Plain-language explainer
Why AI runs on GPUs
Why are GPUs, and not CPUs, the hardware of the AI boom?
Almost everything a language model does is multiplying enormous grids of numbers, and those millions of little multiply-adds do not depend on each other. A CPU has a handful of fast cores built to race through steps one after another. A GPU has thousands of simple cores built to do the same small operation on huge batches at once. For matrix math, the thousands win. Graphics needed exactly that kind of math first, which is why the gaming chip became the AI chip.
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What people get wrong
- GPUs are just faster computers. On sequential, branching work a CPU wins. GPUs only win when the work is massively parallel.
- AI needs GPUs for graphics. No pixels involved. The shared ingredient is matrix multiplication.
- Stacking more GPUs always means proportionally faster. Chips must exchange results, and that communication becomes its own bottleneck.
Where you see it in real products
- The GPU shortage and Nvidia's market run are demand for this parallel math.
- Cloud providers price AI compute as GPU instances by the hour.
- A consumer gaming card can run a quantized local model, same hardware, new job.
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