Why does AI need so many GPUs, and what is the difference between training and inference?

AI needs so many GPUs because an AI model is, underneath, an enormous pile of simple arithmetic, and a GPU is a chip built to do huge numbers of calculations at the same time. A graphics processing unit was designed to draw pictures, and the parts of a picture can mostly be worked out independently of one another. So a modern GPU packs hundreds or thousands of small calculation units that all work side by side.

That design turned out to fit AI as well as it fits video games. GPUs sit in graphics cards, phones and game consoles, yet in the 2020s they have increasingly been used to train neural networks on the enormous datasets AI needs. Many modern GPUs even carry cores specialised for deep learning.

Two different jobs use these chips. Training is when a model is built: a GPT-style model is first pretrained to predict the next word from a large amount of data, then fine-tuned. Inference is when you use the finished model: you give it an input and it hands back text, images or code. By one estimate, training costs more computing per token than inference does.

Because the chips are so power-hungry and numerous, they are packed into specialised AI data centers. When you chat with an AI on your phone, hosted inference means the work is done in a remote data center and the answer travels back over the Internet. As of 2026, one encyclopedia article estimated that major tech companies would spend about $650 billion on such buildings that year.