GPUs (Graphics Processing Units)
A GPU (Graphics Processing Unit) is a specialized processor designed to handle many calculations at the same time (parallel processing).
It was originally created to render graphics, images, and video quickly
Why do computers even have GPUs?
Computers have GPUs because not all tasks are the same.
- CPU (Central Processing Unit) → handles general tasks, one at a time (or few at a time)
- GPU → handles thousands of smaller tasks simultaneously
This makes GPUs perfect for workloads that involve repeating the same math over and over across large data.
Originally, this was used for:
- video games
- 3D rendering
- image processing
But now it’s way bigger than that…
Why AI needs GPUs
Artificial Intelligence models (especially deep learning) rely on massive matrix calculations.
These include:
- multiplying huge arrays of numbers
- updating millions/billions of parameters
- training across massive datasets
AI = lots of repeated math at scale
GPUs = built exactly for lots of repeated math at once ⚡
That’s why GPUs make AI:
- faster
- cheaper (long-term)
- scalable
Without GPUs, training modern AI models would take weeks, months, or even years.
Simple analogy
- CPU = one very smart chef cooking meals one by one
- GPU = a whole kitchen full of chefs cooking everything at once
Important note (it depends)
You don’t always need a GPU on your own device.
If you run AI using cloud services (like OpenAI, Google Cloud, etc.):
- the GPU is on their servers, not your computer ☁️
- your device just sends requests and receives results
So:
- your computer may not need a GPU at all
- but the AI is still running on GPUs somewhere in the background