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An AI PC having an NPU does not prove that a particular app will use it. To verify support, check the app’s own local-mode and model requirements, confirm that Windows recognizes the NPU and meets the required build, run the exact AI feature, and watch Task Manager > Performance > NPU while it works. NPU activity is useful evidence of dispatch; an app that works without NPU activity may be using the CPU, GPU, or a cloud service instead.
The procedure below separates three questions that are often confused: whether the AI task runs on the PC at all, whether it runs on the NPU specifically, and whether it stays offline. A “Copilot+ PC,” an “AI PC” label, or a 40-TOPS specification answers none of those questions by itself.
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What you are actually trying to prove
“Runs locally” means the model inference happens on your computer. “Runs on the NPU” means the work is dispatched to the neural-processing unit rather than the CPU or GPU. Those are different outcomes. Windows ML supports NPU, GPU and CPU execution, so a successful offline task can still be CPU- or GPU-based. Conversely, an app can expose an NPU option yet send some operations to another processor or to a remote service.
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Microsoft describes Windows ML as “the unified and high-performance local AI inferencing framework for Windows, powered by ONNX Runtime.” Its hardware-optimized NPU execution providers have their own operating-system and driver requirements. Application developers can also impose model, runtime, architecture and memory requirements that are not visible in the PC’s marketing label.
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- Local execution: the input and inference remain on the PC. CPU or GPU fallback is still local.
- NPU execution: Task Manager or the application’s diagnostics show that the NPU handled work during the test.
- Cloud execution: the app sends data to a remote service, whether or not the PC also contains an NPU.
Test the exact feature and model you intend to use. A camera effect, transcription tool and text-generation model may use different APIs and hardware paths in the same application.
Step-by-step: verify NPU support for one app
1. Identify the exact AI feature and mode
Start with the app vendor’s current requirements page, not the computer’s product page. Look for separate language about “on-device,” “offline,” “local,” “accelerated,” or “NPU” operation. Record:
- the application version and supported Windows edition;
- whether the feature is local, cloud-assisted or cloud-only;
- the model name, download size, file format and quantization, if specified;
- required CPU architecture (for example, x64 or Arm64), system memory and graphics memory;
- the named execution provider, runtime or NPU vendor, if one is listed; and
- minimum driver and Windows build numbers.
If the requirement only says “AI PC” or “40-TOPS NPU” without identifying a supported runtime, model or device family, mark compatibility as unconfirmed. “AI PC” is a capability category, not an application compatibility certificate. Microsoft’s Windows AI API guidance shows why: some APIs require a Copilot+ PC with an NPU, while others can use a GPU or CPU on non-Copilot+ systems.
2. Confirm that Windows detects an NPU
Use the built-in Studio Effects check as a quick hardware-and-driver signal:
- Open Settings > Bluetooth & devices > Cameras.
- Select the front-facing camera.
- Look for Windows Studio Effects. The controls may also appear in Quick Settings.
Microsoft lists Windows 11 version 22H2 or newer (build 22623.885 or later), a supported NPU and an OEM-installed Studio Effects driver for this feature. The Studio Effects documentation explicitly says the hardware manufacturer must opt in and install the driver. Therefore, visible Studio Effects controls are evidence that this particular Windows feature has a supported configuration; they do not certify a third-party app, a different NPU vendor or every AI model.
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If Studio Effects is absent, check Windows Update and the computer maker’s driver package, then verify the model’s specifications in Settings > System > About or Device Manager. Absence can indicate an unsupported camera, missing OEM driver, an older build or an NPU that the feature does not use; it is not by itself proof that no NPU exists.
3. Compare the exact hardware and Windows build with the app’s requirements
Write down the NPU vendor and model, processor architecture, installed RAM and Windows build. The “Copilot+ PC” category is defined by Microsoft around an NPU capable of more than 40 trillion operations per second (TOPS), but TOPS is only one compatibility axis. A model can require a particular vendor, instruction set, provider, driver or memory capacity even when both machines exceed 40 TOPS.
For NPU execution through Windows ML, Microsoft’s current documentation requires Windows 11 version 24H2, build 26100 or later, for hardware-optimized NPU execution providers. CPU and DirectML GPU paths have different support ranges. Check the build in Settings > System > About or by running winver, then compare it with the application’s stated minimum rather than assuming that an update will make an unsupported model work. The Windows ML overview was updated on September 8, 2026 and is the relevant reference for those provider requirements.
4. Select the local model and run a sustained task
Install or download the model the application identifies as local. Some programs default to a hosted model, even on an AI PC, and some offer a separate “on-device” switch. Disconnecting the network can be a useful diagnostic only after the app documents an offline mode; otherwise it may simply fail or silently switch features.
- Open the app’s AI settings and choose its local or on-device mode.
- Download the required model and allow any conversion or optimization step to finish.
- Use a task that lasts long enough to observe, such as processing a batch of images, transcribing a recording or generating several responses.
- Note the model name, response or processing time, and any status message that says “local,” “accelerated,” “NPU,” “CPU,” “GPU” or “cloud.”
Model packaging matters. Microsoft notes that many NPUs favor lower-bit integer formats such as INT8. A model may therefore need conversion, quantization or an NPU-specific execution provider before it can be dispatched. A model that runs in a generic format may be perfectly valid on the CPU while unsupported on the NPU.
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5. Watch NPU activity in Task Manager
Open Task Manager > Performance > NPU before starting the task. Microsoft’s Copilot+ developer guide documents this view for NPU resource use and device details: “For devices with NPUs, the Task Manager can now be used to view NPU resource usage.”
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchKeep the graph visible while the local feature is active. A sustained rise that coincides with the task is evidence that the tested workload used the NPU. If the task is brief, the graph can return to idle before you see it, so repeat with a larger batch or longer input. Also watch the CPU and GPU graphs; simultaneous activity is normal when preprocessing, postprocessing or unsupported operators are handled elsewhere.
Task Manager reports system activity, not an application’s internal intent. If the app offers logs, an accelerator selector, a diagnostic panel or a model report, save that information as stronger app-specific confirmation. A screen recording of the task and the NPU graph can help when reporting a driver or compatibility problem.
6. Classify the result without overclaiming
| Observed result | What it establishes | What remains unknown | Next action |
|---|---|---|---|
| Feature completes and NPU activity rises during the task | The tested feature and model dispatched work to the NPU in that session. | Other features or models in the same app may use different paths. | Record the app version, model, driver and Windows build for repeatability. |
| Feature completes but NPU stays idle | The task ran successfully on the PC, or the app may have obtained a result remotely. | Whether CPU, GPU or cloud execution was used. | Check the app’s mode/status, inspect logs, repeat with a sustained task and test offline only if documented. |
| Feature fails before producing a result | The tested configuration is not working. | Whether the cause is the NPU, model, runtime, driver, OS, architecture or memory. | Compare each requirement and try the documented CPU/GPU fallback. |
| NPU graph is missing | Windows is not exposing an NPU performance view on this installation. | Whether an NPU is physically present but lacks a driver or supported Windows integration. | Install the OEM NPU/chipset driver, update Windows and recheck Device Manager and the app documentation. |
How to interpret TOPS, Windows versions and model formats
| Fact | Precise meaning | What it does not guarantee |
|---|---|---|
| More than 40 TOPS | Microsoft’s current Copilot+ PC capability threshold for the NPU category. | Support for every model, provider, operation or third-party application. |
| Windows 11 24H2, build 26100 or later | Minimum stated by Windows ML for hardware-optimized NPU execution providers and specific GPU hardware. | That an app packaged for another runtime will install or dispatch correctly. |
| Windows 11 22H2, build 22623.885 or later | Minimum listed for Windows Studio Effects when paired with a supported NPU and OEM-installed driver. | General NPU compatibility with unrelated applications. |
| INT8 or other low-bit model | A format often favored by NPU hardware and providers; conversion may be required. | That every INT8 model is compatible with every NPU or produces identical results. |
Neither Microsoft’s requirements pages nor the developer guide provide a universal real-world compatibility percentage or a performance guarantee. Treat TOPS as a throughput specification measured within a category, not as a promise that an application will be faster than a CPU or GPU.
Why a compatible-looking app can still avoid the NPU
The app chose another execution provider
Windows AI APIs can expose fallback paths. A provider may select the GPU or CPU when an NPU operator is unavailable, when the driver is missing, or when the workload is too small to justify a transfer. The result can be local and correct while the NPU remains idle.
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The model contains unsupported operations
NPU providers support a defined set of operators and data types. One unsupported layer can cause the whole graph, or just that layer, to run elsewhere. Quantization, tensor shapes and memory use can also determine whether the model fits. Check the app’s model list rather than assuming that a model format accepted by the CPU path is accepted by the NPU path.
The runtime or driver is stale
Older guides may name “Copilot Runtime APIs,” “Windows AI APIs,” Windows ML or DirectML interchangeably. Microsoft’s current comparison page says “Copilot Runtime APIs” was the old name for Windows AI APIs and that DirectML is no longer being actively developed, remaining in sustained engineering: Choose your Windows AI solution. Follow the app’s current documentation and update the OEM NPU driver instead of copying setup commands from an undated tutorial.
The feature is cloud-assisted
An app can perform local preprocessing, send a prompt or image to a server, and display a result while the NPU graph remains idle. Look for a local/offline indicator, network requirement, account requirement and privacy statement. “Offline,” “local,” “AI PC” and “Copilot+” are not interchangeable labels.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Troubleshooting by symptom
| Symptom | Checks in order |
|---|---|
| Studio Effects is missing | Confirm Windows 11 22H2/build 22623.885 or later; install the computer maker’s camera, chipset and NPU drivers; test a supported front-facing camera; then recheck Settings and Quick Settings. |
| The app says no accelerator is available | Verify the required NPU vendor/model, architecture, Windows build and runtime. Confirm that the model is downloaded in the required format and that the app is not limited to a cloud account or region. |
| The app works but NPU usage is zero | Confirm the selected mode is local, run a longer task, watch CPU/GPU graphs, inspect logs and repeat after updating the provider and driver. Do not label the result “NPU-accelerated” without evidence. |
| The app crashes while loading the model | Check available system and graphics memory, model quantization, supported operators and architecture. Try the vendor’s smaller or explicitly NPU-optimized model before changing hardware. |
| The feature works only online | Read the vendor’s mode description and network requirements. If the service requires a remote account or endpoint, it is not a fully local test even if the PC has an NPU. |
| NPU activity appears only briefly | Repeat with a batch or longer recording. Capture the graph during model loading and inference; short bursts can be missed when polling the view manually. |
A comparison checklist for apps or PCs
When deciding between two applications or two NPU-equipped computers, compare the following fields side by side rather than ranking them by TOPS alone.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute| Comparison field | Question to answer |
|---|---|
| Windows and architecture | Does the app require a specific Windows 11 release, build, x64 or Arm64? |
| NPU identity | Is a particular vendor or model named, and is a minimum TOPS figure explicitly stated? |
| Runtime and driver | Which execution provider is supported, and which OEM driver version is required? |
| Model | Which model files, formats, quantization levels and memory limits are supported? |
| Fallback behavior | If NPU dispatch fails, does the app remain local on CPU/GPU or switch to a cloud service? |
| Verification | Does the app expose logs or diagnostics that identify the device, and can Task Manager show activity during a sustained task? |
This checklist is also a purchase safeguard. You do not need an external accelerator, webcam or paid compatibility checker to perform the test: Windows Settings, Task Manager and the application’s published requirements are sufficient. Consider an NPU-equipped PC upgrade only when the computer you have fails an app’s explicit requirement or cannot receive the needed Windows and driver support.
Where Phi Silica and Microsoft’s current names fit
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 one supported Microsoft workload; it does not imply that unrelated third-party models or applications will use the same NPU. Microsoft’s Windows Copilot+ AI components page describes that model and its Windows AI API access.
Microsoft’s current umbrella term is Microsoft Foundry on Windows, covering Windows AI APIs, Foundry Local and Windows ML. The naming comparison at Microsoft Learn explains the transition from the older “Copilot Runtime APIs” label. When following a tutorial, check its publication and update date; an old API name can indicate an obsolete setup path even when the underlying concept is similar.
Minimal evidence to keep for a support report
If you need the app developer or computer maker to investigate, collect a reproducible record rather than only saying that the PC is an AI PC:
- app name and exact version;
- feature, model name, model format and local/cloud setting;
- Windows edition, version and build;
- NPU vendor/model and driver version;
- available system memory and processor architecture;
- the task steps and whether the network was required; and
- a timestamped screenshot or recording of Task Manager’s NPU, CPU and GPU graphs during the task, plus any app diagnostic log.
That evidence distinguishes a missing NPU driver from an unsupported model and a cloud-only feature, which require entirely different fixes.
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