GPUs, TPUs, and NPUs all accelerate AI workloads, but they’re genuinely different pieces of hardware, optimized for different points on the training-versus-inference and flexibility-versus-efficiency spectrum.
GPU: Flexible, General-Purpose Parallel Compute
Originally built for rendering graphics (massively parallel matrix math), GPUs turned out to be excellently suited to the equally matrix-math-heavy demands of neural network training and inference. Their genuine strength is flexibility — the same hardware handles training, inference, and a huge range of non-AI parallel workloads, with a mature, extensive software ecosystem (CUDA) built up over nearly two decades.
TPU: Google’s AI-Specific Accelerator
Tensor Processing Units are custom silicon designed by Google specifically and exclusively for neural network operations, particularly the systolic-array matrix multiplication pattern common in deep learning — genuinely more efficient per watt for that narrow workload than a general-purpose GPU, at the cost of far less flexibility for anything outside that specific domain.
NPU: Low-Power, On-Device Inference
Neural Processing Units, now common in phones and modern laptops (Copilot+ PC requirements), are optimized specifically for efficient, low-power inference of already-trained models directly on-device — genuinely not designed for training at all, but excellent at running a compact model with minimal battery drain, which is exactly the use case they’re built for.
The general pattern: GPUs for flexibility across both training and inference, TPUs for Google’s own massive-scale training and inference efficiency, NPUs for efficient on-device inference specifically — each genuinely optimized for a different, real point on the trade-off spectrum, not simply “better” or “worse” than the others in the abstract.