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What is a GPU? Why does AI need GPUs?

Graphics cards — hardware that accelerates parallel computation, the backbone of every modern AI model.

Updated: May 5, 2026 · 2 min read

A GPU (Graphics Processing Unit) was originally built to render 3D games, but its architecture turned out to be a perfect fit for training and running AI. That’s why Nvidia became the most important tech company of the 2020s.

Why is the GPU a fit for AI?

CPUs are designed to run a few complex tasks quickly and sequentially. GPUs are designed to run thousands of simple tasks in parallel.

Neural networks boil down to millions of simple matrix multiplications — exactly the GPU’s “specialty.”

CPU:  4-32 powerful cores, runs complex tasks
GPU:  10,000+ weaker cores, runs them simultaneously

Training an LLM on a CPU: months. On a GPU cluster: weeks.

Popular AI GPUs (2026)

GPU	Memory	Estimated price	Purpose
Nvidia H200	141GB	~$30k-40k	Data-center training + inference
Nvidia B200 (Blackwell)	192GB	~$50k+	Frontier model training
Nvidia A100	40-80GB	~$8k-15k	Older generation, still common
Nvidia RTX 4090	24GB	~$1.6k	Local inference, hobbyists
AMD MI300X	192GB	~$15k	H100 rival, gaining traction
Apple M4 Max	unified 128GB	inside Macs	Local inference for developers

Training vs inference

Training: needs the most powerful GPUs and lots of memory; runs for weeks to months. Expensive.
Inference: running an already-trained model to answer users. Cheaper per run, but must scale with user traffic.

Training: H100/B200 cluster. Inference: could be H100s, or cheaper cards (L4, T4), or even an Apple M-chip for local LLMs.

Why does Nvidia “dominate”?

CUDA — proprietary software stack; most AI frameworks (PyTorch, TF) optimized for CUDA first
Deep library ecosystem (cuDNN, NCCL, TensorRT)
Networking (NVLink) lets you connect many GPUs into a large cluster
AMD and Intel are catching up but still far behind

Do end users need to know about GPUs?

Using ChatGPT or Claude through web/app: NO. The provider handles everything.
Running LLMs locally (Ollama, LM Studio): YES. At minimum an RTX 3060 12GB; ideally an RTX 4090 or Mac M-series.
Building AI products: a basic understanding helps you estimate inference costs.