Why GPUs Dominate Neural Network Training
A GPU is a freight train, a CPU a race car: deep learning moves identical math across huge batches. GPUs win on transformers and CNNs. The footgun is using them for tiny models, where data transfer overhead eats the gains.
A GPU is a freight train to the CPU's race car: it hauls thousands of identical math operations in parallel, which is exactly what neural network training demands. You see the gap when fitting transformers or CNNs, where matrix multiplications repeat across massive batches and every core stays busy. The footgun is defaulting to a GPU for tiny models, sparse tasks, or small batches, where PCIe data transfer and kernel launch overhead erase the speed advantage and leave silicon idle.
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