UK How an NPU and a CPU differ
A CPU, or central processing unit, is the general-purpose processor that runs the operating system, the application and any code you give it. An NPU, or neural processing unit, is a dedicated engine for the multiply-and-add maths at the heart of neural networks. Since 2023 most new laptop and embedded processors from Intel, AMD and Qualcomm have carried both on the same chip.
A CPU can run AI models, but it was designed for varied, branching work rather than millions of repeated multiply-add operations. An NPU does that one job efficiently, at low precision such as INT8. In practice the CPU handles the application and hands the model to the NPU, so a fanless computer can run inference all day without the CPU becoming the bottleneck.
How a CPU runs AI
A modern CPU has a handful of powerful cores, each able to run any instruction, follow complex branches and switch between tasks in microseconds. Vector extensions such as AVX-512 and Intel AMX, or Arm’s NEON and SVE, let each core process several numbers at once, so small models and occasional inference run acceptably on the CPU alone.
The limit is efficiency. Every operation still passes through general-purpose machinery built for flexibility, so sustained inference on video keeps several cores busy, draws more power and leaves less headroom for the rest of the application. Our guide to how a CPU works covers cores, threads and clock speed in more detail.
How an NPU runs AI
An NPU is built around large arrays of multiply-accumulate (MAC) circuits, fed by local on-chip memory so that weights and activations do not have to travel back and forth to system RAM. It works at low precision, typically INT8 and FP16, and some newer NPUs add INT4 or block floating-point formats. Its performance is quoted in TOPS, trillions of operations per second.
Because it does one kind of work, an NPU delivers far more inferences per watt than CPU cores. The trade-off is flexibility: it only runs the layer types its compiler supports, so models reach it through toolkits such as OpenVINO, ONNX Runtime or AMD’s Ryzen AI software, and unsupported layers fall back to the CPU or GPU. Our white paper on what an NPU is and how it works goes into the architecture.
NPU vs CPU side by side
| Factor | CPU | NPU |
|---|---|---|
| Purpose | Any software: OS, applications, control logic | Neural network inference only |
| Core design | A few powerful, flexible cores | Large arrays of simple multiply-accumulate circuits |
| Typical precision | FP32 and FP64, with INT8 vector support | INT8 and FP16, sometimes INT4 |
| Energy per inference | Relatively high | Much lower |
| Model support | Runs any model | Runs supported layers; others fall back |
| Programming | Any language and framework | Through a vendor toolkit or ONNX Runtime |
| Best at | Small models, pre- and post-processing, branching code | Sustained inference on camera, audio or sensor data |
Neither replaces the other. The CPU is always present and always in charge; the NPU is an accelerator that the CPU hands work to.
When to use the CPU and when the NPU
In a real application the work is split by what each part does best.
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Use the NPU for continuous inference: object detection on a camera stream, defect classification, keyword spotting, noise suppression and background effects.
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Use the CPU for the application itself, data handling, communications, small or occasional models, and layers the NPU cannot run.
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Use a GPU for larger models, several high-resolution streams, or models with operators the NPU does not support.
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Benchmark first. Run your model on each target and compare frames per second, latency and power before choosing hardware.
Where the GPU fits
Most processors with an NPU also have integrated graphics, and that GPU can run AI too. It is more flexible than the NPU and often faster in short bursts, but uses more power for sustained work. Our comparison of GPU vs NPU for edge AI explains how to split a workload between them, and Intel’s OpenVINO can spread one model across CPU, GPU and NPU automatically.
NPUs in industrial computers
NPUs have moved quickly from laptops into embedded and industrial boards. Our Intel Core Ultra computers carry Intel AI Boost NPUs rated from 11 TOPS on Meteor Lake to 50 TOPS on Panther Lake, our AMD Ryzen AI 300 systems carry a 50 TOPS XDNA 2 NPU, and Ryzen 8000-series processors in our range carry a 16 TOPS NPU. Qualcomm and NXP Arm processors add NPUs to low-power gateways and panel PCs.
Quick answers on NPU vs CPU
NPU vs CPU: common questions
Are NPUs better than CPUs?
Only for AI inference. An NPU runs neural networks far more efficiently, but it cannot run an operating system or general software. Every computer still needs a CPU; the NPU is an addition.
Can a GPU be used as an NPU?
It can do the same job. A GPU runs AI models well and is more flexible than an NPU, but it usually draws more power for the same sustained workload.
Which CPU has the best NPU?
In our range, 50 TOPS is the current top rating. Intel Core Ultra Series 3 (Panther Lake) and AMD Ryzen AI 300-series processors both carry NPUs rated at around 50 TOPS. Real performance depends on your model and the software support.
Is an NPU really necessary?
Not always. If AI is occasional or the model is small, the CPU or integrated GPU may be enough. An NPU pays off for continuous inference in fanless or power-limited systems.
Can the NPU and CPU work at the same time?
Yes. They are separate engines on the same chip. The CPU runs the application while the NPU runs the model, and toolkits such as OpenVINO can split one model across both.
We configure processors with integrated NPUs in industrial and edge AI computers, with the drivers and AI runtime installed and validated as standard. Tell us about your model and workload, and we will recommend the processor that suits it.