Windows Task Manager now provides deeper visibility into AI workloads running on a device. Previously, the Processes tab didn't show per-process activity for the neural processing unit (NPU) or graphics processing unit (GPU) neural engine. Now, on some newer devices, this data appears alongside familiar central processing unit (CPU) and standard GPU activity on the Processes tab, while the Performance tab shows overall utilization. Whether you're an IT pro, developer, or power user, this data can help as follows:
- For IT professionals: Troubleshoot and report on performance.
- For developers: Validate whether AI models are landing on the intended NPU or GPU neural engine instead of CPU or standard GPU compute.
- For power users: Understand and improve performance and battery life.
Let's look at these use cases more closely.
Note: In this post, we distinguish standard GPU compute from the newest GPU equipped with dedicated AI matrix engines, called the GPU neural engine. The NPU and GPU neural engine are new pieces of hardware that perform AI math more efficiently than the standard GPU.
Enterprise helpdesk support
As the demand for AI based tooling increases, it's important to have a built-in way to determine what processing resources an app is using.
If you're an IT helpdesk professional, you support hundreds to thousands of corporate devices. When a user reports a problem with an AI-enabled app, you can use the improved Windows Task Manager to troubleshoot.
On supported devices, the Processes and Performance tabs show exactly how background tasks are interacting with processing engines. For example, here is how to tell if a workload designed to reach the dedicated NPU and GPU neural engine is instead falling back to general CPU and GPU compute.
- In Task Manager, go to the Processes tab.
- Choose which data to view. Right-click (or select and hold) in the column area and select any (or all) of the following columns: NPU, NPU engine, GPU, and GPU engine.
The Processes tab in Task Manager shows a popup menu of available columns to display. The NPU shows high usage from an AI-enabled application in the top row.
- Locate the desired software or enterprise agent in the Name column. If your application takes advantage of hardware designed for AI workloads, you can see utilization across the different areas.
The GPU Neural engine shows usage from an AI-enabled application in the top row.
- To visualize usage over time, navigate to the Performance tab.
- Select NPU or GPU. Alternatively, view the NPU or GPU by right-clicking (or selecting and holding) anywhere in the graph space. Select View, then choose among CPU, Memory, Disk, Network, GPU, or NPU views.
The NPU performance graph shows usage from an AI-enabled application.
The GPU performance graph shows usage from an AI-enabled application in the Neural engine.
To evaluate whether an application takes advantage of dedicated hardware appropriately, check if the application offloads to the NPU or the GPU's matrix arrays. If you see that the application mostly uses the CPU or runs on the GPU's rendering pipeline, that application might be misconfigured or it might not have been designed to take advantage of this hardware.
Once you identify whether the workload is running on the NPU, GPU, or CPU, you can spot-check individual devices for further details. For example:
- Is a device's AI workload reaching the NPU? Or is it falling back to the GPU rendering pipeline or general CPU compute?
- Which applications on the device are actually using the NPU or the GPU's neural engine?
Real-time validation for AI-driven app development
When building AI applications with frameworks like ONNX Runtime or Windows DirectML, you write code to target specific hardware execution providers (i.e., CPU, NPU, or GPU). If an application encounters an unsupported data type or driver mismatch, the framework can silently fall back to a higher-overhead compute pipeline to prevent the application from crashing.
To help you identify and correct these issues, as well as to track appropriate AI model performance, we've introduced more granular views in Task Manager specific to the NPU and the GPU neural engine.
Hardware use verification
You can use Task Manager for basic runtime verification to confirm what processing resources your AI workload is actively using.
- In Task Manager, open the Details tab.
- Right-click (or select and hold) on any column header and select Select columns.
- Check any of the following to monitor active execution: NPU, NPU engine, GPU, and/or GPU engine.
- Look at the active use data to identify any workloads that fall back to the CPU and, therefore, don't use the device's resources appropriately. They show the following symptoms: the NPU column stays at 0%, the NPU engine remains blank, while the GPU engine column shows no activity from its neural engine.
- (Optional) Enable Dedicated NPU memory, Shared NPU memory, Dedicated GPU memory, or Shared GPU memory to track weight allocations and memory overflow.
The Task Manager Details tab shows a pop-up menu to select additional columns.
Reasons for unexpected AI model performance
If your local AI model is running unexpectedly slow, the system might be silently falling back to the CPU or the standard GPU compute pipeline. There are two main reasons for this:
- A device might lack a specific driver.
- There might be a data type mismatch (for example, trying to force an FP32 model onto an INT8-optimized NPU or an unsupported operation in the model not compatible with the neural engine or NPU).
Modern silicon architectures integrate dedicated matrix accelerators right inside the graphics processor. Therefore, it's critical to track whether your math lands on the GPU 0 - Neural engine versus the NPU as you debug your execution provider bindings.
Another dimension that can explain variable AI model performance is through the new columns: Shared NPU memory and Dedicated NPU memory. These columns show how much memory an AI workload is using and which pool it comes from.
- Shared NPU memory: The NPU draws from system memory alongside the CPU and GPU. On a system with both Dedicated and Shared NPU memory, watch for rising shared NPU memory usage. It's your indicator that dedicated memory is filled, and allocations are spilling over to shared NPU memory, which can reduce performance.
- Dedicated NPU memory: Memory belonging to the NPU alone. A workload that fits within it doesn't compete with the rest of the system. This column is empty on integrated NPUs, which have no separate pool.
Live tracking
As the previous screenshots show, the Performance tab brings multiple details together into a live tracking view. This means you can run your tuned model, look at Task Manager, and instantly verify:
- The success state: Matrix multiplication executes on the NPU and/or the GPU neural engine as expected.
- The fallback state: If the NPU and the GPU neural engine both show zero usage while CPU or general GPU usage is increasing, the fallback is happening. Proceed to re-quantize your model.
Task Manager now makes execution provider fallback visible at a glance, in real time, alongside what the rest of the system is doing.
Power profile insights for power users
If it's not your job to build or troubleshoot AI applications, the enhanced Task Manager experience still offers workload transparency and proactive battery management.
AI workloads are computationally heavy. When a local application runs a background blur, a video upscale, or an image manipulation model, it has two main ways to execute these tasks.
- The CPU and standard GPU route: Power-hungry workloads kick the fans into overdrive and drain your battery in about two hours.
- The NPU and GPU neural route: Highly efficient workloads sip milliwatts and keep your laptop cool on your lap.
Imagine you're working on battery power and running a suite of creative or collaboration tools. They don't drain your battery as quickly if they're optimized to use modern hardware acceleration pipelines. And you no longer have to guess how your applications are impacting your system resources. To verify feature optimization using Task Manager:
- In Task Manager, open the Processes tab.
- Select an active application.
- Compare the data in the GPU engine, NPU, and NPU engine columns. That's where you can see exactly which execution engine the app is using in real time.
The Task Manager Details tab shows the NPU engine and dedicated and shared NPU data for Python.
An application can be optimized to use both the NPU and the GPU, but it might not be optimized to use either.
- If an application is optimized to use the NPU, you'll see either NPU - Compute or NPU - Neural under the NPU engine column. Depending on your processor and driver version, Task Manager labels active NPU workloads as NPU – Compute or NPU – Neural. Both indicate that on-device AI acceleration is actively running on the NPU.
- Similarly, if an application is optimized to use the GPU's hardware, the GPU engine column lists GPU – Neural.
- Apps that aren't optimized for either of these dedicated resources show one of the following symptoms:
- The CPU column shows high usage while the NPU column is idle.
- The GPU engine column doesn't show GPU - Neural.
More explicitly, Power usage and Power usage trends reflect and can confirm the impact.
Task Manager gives you instant operational awareness. It allows you to adjust your behavior constructively. For example, you can pause heavy background tasks until you're connected to a power outlet. Or, you can continue using apps that are already fully native for your unplugged, on-the-go workflows.
Visibility drives optimization
As AI workloads become more common on Windows devices, visibility into NPU and GPU neural engine utilization can help you make better informed decisions. With the latest Task Manager improvements, you can monitor AI processing activity alongside CPU, memory, storage, and networking data from a familiar interface.
Explore the new metrics in Task Manager to evaluate application performance, troubleshoot resource bottlenecks, and better understand how your devices use AI-capable hardware. Importantly, these growing capabilities today provide a foundation for future hardware innovation across the ecosystem.
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