A GPU (Graphics Processing Unit) is a processor designed to run thousands of simple calculations in parallel. Originally built for graphics rendering, it has become the reference chip for training and running AI models.
Where a CPU handles a few complex tasks very quickly, a GPU applies the same operation to huge arrays of numbers at once. That is exactly what neural networks need: matrix multiplications at very large scale. The memory on the card (VRAM) matters as much as raw power, because it limits the size of the model you can load.
Nvidia dominates this market thanks to its chips and above all CUDA, its software environment. AMD, Google (with its TPUs) and others offer alternatives. GPUs are rented from hyperscalers or specialized clouds, or bought for in-house servers.
Why it matters when hiring
Knowing how to "use GPUs" says very little. The real signal: a candidate who can explain why a training run saturates memory, how to split a model across several cards, or who has already optimized CUDA code or kernels. These profiles (infra-minded ML engineers, performance engineers) are rare. Do not confuse them with a data scientist running notebooks on a rented GPU.