What Is a GPU?

A GPU, or graphics processing unit, is a specialized chip designed to perform many calculations at once. Originally built to render graphics, GPUs turned out to be well suited to the heavy parallel math behind AI, which is why they power most training and much of the day-to-day running of modern models.

How a GPU works

A GPU works by doing many simple calculations in parallel rather than a few complex ones in sequence. Where a CPU has a small number of powerful cores aimed at general tasks, a GPU has thousands of smaller cores that handle the same operation across large batches of numbers at the same time.

AI relies heavily on exactly this kind of math. Training and running a neural network means multiplying large grids of numbers over and over, and a GPU can do that far faster than a CPU. This speed is why access to GPUs has become a central concern in AI, shaping cost, availability, and how quickly models can be built.

GPU vs CPU

The difference is many small tasks versus a few large ones. A CPU handles general work in sequence with a few strong cores; a GPU handles massive parallel math with thousands of smaller cores.

Why GPUs matter for AI

GPUs set much of the cost and speed of building and running AI. The parallel math that GPUs accelerate is the core work of deep learning, so the amount of GPU power available often determines how large a model can be trained and how quickly it can respond in production. Their scarcity and expense are a large part of why advanced AI is costly to develop, and why efficiency techniques that reduce GPU use are actively pursued.