What is a Supercomputer for AI? How GPUs Drive Machine Learning

GPUs became the dominant hardware for AI because they excel at parallel mathematical operations and can hold large model weights in high-bandwidth memory — capabilities originally developed for video game graphics rendering. CPUs, by contrast, are general-purpose chips optimized for varied sequential tasks with less dedicated memory. The post explains the architectural differences between CPUs and GPUs (compute, cache, control, memory), why those differences matter for training and running LLMs, and when you actually need a GPU versus when a CPU can suffice — depending on model size, workload type, and whether the application is personal or customer-facing.