Definition
GPUs excel at many small matrix operations in parallel, exactly what neural network training requires. CPUs handle sequential logic well; GPUs crunch thousands of similar calculations at once.
NVIDIA CUDA is the dominant software stack for AI GPUs; cloud providers rent A100, H100, and similar chips by the hour.
In simple terms
A CPU is one very fast chef cooking dishes one after another. A GPU is a kitchen with hundreds of cooks each doing the same step on different ingredients simultaneously.
Where you see it
- Training LLMs requires clusters of high-end GPUs.
- Local developers use consumer GPUs (RTX series) for smaller fine-tunes.
- Inference servers batch requests on GPU for lower latency per token.
How it works
1.Load model weights
Copy parameters into GPU memory (VRAM).
2.Parallel forward/backward
CUDA kernels run matrix multiplies across cores.
3.Optimize batch size
Balance throughput against memory limits.
Why it matters
- GPU access determines what AI work you can do locally vs. in the cloud.
Often confused
You need a GPU to use AI.
Inference can run on CPU (slower); training large models practically requires GPUs.