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Use an NPU when your priority is a Windows AI feature that supports it or sustained inference with lower power use; use a GPU when a supported model and runtime need high throughput, especially for image, video, or generative AI. Neither accelerator is required for all local AI: a CPU can run some models, and the right choice depends on the application, model, runtime, memory, and workload. Microsoft’s and AMD’s guidance describes these workload differences, but does not establish a universal NPU-versus-GPU speed or efficiency winner.
What does it mean to run AI locally?
Local inference is the execution of a trained AI model on your own device or infrastructure, rather than sending every request to a cloud service. “Locally” describes where inference happens; it does not automatically tell you which chip performs it, whether the device needs an internet connection for setup, or whether every feature of an app works offline.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesOn Windows, there are several distinct routes. Built-in Windows AI APIs expose supported AI features to applications; Foundry Local provides a catalog of local models and hardware options; and Windows ML is intended for applications that bring their own ONNX models and need control over inference. These are not interchangeable entry points, and their requirements vary. Microsoft’s Windows AI solution comparison summarizes which route fits different development needs.
For a user, the practical distinction is whether you want a feature already integrated into Windows or an application, a ready-to-use local model runtime, or a specific model deployed through an app. The hardware choice follows from that software path: the model and runtime must be able to use the accelerator you have.
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What is the difference between an NPU and a GPU for local AI?
An NPU is positioned for AI inference that can run efficiently and continuously on-device; a GPU is designed to handle highly parallel workloads and is often the better fit when throughput is the priority. These are workload tendencies, not guarantees for every chip, model, or application.
| Decision factor | NPU | GPU | What to check |
|---|---|---|---|
| Typical fit | Supported on-device AI features and sustained inference where power use matters. | High-throughput work, including image, video, and generative AI workloads. | Does the exact app, model, and runtime support this accelerator? |
| Power priority | Microsoft describes it as suited to “battery-efficient, sustained on-device inference”; AMD also characterizes NPUs as particularly suited to sustained, low-power local inference. | The cited Windows guidance emphasizes throughput, not a universal power advantage or disadvantage. | For a mobile device, test the real recurring workload and battery impact rather than infer it from the chip category. |
| Throughput priority | May be appropriate when the task is supported and its sustained-inference profile matches the need. | Microsoft says discrete GPUs generally provide maximum performance for high-throughput cases; this is general guidance, not a promise for every model or device. | Measure the task you actually run, with the same model, settings, and request pattern. |
| Compatibility path | Available when the Windows feature or execution provider supports it. | Available when the model and runtime support the relevant GPU. | Windows ML can use CPU, GPU, or NPU execution providers; support is application-specific. |
| Fallback | Not universal across AI software. | Not universal across AI software. | Microsoft describes CPU execution as a broad fallback, though performance and supported workloads vary. |
Microsoft’s overview calls the NPU an option for “battery-efficient, sustained on-device inference” and the GPU a choice for “high-throughput workloads such as image, video, and generative AI.” Read those descriptions as a starting point for choosing what to investigate, not as benchmark results. A fast-sounding accelerator specification cannot tell you whether a particular application uses that chip or how quickly it will complete your task.
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Start with the task and software, then weigh throughput against power use and memory. The best choice is the hardware that the intended model and runtime support and that meets your latency, battery, and capacity needs.
Choose an NPU-oriented setup for supported features and sustained inference
An NPU-oriented device makes sense if you want a particular Windows feature that runs on the NPU, or if your intended local inference workload is sustained and lower power use matters more than maximum throughput. AMD’s overview of Ryzen AI for Business describes NPUs as especially suited to sustained, low-power inference.
This does not mean every AI app will use an NPU just because the computer has one. Verify support for the specific feature or model before buying hardware or planning a deployment. If the feature is a built-in Windows API, check its hardware support rather than assuming all APIs use the same accelerator.
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Choose GPU acceleration when throughput is central
A supported GPU is a stronger candidate when your workload is highly parallel and you want high throughput, particularly for image, video, or generative AI. Microsoft says discrete GPUs generally offer maximum performance for these high-throughput cases. That is a broad vendor recommendation, not an equivalent-model comparison across all NPUs and GPUs.
A GPU is only useful to the extent that the software can send the workload to it. Before treating a discrete GPU as a solution, confirm that the application and model support it and that the available memory is adequate for the model and its context. The cited guidance does not establish a universal minimum memory amount or a particular GPU recommendation.
Use the CPU when compatibility or simplicity matters
CPU execution can be the practical choice when a model is modest, the selected runtime supports CPU inference, or broad compatibility matters more than acceleration. Microsoft describes CPU as a universal fallback, but that does not mean every model will run quickly or that every application offers the same CPU path. Windows Server guidance notes that some models run on CPU while other workloads benefit from GPU acceleration.
Fallback is a useful recovery option when an accelerator is unsupported or unavailable, but it may change the time a request takes. Check the app’s own settings or documentation to learn whether it selects a device automatically or lets you choose an execution provider.
Do you need a GPU or an NPU to run AI locally?
No. Local AI does not universally require either accelerator, and it does not universally require a Copilot+ PC. Microsoft documents multiple Windows paths for local inference, including options that do not require Copilot+ hardware.
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A CPU can be sufficient for some models and use cases, while a GPU or NPU can improve suitability for particular workloads when supported. The useful question is not “Which chip must every local AI computer have?” but “Can this model, through this runtime, run on my available hardware at an acceptable speed and power level?”
Microsoft identifies Copilot+ PCs as a hardware category with an NPU rated at 40+ TOPS, alongside other platform requirements. That figure is a qualification threshold for the Copilot+ PC category, not a universal minimum for local inference. Foundry Local and Windows ML do not require Copilot+ hardware, according to Microsoft’s Copilot+ PCs developer guide and Windows AI solution comparison.
TOPS means trillions of operations per second, but a TOPS figure alone does not predict the experience you will get from a real model. Model architecture, quantization, context length, concurrency, memory, runtime, and latency target all affect compute needs. Use the figure to understand a stated hardware category, not to rank a GPU against an NPU or infer an application’s real-world speed.
How do Windows AI APIs, Foundry Local, and Windows ML differ?
They serve different software needs: Windows AI APIs provide supported system-level features, Foundry Local offers local model inference through a broader model catalog, and Windows ML supports apps that package their own ONNX models and need hardware-accelerated inference.
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Windows AI APIs: use a supported Windows feature
Windows AI APIs are the route to consider when an application needs a feature exposed by Windows. Hardware support is feature-specific. Microsoft’s current Windows AI APIs support information lists Phi Silica for Copilot+ NPUs and supported NVIDIA and AMD GPUs, while other APIs in the table have different support.
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There is an important distinction for Copilot+ PCs: supported APIs on those devices run on the NPU. GPU or CPU support shown in the table refers to expansion on non-Copilot+ devices; it should not be read as a user-selectable alternate backend for those APIs on Copilot+ PCs. Because the matrix is feature-specific and may change, check the entry for the exact API and device you intend to use.
Foundry Local: use a local model catalog
Foundry Local is suited to applications that need access to a broader catalog of local models and hardware. It can continue inference offline after a model is downloaded and cached, but the initial model download requires internet access. “Works offline” therefore describes inference after setup, not the first download.
Use the Windows AI app FAQs and the current Windows AI solution documentation to confirm the hardware and model path for your intended use. Do not assume that an offline-capable runtime means the model is already installed or that every model in a catalog supports every accelerator.
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Windows ML: bring an ONNX model and select a supported provider
Windows ML is the relevant path when an application brings its own ONNX model and needs control over inference on supported CPU, GPU, or NPU execution providers. Microsoft describes it as a way to deliver hardware-accelerated inference across those provider types.
An execution provider is the software route by which a model runtime targets a hardware backend. Having the device is not enough by itself: the application’s implementation, model compatibility, and provider support determine whether inference can actually use it. Microsoft’s Windows ML overview explains the role of the platform.
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How can you compare two local AI setups before buying or deploying?
Compare the same intended task on the actual candidate setups, and verify compatibility before treating a specification as a performance answer. The cited platform documentation does not provide a controlled, equivalent-model benchmark that settles NPU-versus-GPU speed or efficiency overall.
- Name the task. Write down what you will run—such as a Windows AI API feature, image generation, video processing, or another model-based application—and whether you care most about response time, sustained use, battery, or concurrent requests.
- Identify the software route. Determine whether the application uses a built-in Windows API, Foundry Local, Windows ML, or another runtime. If it uses Windows AI APIs, check the support matrix for the exact API; if it uses Windows ML, confirm the relevant execution provider is supported.
- Check model and hardware compatibility. Confirm that the model can run through the chosen runtime on the candidate CPU, GPU, or NPU. Do not assume that a model’s availability implies support for every device or backend.
- Check memory against the real workload. Consider model size and context needs, as well as the number of simultaneous requests you expect. A compute rating does not establish whether the machine has enough available memory for your model and request pattern.
- Set a meaningful test condition. Use the same model, quantization, context length, input, concurrency, and runtime settings on both candidates. Compare the result that matters to you: latency, throughput over sustained use, or power impact. A comparison with different settings will not isolate the accelerator choice.
- Confirm offline and fallback behavior. If offline use matters, distinguish the one-time model download from subsequent cached inference. Find out whether the application can fall back to CPU when the preferred accelerator is unsupported or unavailable.
- Choose against your priority, not a single headline number. Prefer an NPU-oriented option for supported features and sustained low-power use, a supported GPU when throughput is the primary goal, or CPU execution when compatibility with a modest workload is adequate.
This checklist is a decision method, not a claim that all apps expose benchmarks or let users choose providers manually. When the application does not expose enough information to confirm backend selection, its documentation or the developer is the appropriate source for that detail.
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What can make local AI slower or incompatible than expected?
The accelerator label is only one part of the system. A model may fail to use the expected device, exceed available memory, or behave differently under the context length and request pattern you actually use.
- Unsupported API or model: hardware support for one Windows AI API does not imply support for another. Match the exact feature, model, and device against current documentation.
- Runtime or provider mismatch: an NPU or GPU may be present but not targeted by the app’s runtime. Confirm which provider the application supports and uses.
- Insufficient memory: model architecture, quantization, context length, and concurrency affect memory and compute demands. TOPS cannot answer whether the system can hold the model and its active context.
- Different test workloads: a short single request may not represent sustained inference or multiple concurrent requests. Compare under the pattern that reflects your use.
- Offline setup mistaken for offline availability: a local model runtime may require an internet connection to download the model initially, even when later inference can be offline.
- Expectation based on a category threshold: 40+ TOPS qualifies the NPU component of the Copilot+ PC category; it does not guarantee a particular model’s speed or establish the minimum hardware for all local AI.
If an accelerator path does not work, check application and model compatibility first, then the runtime’s supported execution providers. If the app supports CPU inference, that may let you proceed while accepting that performance can differ.
Frequently Asked Questions
Can local AI work without an internet connection?
Yes, some local inference can run offline once the model is present on the device. Whether an app needs a connection for account checks, downloads, or other features is a separate question from where its model performs inference.
Does a higher TOPS number mean an AI model will run faster?
Not by itself. TOPS does not capture the complete effects of model architecture, quantization, runtime support, memory, context length, or the work a particular application assigns to the chip.
What does an ONNX model have to do with Windows ML?
ONNX is the model format specifically named in Microsoft’s Windows ML guidance for applications that bring their own model. The app still needs a compatible runtime and execution provider for the hardware it intends to use.
Can I switch a Windows AI API from NPU to GPU on a Copilot+ PC?
Microsoft’s documentation distinguishes Copilot+ PCs from non-Copilot+ devices: supported APIs on Copilot+ PCs run on the NPU. GPU or CPU support shown for an API refers to expansion on non-Copilot+ devices, rather than an alternate backend selector on a Copilot+ PC.
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