GPU vs. NPU vs. CPU

A CPU handles general-purpose program execution, a GPU emphasizes parallel computation, and an NPU specializes in neural-network operations. (Intel: CPU vs. GPU) (Intel: AI accelerators)

These names describe processing roles, not necessarily three separate physical chips. A system may integrate multiple processing engines, while other designs use discrete accelerators alongside a CPU. (Intel: AI accelerators)

Use this distinction as a starting point, not a ranking. The next question is which operations your application performs and whether its software can use the available hardware.

A system-on-chip can integrate CPU cores, memory-related functions, interfaces, and accelerators such as GPUs or AI blocks. (Arm: SoC development) An AI accelerator can also be a separate component, so a processor name alone does not describe the entire machine. (Intel: AI accelerators)

1. CoordinateCPU runs general application work
2. AccelerateGPU or NPU handles supported operations
3. ReturnResults feed back into the application

Imagine an application that reads a camera image, detects an object, and updates an on-screen result. Use the diagram as a conceptual model: there is general program work as well as model computation. The exact allocation belongs to the software and hardware design, not to the acronym printed on a product page.

Training develops a model from data; inference uses a trained model to produce results from new inputs. (NVIDIA: edge AI) A robot can run inference locally even when its model was trained in a data center. (NVIDIA: edge AI)

Edge AI describes the location of computation, near the data or users. It is not another processor type, and it does not require one particular brand of accelerator. (NVIDIA: edge AI)

Quantization reduces the numerical precision used by a model and can change its storage and execution requirements. Accuracy and supported formats need evaluation rather than an assumption that every reduced-precision model behaves identically. (NVIDIA: quantization)

This is why a useful hardware comparison begins with a specified model, numerical format, input, and software stack. Keep training and inference measurements separate when reviewing results.

• Workload: Which application and model are being tested?
• Compatibility: Which operations, frameworks, and numerical formats are supported?
• Memory: What data must fit in memory, and how much movement does the workload require?
• System limits: What power, cooling, and response-time conditions apply?
• Evidence: Is the claim a peak hardware specification or a measured end-to-end result?

These are reading questions, not a benchmark ranking. AI-accelerator terminology varies across vendors, so compare documented capabilities instead of treating a label as a universal specification. (Intel: AI accelerators)

For the memory side of the system, continue with HBM and memory bandwidth. For the physical arrangement of dies, read the chiplets guide.