In what follows, we demonstrate how FSDP allows us to keep hundreds of GPUs highly utilized throughout a distributed training job, while running over standard Ethernet networking (system description towards the end of the blog). We chose the T5 architecture for our experiments and leveraged the code from the FSDP workshop. In each of our experiments, we start with a single node experiment to create a baseline and report the metric seconds/iteration normalized by the batch size as well as compute the teraflops based on the Megatron-LM paper (see Appendix for details of teraflop computation for T5). Our experiments aim to maximize the batch size (while avoiding cudaMalloc retries) to take full advantage of overlap in computation and communications, as discussed below. Scaling is defined as the ratio of the seconds/iteration normalized by batch size for N nodes versus a single node, representing how well we can utilize the additional GPUs as more nodes are added.