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Choose an AI PC by the models and workloads you intend to run—not by its “AI PC” label or NPU TOPS figure. For local inference, check whether your chosen runtime supports the machine’s GPU, NPU, or CPU; whether its memory can hold the model at your target precision and context length; and whether its drivers and operating system are supported. You do not necessarily need a Copilot+ PC or an NPU to run AI locally.
Start with the models and tasks you want to run
“Local AI” covers different workloads, from image-aware models to conversational agents. Before comparing computers, identify the software and specific models you plan to use, then check their supported formats, hardware providers, memory needs, and context options. A machine that suits one model may be unsuitable for another, even if both are described as local AI.
For a language model, model size and precision affect how much memory its weights require. The context length—the amount of conversation or other input the model can consider—also matters. Running several applications or models at once adds further demand. Intel advises allowing operational memory beyond the model’s basic needs so the system does not have to page data to storage, which can add latency. Its AI PC configuration guidance discusses sizing around model size, precision, the operating system, and companion applications.
Separate NPU features from local AI inference
An NPU is a processor designed for supported AI operations, often with power-efficient on-device features in mind. It helps only when the application and model are designed to use it; the “AI PC” label does not mean every local model will run on the NPU.
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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 API set requires a Copilot+ PC, but local inference more broadly can use other hardware. Microsoft says Foundry Local can run on Windows devices with a DirectX 12-capable GPU, and Windows ML supports CPUs, GPUs, and NPUs. A downloaded and cached Foundry Local model can run offline, although downloading a model for the first time requires internet access. See Microsoft’s Windows AI FAQ.
What the 40+ TOPS figure tells you
Microsoft describes Copilot+ PCs as having an NPU capable of more than 40 trillion operations per second (TOPS). This is a Copilot+ class capability threshold, not a general requirement for local models and not a comparable score for how fast a particular model will run. NPU-targeted software and supported model formats are needed to use that processor; some models may need quantization to match supported low-bit formats. Microsoft’s Copilot+ developer guide explains the NPU requirements.
Size memory for the model, context, and rest of the system
Check system RAM and GPU video memory (VRAM) separately. System RAM supports the operating system, applications, and workloads that use main memory; VRAM is the GPU’s dedicated memory. A runtime may place a model across different processors or memory pools, but that does not make their capacity interchangeable in every setup. SSD space stores model files; it is not a substitute for the memory needed while a model is running.
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There is no universal VRAM minimum for local AI. Ollama’s examples show how much the answer can vary: it specifies at least 8 GB VRAM for Llama 3.2 Vision 11B and at least 64 GB for the 90B model in that family. Separately, Ollama reported that its Gemma 3 12B example with a 128k context used 21.4 GiB VRAM. These figures are tied to those models and, in the latter case, that context and setup—not rules for every model. Review the requirements and performance information for the exact model and runtime you expect to use: Ollama’s Llama 3.2 Vision announcement and Ollama’s model-scheduling example.
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For system RAM, account for model size and precision as well as the operating system, companion applications, and whether you intend to run multiple workloads concurrently. A checklist from AMD lists 16 GB RAM, a 256 GB SSD, a 40 TOPS NPU, and 8 GB GPU VRAM as example enterprise AI PC specifications; it is not a universal recommendation for home users or all local models. Treat it as one enterprise-oriented checklist, not a substitute for model-specific sizing: AMD’s AI PC consideration checklist.
Compare the processor that your software can actually use
The GPU is often a key option for local inference when the chosen runtime supports it. Its VRAM capacity can constrain which model and context fit. Check GPU support by vendor and runtime rather than assuming that any discrete graphics card will accelerate every model. Ollama documents NVIDIA acceleration and, in a June 2026 release, Vulkan acceleration across a wider range that includes AMD and Intel GPUs. Supported hardware and software can change, so verify current compatibility for the version you will install.
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An NPU may be useful for applications specifically built to target it, especially supported on-device features. A CPU can also run workloads when a supported runtime offers CPU execution or fallback, but speed and suitability depend on the model and task. Microsoft documents provider selection across Qualcomm NPU, DirectX GPU, NVIDIA CUDA, and CPU fallback for the Windows runtime described in its Windows AI FAQ. That list is not a promise that every model or application supports every provider.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Verify software, model format, and drivers before buying
Hardware specifications alone cannot establish that a particular local AI setup will work. Check the exact runtime’s supported operating systems, processor providers, GPU models, and model formats, then confirm that the model you want is available in a compatible format. For NPU use, verify that the application explicitly supports the device and that the model format is accepted; a model that works through GPU or CPU execution may not be usable on the NPU.
Check driver and operating-system requirements for the specific computer, including whether current GPU and NPU drivers are available. Intel recommends keeping those drivers current in its Windows ML support guidance. Microsoft’s documentation describes automatic provider selection for the Windows runtime and the possibility of CPU fallback, but fallback does not guarantee that performance will meet your needs.
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Choose a laptop or desktop around how you will use it
For a desktop, compare upgrade options and whether you can install a discrete GPU with enough VRAM for your target models and contexts. A graphics card with 16 GB VRAM may be a capacity target worth investigating for particular workloads, but the cited examples do not establish 16 GB as a general local-AI threshold. Confirm the exact model, context, runtime support, and expected performance before choosing a card.
For a laptop, weigh sustained workload performance and memory capacity against portability, battery life, heat, and fan noise. An NPU can be relevant to supported low-power features, but it is not a replacement for checking GPU VRAM or system RAM when your intended runtime uses those resources. In either form factor, make sure the memory configuration is sufficient for the workload and that upgrade options—or the lack of them—fit your plans.
Use a workload-based comparison checklist
- Model and precision: Which exact models and supported formats do you intend to run?
- Memory: How much system RAM and, where relevant, GPU VRAM does that setup need at your intended precision?
- Context and concurrency: What context length do you need, and will you run other applications or models at the same time?
- Runtime and accelerator: Does the software support the computer’s GPU, NPU, or CPU for that model?
- Compatibility: Are the operating system and current drivers supported?
- Form factor: Do portability, battery life, noise, or future upgrades matter more for your use?
- Performance evidence: Look for model-specific performance on the runtime you plan to use rather than comparing TOPS figures as though they predicted local-model speed.
The available guidance does not establish a neutral cross-vendor speed or value winner. Compare the same target model and context on the intended software where possible; otherwise, treat performance claims as specific to the vendor, runtime, model, and test conditions stated.
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