CPU, GPU, and NPU can all participate in AI workloads, but they serve different roles. For local language models, the key question is which processor the runtime actually supports, not simply which chips appear on the computer's specification sheet.
CPU: general purpose and widely available
The CPU is the computer's general-purpose processor. Many local models can run entirely on it, which is useful because no special graphics hardware is required. Larger models are often slower on the CPU than on a well-supported GPU.
GPU: high parallel throughput
GPUs are optimized for many parallel calculations, which fits the matrix operations used by neural networks. When enough VRAM or shared memory is available, GPU acceleration can increase local model generation speed substantially.
NPU: specialized AI acceleration
An NPU is optimized for certain neural-network operations and is often designed for energy-efficient AI workloads. Newer computers increasingly include one, but language-model performance depends on support from the runtime, model format, and specific operations.
Why an NPU may sit unused
Many local LLM runtimes were originally optimized around CPU and GPU execution. A computer can therefore contain an NPU without the selected application using it for the chosen model. Hardware presence alone is not enough.
Apple Silicon is a special case
Apple Silicon Macs combine CPU and GPU access through Unified Memory and also include a Neural Engine. For many local language-model runtimes, the GPU remains the primary acceleration path while the Neural Engine is not automatically used for every model.
Windows systems vary more
A Windows machine may combine an integrated GPU, discrete GPU, and NPU from different vendors. The useful question is therefore not merely “Does this PC have an NPU?” but “Does my runtime support this NPU for this model?”
Which processor fits which goal?
- CPU: broad compatibility, small models, no special accelerator required
- GPU: high LLM throughput when sufficient memory and runtime support are available
- NPU: energy-efficient AI workloads when the software path is explicitly optimized for it
Hybrid execution is possible
Some runtimes split model work between CPU and GPU. This can help when VRAM is insufficient for the full model, although speed then depends heavily on data movement and the hardware combination.
Priviot and hardware paths
For Priviot, hardware support should be communicated specifically rather than generically. A model should only be described as using a particular accelerator when the actual execution path supports it. See Local AI for more background.
Conclusion
CPU, GPU, and NPU are not interchangeable labels for an “AI chip.” The CPU is universal, the GPU is highly effective for many local language models, and the NPU can be efficient for supported workloads. The real result always depends on hardware, runtime, and model together.