Similar performance charts can be seen in 3D medical image analysis, gaze estimation, video synthesis, conditional GANs, and convolutional LSTMs. Huang et al. showed that mixed precision training is 1.5x to 5.5x faster over float32 on V100 GPUs, and an additional 1.3x to 2.5x faster on A100 GPUs on a variety of networks. On very large networks the need for mixed precision is even more evident. Narayanan et al. reports that it would take 34 days to train GPT-3 175B on 1024 A100 GPUs (with a batch size of 1536), but it’s estimated it would take over a year using float32!