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Existing general purpose frameworks for gigantic model training, i.e., densemodels with billions of parameters, cannot scale efficiently on cloudenvironment with various networking conditions due to large communicationoverheads. In this paper, we propose MiCS, which Minimizes the CommunicationScale to bring down communication overhead. Specifically, by decreasing thenumber of participants in a communication collective, MiCS can utilizeheterogeneous network bandwidth, reduce network traffic over slower links,reduce the latency of communications for maintaining high network bandwidthutilization, and amortize expensive global gradient synchronization overhead.Our evaluation on AWS shows that the system throughput of MiCS is up to2.89$\times$ that of the state-of-the-art large model training systems. MiCSachieves near-linear scaling efficiency, which is up to 1.27$\times$ that ofDeepSpeed. MiCS allows us to train a proprietary model with 100 billionparameters on 512 GPUs with 99.4% weak-scaling efficiency, and it is able tosaturate over 54.5% theoretical computation power of each GPU on a public cloudwith less GPU memory and more restricted networks than DGX-A100 clusters.

MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud