Writing an LLM from scratch, part 32k -- Interventions: training a better model locally with gradient accumulation

The author explains gradient accumulation techniques to match cloud training batch sizes locally. By accumulating gradients over multiple forward-backward passes before optimizer updates, they achieve the stabilization benefits of larger batches without requiring more GPU memory. This allows local training with effective batch sizes comparable to cloud setups.