processing-status indicator and logs.
Interpret every possible result
| Observed result | Most defensible conclusion | Next action |
|---|---|---|
| Feature works and NPU activity tracks the task. | The tested feature and model used the NPU in that session. | Save the app version, model, driver and Windows build because an update can change provider selection. |
| Feature works but NPU activity is absent. | The app may be using CPU, GPU or a cloud service; local success alone does not identify the device. | Check the app’s mode, network status, logs and provider setting, then repeat with a sustained task. |
| Feature fails before inference. | A requirement is unmet, but the NPU is not automatically the cause. | Check OS build, architecture, driver, runtime, model format, quantization and memory. |
| NPU activity appears while the feature fails. | The NPU is being used for some work, but the app or model still has another fault. | Read the app error and verify model operations, memory and provider compatibility. |
| No NPU entry appears in Task Manager. | Windows may lack the required driver, build or exposed device, or the hardware may not provide an NPU. | Confirm Device Manager and OEM driver updates, then compare against the app’s requirements. |
Do not treat a flat NPU graph as proof of cloud use: the task may have been too brief, telemetry may be sampled too slowly, or the app may have selected CPU/GPU execution. Do not treat a working result as proof of local processing: cloud fallback can be silent unless the app exposes status or you test offline.
Troubleshoot an app that will not use the NPU
1. Verify the operating-system build
Run winver and compare the build with the app and provider requirements. For current Windows ML hardware-optimized NPU providers, Microsoft lists Windows 11 24H2, build 26100 or newer. Update through Settings > Windows Update only when the app and your organization permit it; an update can also change driver behavior.
2. Verify architecture and runtime
Confirm whether the app is native x64, native Arm64 or an emulated build. A provider packaged only for one architecture may not load in another. Check that the required Windows ML, ONNX Runtime or vendor runtime is installed and that the app is not forcing a CPU-only compatibility mode.
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3. Update the NPU and platform drivers
Install the OEM’s current NPU, chipset and graphics packages, reboot, and check whether Studio Effects and the Task Manager NPU page appear afterward. A generic Windows driver may expose basic hardware while lacking the provider integration required by the app.
4. Check model operations, precision and memory
Read the model’s supported operations and precision. NPU providers often require optimized low-bit models; unsupported operators can cause a complete fallback or split execution between devices. Confirm that system memory is sufficient for the model and that no other process is consuming the NPU or most available memory.
5. Disable accidental cloud fallback for diagnosis
If the app offers a “use cloud,” “online enhancement” or “automatic” switch, select its explicit local mode. Test with the network disabled only after saving work and confirming the app supports offline operation. A failure offline demonstrates that the prior successful run depended on a remote service; it does not by itself prove the NPU was unsupported.
6. Read diagnostics instead of guessing from utilization
Look for a provider name, device identifier, inference-backend log or performance panel. Capture the error, app version, model version, Windows build, driver versions and whether the network was available. This information lets the vendor distinguish an unsupported operator from a missing driver or a cloud-policy restriction.
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Use Microsoft’s current terminology carefully
Microsoft now uses Microsoft Foundry on Windows as an umbrella covering Windows AI APIs, Foundry Local and Windows ML. The comparison page explains that “Copilot Runtime APIs” was the former name for Windows AI APIs and that DirectML is no longer actively developed. See Choose your Windows AI solution, updated August 6, 2026, before applying an older tutorial.
Phi Silica is a Microsoft small language model optimized for local NPU execution on supported Windows 11 Copilot+ PCs and exposed through Windows AI APIs. Its availability demonstrates support for that Microsoft model and API combination; it does not establish that an unrelated third-party app or model can use the same NPU. Microsoft documents the component and API relationship in Windows Copilot+ AI components.
Compare an app or PC using the right checklist
When choosing between applications or deciding whether a new machine is necessary, record these six fields rather than comparing TOPS alone:
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| Comparison field | Question to answer |
|---|---|
| Operating system and architecture | Does the feature require a particular Windows release, build, x64 or Arm64 package? |
| NPU identity and capability | Is a specific vendor/model or minimum TOPS explicitly named? |
| Runtime and driver | Which execution provider is supported, and which OEM or vendor driver version is required? |
| Model package | Is the model available locally, in the required format and quantization, with enough memory? |
| Fallback behavior | Does CPU/GPU fallback remain local, or does “automatic” mode send work to a cloud service? |
| Proof of dispatch | Does the app provide logs or a device indicator, and can Task Manager show activity during a sustained task? |
If a requirement is not stated—especially the NPU model, provider or local/cloud behavior—mark it unconfirmed rather than assuming that a higher TOPS number resolves the uncertainty. The compatibility check itself requires no paid checker, external accelerator or special webcam: Windows Settings, Task Manager and the app’s own documentation are sufficient for an initial investigation.
A repeatable verification worksheet
- Feature: write the exact AI function, not just the app name.
- Mode: record local, cloud, hybrid or unknown.
- Model: note the model name, version, format and quantization.
- PC: record NPU vendor/model, architecture, memory and Windows build.
- Software: record app, runtime and NPU/chipset driver versions.
- Test: run a sustained task with the intended model and observe Task Manager’s NPU, CPU and GPU graphs.
- Evidence: save the app’s provider log or diagnostic output, if available.
- Classification: label the result “NPU confirmed,” “local but device unknown,” “cloud or hybrid,” or “not compatible under current requirements.”
This classification prevents an unsupported leap from “the task completed” to “the NPU ran it.” It also gives you a useful baseline when an app, model, driver or Windows build changes.
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