
Maximize the Nvidia TensorCore utilization by keeping matrix dimensions to be multiple of 8. For More details check Nvidia Tensor Core Performance Guide.

4. Results
In this section, we show the scaling results of ViT on three types of tasks: (1) image classification, (2) object detection (3) video understanding. Our key result is that we are able to train massive ViT backbones across these vision tasks after applying the discussed scaling and optimization techniques. This enables vision research at a much larger scale. We trained the models to convergence to verify that we maintain the current baselines even with all the optimizations. A common trend in Figures 2, 3, 4 is that we are able to train up to 25B-param models with an epoch time of less than 4 hours on 128 A100 GPUs. The 60B and 120B models are relatively slower to train.