
layout: blog_detail
title: 'Introducing native PyTorch automatic mixed precision for faster training on NVIDIA GPUs'
author: Mengdi Huang, Chetan Tekur, Michael Carilli

Most deep learning frameworks, including PyTorch, train with 32-bit floating point (FP32) arithmetic by default. However this is not essential to achieve full accuracy for many deep learning models. In 2017, NVIDIA researchers developed a methodology for mixed-precision training, which combined single-precision (FP32) with half-precision (e.g. FP16) format when training a network, and achieved the same accuracy as FP32 training using the same hyperparameters, with additional performance benefits on NVIDIA GPUs:

Shorter training time;
Lower memory requirements, enabling larger batch sizes, larger models, or larger inputs.
