Why GPUs Power AI

Why GPUs Power AI

If you have followed AI news at all, you have probably heard about GPUs. Companies are buying them by the thousands, and there is a global shortage. But why a graphics card? What does drawing video game graphics have to do with artificial intelligence?

The answer comes down to a fundamental difference in how CPUs and GPUs work. A CPU, or Central Processing Unit, is designed to handle a wide variety of tasks quickly, one after another. It has a few very powerful cores that can each handle complex logic, branch prediction, and all the other things a general purpose computer needs to do. It is like a few expert chefs who can cook any dish you ask for.

A GPU, or Graphics Processing Unit, takes the opposite approach. Instead of a few powerful cores, it has thousands of simpler cores. Each core is weaker than a CPU core, but they all work in parallel. A GPU is like a thousand line cooks, each doing one simple task over and over. They cannot make a complex sauce, but they can chop a thousand onions at the same time.

Neural networks are all about doing the same simple operation millions of times. Multiply two numbers, add the result to a running total, repeat. This is matrix multiplication, and it is the bread and butter of AI. A CPU can do matrix multiplication, but it does it step by step. A GPU does thousands of multiplications simultaneously, making it orders of magnitude faster.

This is why the AI boom is so closely tied to GPU companies like NVIDIA. They designed their chips for gaming and graphics rendering, which also requires massive parallel computation. It turned out that the same hardware that makes video games look beautiful is also perfect for training and running neural networks. Modern AI would simply not exist without the parallel processing power of GPUs.