What is mixed precision training and why is it important in large vision models?

Mixed precision training uses FP16 and FP32 together to speed up training and reduce memory usage.

Mixed precision training stores activations in FP16 to reduce memory and accelerate computation on GPUs with tensor cores, while keeping critical operations like loss scaling and gradients in FP32 for stability. This allows training larger vision models with higher batch sizes.