Do I need an NPU to run AI models locally?

No. For running large models, generating images or experimenting, the GPU and its VRAM matter far more. The NPU helps with small, constant AI tasks like background blur, captions and voice typing, which it handles without draining your battery.

Can an NPU replace a GPU?

Not for real local AI work. The NPU doesn't have the memory or the software support to run large models, and most AI tools are built for GPUs. Think of the NPU as a helper that handles small tasks so the GPU stays free for the big ones.

Is a 40 TOPS NPU as good as a 40 TOPS GPU?

No. The chips are built differently and use different memory and software, so their TOPS numbers can't be compared directly. A high TOPS figure tells you the NPU can handle supported on-device AI features, not that it can run your models.

Can an NPU run local LLMs?

Only some small ones. Microsoft's Phi Silica runs on NPUs in Copilot+ PCs for tasks like summarising and short text generation. Bigger open-source models like Llama, Qwen or Mistral still need a GPU with enough memory to run properly.

What should I check before buying a laptop for local AI?

Check the GPU first, then its VRAM, then the system RAM, and make sure the software you use supports the hardware. Decide which models you want to run before you shop, and buy for those. The NPU is a bonus, not the deciding spec.

NPU vs GPU for Local AI: What Actually Matters?

There is a lot of confusion around NPUs right now, and some of it comes from the way AI PCs are being marketed.

Mention an NPU and you will often hear that it is there for things like webcam effects, noise cancellation and other AI features running quietly in the background.

That's not wrong. It's just incomplete.

An NPU isn't a background-AI processor. It's a processor specifically designed to run certain AI workloads efficiently. If the software supports it and the model fits its capabilities, an NPU can be doing the main AI work of an application while you're actively using it.

That distinction matters if you're trying to understand what an NPU actually brings to local AI.

(Still deciding whether you need AI hardware at all? Read Do I Actually Need an AI Laptop? first.)

So, What Does an NPU Do?

NPU stands for Neural Processing Unit. Unlike a CPU, which is designed to handle a huge variety of computing tasks, an NPU is built around the mathematical operations commonly used by neural networks.

Its big advantage is efficiency.

That makes an NPU useful for the obvious background jobs—microphone noise removal, live captions, webcam processing and similar features—but it doesn't stop there.

An NPU can also accelerate foreground AI workloads, including supported speech recognition, translation, computer vision, image processing and some local generative AI applications. Microsoft's Phi Silica, for example, is a small language model built to run on the NPU inside Copilot+ PCs.

The catch is the word supported.

AI software needs to be written or optimised to take advantage of the NPU. A model that runs beautifully on a GPU won't automatically become faster simply because your laptop has an NPU. IBM's NPU vs GPU explainer covers this design difference in more depth.

Then Why Do GPUs Still Matter?

Because local AI isn't one workload.

Running a small speech model is very different from running a large language model with billions of parameters. Generating an image is different again.

GPUs are exceptionally good at handling large amounts of parallel computation, and they've had years of development behind them as AI accelerators—most of it built on CUDA, NVIDIA's software layer that developers have relied on for nearly two decades.

For people experimenting with local LLMs, image generation, AI video or model development, the GPU can therefore be much more important than the presence of an NPU.

And don't overlook VRAM.

With local AI, memory can become a limiting factor surprisingly quickly. A powerful GPU with plenty of fast VRAM can run models and workloads that simply aren't practical on a small, power-efficient NPU. Our guides to how much VRAM you need and RAM vs VRAM go through the numbers.

NPU vs GPU Isn't Really an Either-Or Choice

This is where the comparison gets interesting.

A modern PC doesn't necessarily have to decide between the two.

The CPU, GPU and NPU can each handle different parts of an AI workload. An NPU might process an efficient neural-network operation while the GPU handles a much heavier computation and the CPU takes care of the surrounding application. Microsoft's Windows ML is built exactly this way: apps send each task to whichever chip suits it best.

The exact division depends on the software, model and hardware.

That's why simply looking at an AI processor's TOPS figure can be misleading. TOPS tells you something about theoretical AI processing capability, but it doesn't tell you how quickly your particular model will run. Microsoft's Copilot+ PC label, for instance, requires an NPU that can hit at least 40 TOPS—but that number says nothing about whether your model will run well.

Model architecture, precision, quantisation, memory bandwidth, software optimisation and supported operations all matter.

What Should You Look For?

If you're buying a laptop mainly for everyday AI features and efficient on-device processing, an NPU is genuinely useful.

If you're buying a machine because you want to run larger local models, generate images or experiment with AI seriously, look closely at the GPU and its VRAM. Our AI laptop workload guide matches specs to what you'll run, and NVIDIA vs AMD vs Apple for AI covers the platform question.

And if you're buying an AI PC for the long term, having both a capable NPU and GPU could be more useful than treating them as competitors.

The NPU isn't replacing the GPU.

It doesn't need to.

Its purpose is to make AI computing more efficient—and, where the software supports it, to handle real AI workloads in its own right.

That's a much more useful way to understand the NPU than simply calling it the chip that handles AI in the background.