GPUs, explained without the datasheet

CPUs use a small number of powerful cores for varied sequential tasks, while GPUs contain thousands of simpler cores designed to perform identical calculations in parallel, making them especially suited to the matrix operations underlying AI. GPUs substantially accelerate both model training, which requires repeated processing of vast datasets, and inference, where large models must generate fast responses for many simultaneous users. For AI workloads, VRAM determines whether a model can fit on a GPU, while memory bandwidth and throughput affect performance once it does. NVIDIA dominates the AI GPU market through its hardware and CUDA software ecosystem, although TSMC manufactures its chips and AMD and Intel offer alternatives. The key capacity-planning advice is to evaluate memory requirements before raw speed, since a high-performance GPU cannot run a model that exceeds its available VRAM.

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