This is a first in the industry to achieve such scaling efficiencies for up to 11B parameter models using Kubernetes with vanilla Ethernet and PyTorch native FSDP API’s. This improvement enables users to train huge models on a Hybrid Cloud platform in a cost efficient and sustainable manner.
We plan on continuing to investigate scaling with decoder only models and increasing the size of these models to 100B+ parameters. From a system design perspective, we are exploring capabilities such as RoCE and GDR that can improve latencies of communications over Ethernet networks.
Acknowledgements
This blog was possible because of contributions from both PyTorch Distributed and IBM Research teams.
From the PyTorch Distributed team, we would like to thank Less Wright, Hamid Shojanazeri, Geeta Chauhan, Shen Li, Rohan Varma, Yanli Zhao, Andrew Gu, Anjali Sridhar, Chien-Chin Huang, and Bernard Nguyen.