AI Glossary
GPU
A processor designed to perform many calculations simultaneously, making it useful for AI and machine learning workloads.
GPU
Overview
Training modern AI systems requires enormous computing power.
Large AI models may perform billions or even trillions of calculations while learning from data.
This is where GPUs become important.
GPU stands for Graphics Processing Unit.
Although GPUs were originally developed for rendering graphics and video games, they are particularly effective at performing large numbers of calculations simultaneously.
A helpful way to think about a GPU is a team of workers.
Rather than assigning one person to complete every task sequentially, many workers perform tasks at the same time.
This parallel processing capability makes GPUs highly effective for machine learning and deep learning workloads.
Many of today’s most advanced AI systems depend heavily on GPUs during both training and inference.
As AI adoption continues to grow, GPUs remain one of the most important pieces of modern AI infrastructure.
Why It Matters
GPUs provide the computing power required to train and operate many AI systems.
Real-World Example
An organization may use hundreds or thousands of GPUs to train a large language model.