The architectural differences between Central Processing Units (CPUs) and Graphics Processing Units (GPUs) explain why GPUs dominate deep learning computation. A CPU features a small number of highly powerful, complex cores designed for general-purpose computing, dedicating significant silicon area to sophisticated control flow like branch prediction. This makes them ideal for executing sequential code but less efficient for massive parallelism. In contrast, a GPU comprises thousands of simpler, weaker cores running at lower clock frequencies. Because power consumption grows quadratically with clock speed, using many slower cores is vastly more energy-efficient than a few fast ones. This high-throughput parallel design, combined with exceptionally wide memory buses, allows GPUs to process the massive matrix multiplications required in deep neural networks orders of magnitude faster than CPUs.