For training, the floating-point formats FP16 and FP32 are commonly used as they have high enough accuracy, and no hyper-parameters. They mostly work out of the box, making them easy to use.

Floating-point arithmetic is a cornerstone of modern computational science, providing an efficient means to approximate real numbers within a finite precision framework.