Everyone’s talking about chips, GPUs, and "compute." But what is actually in the box? Strip away the jargon and an AI computer is three parts doing three jobs. Once you see how they split the work, the whole AI build-out suddenly makes sense.
A modern AI machine is a team. The CPU is the manager that runs the show and makes decisions. The GPU is the workhorse that does the mountain of math. And HBM is the fast memory sitting right beside the GPU, keeping it fed. Tap each one below to see what it does.
Thousands of simple cores that all crunch numbers at the same time. This is where the actual AI math happens.
Why it matters: AI is mostly the same multiplication repeated billions of times. The GPU does those in parallel, so it is hundreds of times faster than a CPU for this job.
A CPU has a few very powerful cores — brilliant for complicated, one-after-another tasks. But AI is the opposite kind of work: the same simple multiplication, repeated billions of times. For that you don’t want a few geniuses; you want thousands of fast hands working in parallel. That’s a GPU. Press the button below and watch the difference.
Same 64 jobs. The CPU grinds through them a handful at a time. The GPU does the lot in one go — that is why AI runs on GPUs.
Training an AI model is mostly one operation — multiplying big grids of numbers (matrices) — done over and over. A GPU is purpose-built to do exactly that, in parallel. That single fact is why NVIDIA, not Intel, became the face of the AI boom.
Here’s the catch. Thousands of GPU cores are useless if they’re sitting around waiting for numbers to arrive. The model — and the data it’s working on — has to be close by and delivered fast. That’s the job of High-Bandwidth Memory (HBM): memory chips stacked into tall towers and glued right next to the GPU, feeding it terabytes of data every second.
HBM is now one of the most fought-over components in tech. A single top-end GPU carries well over a hundred gigabytes of it, and only three companies in the world can make it well — SK Hynix, Micron, and Samsung. When people say AI is "supply constrained," HBM is often what they mean.
A GPU core can do math far faster than memory can deliver the numbers. So the real race in AI hardware is increasingly about feeding the chip, not the chip itself. That’s Part 2 of this series.
Part 1 of the AI Compute 101 series. Next: The Memory Wall — why moving data, not doing math, is the real bottleneck.
For informational purposes only. Not investment advice. Company names are illustrative of who builds each component.
A CPU has a few very powerful cores built for complicated tasks run one after another. A GPU has thousands of simple cores that all do arithmetic at the same time. AI workloads are the same simple multiplication repeated billions of times, so the GPU’s parallelism wins by orders of magnitude.
HBM (high-bandwidth memory) is memory stacked directly beside the GPU die rather than out on the motherboard. It exists because GPU cores burn through numbers faster than ordinary memory can deliver them. Without HBM the cores sit idle, so HBM capacity often caps how much useful work an AI chip can actually do.
Because the bottleneck is throughput, not single-task speed. AI is the same simple operation repeated billions of times. A faster CPU gives you a few faster geniuses; the job needs thousands of fast hands working simultaneously. That is structurally what a GPU is.