Simulating Unified Tensor Resharding in heterogeneous AI systems (opens in new tab)

State-of-the-art AI training simulators assume homogeneous compute and network infrastructure. However, real-world training infrastructure is becoming increasingly heterogeneous since: (a) Model architectures such as multimodal and MoE exploit heterogeneity to improve device utilization, (b) Public cloud platforms often provide limited availability of homogeneous hardware due to fast hardware evolution, and (c) Large enterprises frequently deplo...