Mixed precision training is using lower-precision formats (FP16, BF16) for most operations and higher precision (FP32) for critical accumulations, cutting memory and compute roughly in half with minimal accuracy loss. This technique is essential for training large models efficiently on modern GPUs that have dedicated hardware for low-precision arithmetic.