GPUs & AI accelerators
Chips built to do enormous numbers of simple multiplications in parallel, the core operation of neural networks.
D1 · StoryFor young children and total beginners
A normal computer brain (a CPU) is like one very clever person doing sums one after another. A GPU is like a stadium full of people each doing one small sum at the same time.
AI needs billions of small sums, so the stadium wins.
Like: One clever person versus a stadium full of helpers.
D2 · PictureFor curious beginners
Neural networks mostly multiply large grids of numbers (matrices). GPUs and other accelerators contain thousands of simple cores and specialised matrix units that do these multiplications in parallel.
They also use lower-precision number formats (16-, 8- or even 4-bit) to fit more maths per second and more numbers in memory.
Each chip maker's software platform lets machine-learning frameworks use the hardware without chip-level code.
Like: A CPU is a few expert chefs; a GPU is a huge team of line cooks all chopping at once.
Where it breaks: Line cooks work independently. GPU cores run the same instruction on different data in lockstep, so work that can't be split evenly wastes most of the team.
D5 · FrontierFor experts
Frontier questions: specialised inference chips versus general-purpose GPUs, how far low-precision formats can go without hurting quality, and whether memory bandwidth rather than arithmetic becomes the permanent bottleneck.
Examples as of 2026-10-13
- NVIDIA GPUs, programmed with CUDA
- AMD GPUs, programmed with ROCm
- Google TPUs
- PyTorch (a framework that runs on all of these)