What is GPU Computing?

A CPU is optimized for executing a sequence of complex instructions quickly, one after another (with some parallelism across a handful of cores). A GPU takes the opposite approach: thousands of simpler cores that can execute the same operation across huge amounts of data simultaneously. That architecture happens to match almost exactly what neural networks need, since both training and running inference on a model is fundamentally a huge number of matrix multiplications applied in parallel — which is why GPUs (and increasingly specialized AI accelerators like TPUs) became the backbone of the modern AI boom rather than CPUs. GPU availability and cost are now a real business constraint: training a large frontier model requires enormous GPU clusters running for weeks, and even serving inference for a popular AI product at scale requires provisioning enough GPU capacity to handle concurrent requests without unacceptable latency. This is part of why so many AI products are built on top of hosted foundation models via an API rather than running their own GPU infrastructure — the GPU cost and complexity of training and serving a frontier-scale model from scratch is out of reach for all but the largest labs.