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Choose an AI PC by the models and tasks you plan to run—not by its “AI PC” label or the largest TOPS number on the spec sheet. For local inference, check whether your software uses an NPU, GPU, or CPU; then match the model’s memory and context needs to the system’s RAM and graphics memory. A Copilot+ PC is relevant for certain Windows features, but it is not a general requirement for running AI locally.
Start with the models and workloads you want to run
“Local AI” covers very different jobs: chatting with a language model, summarizing documents, generating images, or running an on-device Windows feature. Before comparing PCs, identify the model or model family, its precision or quantization, the context length you expect to use, and whether you need several tasks or models to run at once. A configuration comfortable for a small model may not accommodate a much larger one or a long context.
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Memory is not determined by model size alone. The runtime, model precision, context, operating system, and other open applications all affect what will fit. Intel’s AI PC system-configuration guidance notes that additional operational memory may be needed to avoid paging and the latency it can cause. Treat storage and memory as separate checks: a large SSD can hold model files, but it does not provide RAM or VRAM for inference.
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NPU: for software built to use it
A neural processing unit (NPU) is useful when an application or Windows feature is specifically designed to run on it, often for supported on-device features with low power use. Its presence does not mean every local model will use it. Microsoft says software must be programmed to target an NPU, and supported model formats matter; some models may need quantization to fit the supported low-bit formats.
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GPU: a common route for supported local-model runtimes
A compatible graphics processor can accelerate inference when the runtime supports it. For GPU-based workloads, check graphics-memory capacity (VRAM) against the specific model and context you intend to use. A GPU’s general performance rating or the PC’s NPU TOPS figure does not tell you whether a particular model will fit in VRAM or how quickly it will run.
Runtime support is specific to the software and hardware combination. Ollama documents NVIDIA GPU acceleration and, in its June 2026 release, Vulkan acceleration across a wider range that includes AMD and Intel GPUs. That does not establish that every model or application uses every GPU vendor equally; verify support for your exact runtime, model, and driver.
CPU: a possible fallback, not a universal performance promise
Some supported runtimes can run on a CPU, including Microsoft Foundry Local, which can select a provider and fall back to CPU. Whether that is usable for your workload depends on the model and task. Confirm the supported hardware path and try to find model-specific performance information for the intended software rather than assuming a CPU-only system will meet your needs.
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Do you need a Copilot+ PC or an NPU?
For local inference in general, no: an NPU is not a universal requirement. Microsoft’s answer to whether an NPU or Copilot+ PC is needed for Windows AI features is, “It depends on which feature you’re using.” The documented Windows AI APIs require a Copilot+ PC, but Foundry Local can run on Windows devices with a DirectX 12-capable GPU, and Windows ML spans CPUs, GPUs, and NPUs. See Microsoft’s Windows AI FAQ for the distinctions between these options.
Microsoft describes Copilot+ PCs as having an NPU capable of more than 40 trillion operations per second (TOPS). That is a Copilot+ feature-class threshold, not a general local-LLM performance score or a minimum requirement for every local model. Even on a system above that threshold, software must target the NPU and support a compatible model format to use it. Microsoft’s Copilot+ developer guide explains those requirements.
Estimate RAM and VRAM from the actual model
There is no single VRAM figure that guarantees support for all local AI. Model size, precision, context length, runtime behavior, and concurrent workloads change memory use. Published examples illustrate the range, but should not be turned into a blanket buying minimum:
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- Ollama’s 2024 Llama 3.2 Vision announcement specifies at least 8 GB of VRAM for its 11B model and at least 64 GB for its 90B model. These are minimums for those particular models, not a rule for other models. Ollama’s Llama 3.2 Vision announcement
- In a 2025 example, Ollama’s updated engine used 21.4 GiB of VRAM for Gemma 3 12B at a 128k context. That example shows why context length can change the memory picture; it is not a universal requirement for 12B models. Ollama’s model-scheduling example
For a prospective PC, compare its usable RAM and VRAM with the requirements for the exact model, precision, and context you plan to run. Leave room for the operating system and companion applications, especially if you expect to keep a browser, editor, or other workloads open. Do not infer a model’s fit from its parameter count alone.
Check software, drivers, and operating-system support
Before buying, confirm that the exact model and format work in your chosen runtime on the PC’s operating system and accelerator. Microsoft documents provider choices across Qualcomm NPUs, DirectX GPUs, NVIDIA CUDA, and CPU fallback for its Windows runtime; provider availability is not the same as guaranteed support for every model or feature. Intel recommends current GPU and NPU drivers in its Windows ML support guidance.
If you plan to use Foundry Local offline, Microsoft says a downloaded and cached model can run without an internet connection; downloading the model for the first time requires internet access. Because runtime and driver support changes, check the software vendor’s current compatibility information for the specific PC configuration rather than relying solely on the processor brand or an “AI PC” badge.
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Compare the systems that fit your use
Once you know the workload and software path, compare candidate PCs on these points:
- Model and precision: Does the runtime support your intended model and format?
- RAM and VRAM: Will the system’s memory accommodate the model, context, operating system, and other apps?
- Context and concurrency: Does your expected context length or simultaneous workload raise memory needs?
- Accelerator and runtime: Is the NPU, GPU, or CPU path supported by the software you intend to use?
- Drivers and OS: Are the required operating system version and current drivers available for the exact machine?
- Form factor: For a laptop, weigh portability and battery life against sustained workload needs; for a desktop, consider noise and whether memory or graphics can be upgraded.
A desktop with a discrete graphics card may be a practical choice if your runtime supports its GPU and its VRAM suits your target model and context. A laptop may make more sense when portability matters or when its specific NPU features match your software. There is no universal winner: ask for model-specific performance on the intended runtime, and treat vendor TOPS claims as only one part of the compatibility picture.
Interpret example specifications carefully
AMD’s 2026 enterprise AI PC checklist includes example specifications of 16 GB RAM, a 256 GB SSD, a 40 TOPS NPU, and 8 GB of GPU VRAM. AMD presents these as enterprise checklist examples, not a universal minimum for home users or every local AI workload. Its figures should not be combined with model-specific VRAM examples as if they defined one general-purpose buying threshold. AMD’s AI PC consideration checklist
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