Gradient accumulation is a critical technique for training large models on limited GPU memory, but its effective use requires careful design of micro-batches and understanding of distributed synchronization. This article breaks down the concepts of effective batch size, memory boundaries, and the trade-offs between gradient accumulation and data parallelism. It provides practical strategies for optimizing training throughput while maintaining model convergence. The content is particularly valuable for engineers working with large language models or computer vision models on multi-GPU setups. By mastering these techniques, teams can reduce hardware costs and accelerate experimentation cycles.
A detailed guide on gradient accumulation and micro-batch design for distributed training, covering effective batch size, memory boundaries, and synchronization.