Built for graphics, adopted by AI
A GPU (graphics processing unit) was originally designed to render images for games. It excels at parallel processing — running thousands of simple calculations at once — and that turned out to match the shape of AI computation, which is why it is now indispensable to AI development.
Why AI needs one
Training and running a neural network is an enormous number of multiplications and additions repeated over and over. A CPU handles a small number of complex jobs quickly; a GPU handles a mountain of simple ones simultaneously. AI computation is exactly that mountain, so a GPU makes it faster by orders of magnitude.
Training a large model uses data centers with thousands to tens of thousands of GPUs, drawing enormous amounts of electricity. This is why the AI boom keeps intersecting with news about power grids and semiconductor policy.
NVIDIA’s position
NVIDIA holds an overwhelming share of the AI GPU market. Its share price and chip announcements become industry-wide news because it controls a necessity of AI development. Export controls on GPUs have become a diplomatic issue — these chips are now strategic goods.
What it means for individuals
If you only use cloud AI services, you never need to think about GPUs. If you want to run a local LLM or Stable Diffusion on your own machine, the GPU — especially its memory capacity — determines whether it runs at all. The NPUs now shipping in phones and laptops follow the same idea: dedicated hardware for AI computation. Progress in AI has tracked progress in available compute, and the GPU has been at the center of it.
In industry coverage, the number of GPUs a company has secured is treated as a measure of its competitiveness. The race for compute is one of the constant background stories of the field.