A surprising discovery reveals that inference can handle drastic reductions in precision, while training struggles significantly when precision is lowered. The transition from double to single precision was seamless, but moving to half precision exposes critical failures due to the large dynamic range required for various operations. This insight highlights the complexities of optimizing deep neural networks on GPUs.
