Figure 1 plots the throughput for each model configuration and optimization, both with a local batch size of 8 and then with the maximum batch size possible on 1 node. Absence of a data point for a model variant for an optimization indicates that the model could not be trained on a single node.
Figure 2 plots the maximum possible batch size per worker for each optimization. We observe a few things:

Scaling model size: DDP is only able to fit the 350M and 900M model on a node. With FSDP, due to memory savings, we are able to train ~3x bigger models compared to DDP  (i.e. the 1.8B and 2.7B variants). Combining activation checkpointing (AC) with FSDP enables training even bigger models, on the order of ~10x compared to DDP (i.e. 4.8B and 10B variants)
Throughput:
