NPU vs. GPU
While many AI and machine learning workloads are run on GPUs, there is an important distinction between the GPU and NPU.
While GPUs are known for their parallel computing capabilities, not all GPUs are good at doing so beyond processing graphics, as they require special integrated circuits to effectively process machine learning workloads. The most popular Nvidia GPUs have these circuits in the form of Tensor cores, but AMD and Intel have also integrated these circuits into their GPUs as well, mainly for handling resolution upscaling operations — a very common AI workload.
NPUs, meanwhile, simply take those circuits out of a GPU (which does a bunch of other operations) and make it a dedicated unit on its own. This allows it to more efficiently process AI-related tasks at a lower power level, making them ideal for laptops, but also limits their potential for heavy-duty workloads that will still likely require a GPU to run.