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Last updated: September 15, 2026 · Information cutoff: September 13, 2026 at 8:00 a.m. ET (9:00 p.m. KST) Quick Take An NPU is a power-efficient accelerator for supported AI inference. It does not make every AI app faster automatically: the app, model, runtime, and driver must target that NPU, and some features may still use the CPU, GPU, or cloud.   NPU app compatibility is a software-and-hardware question, not a chip-label shortcut. A large TOPS number can look like a simple buying shortcut, but it does not tell you whether your video-call effects, photo editor, transcription tool, or local language model will use the hardware. Start with the software you run, then work backward to the processor requirement.   The CPU, GPU, and NPU divide the work The CPU handles varied operating-system and application tasks. The GPU is built for broad parallel work, including graphics and many compute workloads. The NPU is specialized for the math used by neural-network infer...

Quick Take The CPU handles general application logic and flexible workloads. The GPU handles highly parallel graphics and AI workloads. The NPU is a power-conscious accelerator for supported AI models. Last updated: September 5, 2026 An AI PC usually contains three compute engines rather than one “AI chip.” The operating system, app, model format, driver, and execution provider determine where work runs. What the CPU contributes The CPU runs the operating system, coordinates applications, prepares data, and handles code with branching or serial dependencies. Broad software compatibility makes it a dependable fallback. For light inference or an app without accelerator support, the CPU may be practical. Sustained workloads can compete with ordinary application work and use more power. What the GPU contributes The GPU contains many execution resources designed for parallel work. This suits graphics rendering and matrix-heavy AI operations. GPUs remain important for m...