How it works
NPU stands for Neural Processing Unit, or neural processing unit. It is a component designed to run operations used by artificial intelligence models, especially repeated calculations on matrices and tensors. Implementation varies between manufacturers, so the name does not guarantee the same performance or compatibility across all devices.
An NPU can exist as part of a system on chip, alongside other compute components. Software needs to recognize the hardware and convert model operations into a format it accepts. If an operation is not compatible, part of the work may be executed by another processor or by a combination of components.
Practical differences depend on the full stack: chip, memory, operating system, libraries and model. The presence of an NPU alone does not mean any application will use it or that every model will run locally. Developers need to verify support and measure application behavior on the target device.
Practical example
Imagine a mobile app that transcribes a short recording. If the model and system are compatible, the application can route inference operations to the NPU. Inference is the step where a trained model processes a new input and produces an output.
The team tests transcription under different audio conditions, observes response time and checks whether the device remains stable. It also confirms whether the model fits within available resources and how the app behaves when accelerator support is not available. The same flow can fall back to CPU, GPU or a remote service, depending on the architecture chosen.
Processing data on device can reduce the need to send it to a server, but this does not happen automatically. The app may still transmit information for other reasons. Privacy depends on product design, permissions and the data it sends.
What it is for
An NPU can accelerate certain AI tasks on devices like phones and computers, and help reserve other components for different work. Depending on hardware and application, it can support camera features, audio, pattern recognition and local models. Real usefulness needs to be confirmed for each workload.
For software creators, the challenge is to provide a hardware compatible path without leaving behind devices that do not have the same capability. It may be necessary to offer a lighter alternative, use the CPU or transfer the task to an external service. This decision involves performance, availability, privacy and maintenance.
Compare execution under representative conditions, not just in a demonstration. Check memory consumption, response time, output quality and behavior when the accelerator is not available. A fast result on an isolated operation does not prove the whole application will be faster.
Difference between NPU, GPU and CPU
CPU is the general purpose processor that coordinates many device tasks. GPU is suited to parallel operations and can execute AI calculations, in addition to other workloads. NPU is focused on calculation patterns used by neural networks. Boundaries vary and can overlap.
The best option depends on the model, software and hardware available. An application can distribute operations across components or use only one of them. Instead of choosing by chip name, test the execution path that will be used by your users.