AI Practitioner

In 2020, Microsoft researchers introduced ZeRO (Zero Redundancy Optimizer) via their paper "ZeRO: Memory Optimization Towards Training Trillion Parameter Models" (arXiv:1910.02054). ZeRO is a memory optimization technique that eliminates redundancy in distributed training, enabling efficient scaling to trillion-parameter models. This provides an in-depth technical breakdown of ZeRO's partitioning strategies, memory usage analysis, and integration with DeepSpeed. 1. What is ZeRO? ZeRO eliminates redundant memory copies of model states across GPUs. Instead of replicating parameters, gradients, and optimizer states across each GPU, ZeRO partitions them across all devices. This results in near-linear memory savings as the number of GPUs increases. 2. Limitations of Traditional Data Parallelism In standard data-parallel training, every GPU maintains: Model Parameters $\theta$ Gradients $\nabla \theta$ Optimizer States $O(\theta)$ This causes memory usage ...