GPU
The “accelerator” that makes AI much faster

You have probably heard that “AI needs GPUs” to learn. GPUs were originally for drawing images on a screen—so why are they now an almost indispensable accelerator for AI? Here is a simple way to think about it.

CPU: a brilliant mathematician. GPU: a thousand schoolkids

When people say “the brain of the computer,” they often mean the CPU. So what is different about a GPU?

CPU (central processing unit) is like one brilliant mathematician who can handle hard problems and long logic chains. They solve each task very fast, but they can only focus on one main thing at a time.

By contrast, a GPU (graphics processing unit) is more like an army of a thousand schoolkids who are not each a genius—but for simple math, if everyone works in parallel, they can plow through a huge pile of small jobs at breathtaking speed.

Why does AI need a GPU?

Training and using AI models mostly boils down to an enormous number of simple multiplications and additions (often in big tables called matrices), repeated over and over.

For example, when an AI labels what is in a photo, it may do math on millions of pixels in parallel. Doing that strictly one after another on a CPU is like the single mathematician checking every line by hand—it takes forever. A GPU, with thousands of small cores, splits the work and finishes in a flash.

Hardware that makes a specific kind of work dramatically faster is often called an accelerator—and for many AI workloads, a GPU is exactly that: the engine that makes training and inference practical.