Dr Ashish Bamania · Distributed Data Parallel

Distributed Data Parallel Explained

A free, diagram-led walk through PyTorch DDP in eleven steps: replicating the model per GPU, splitting the dataset with DistributedSampler, the forward and backward passes, and gradient synchronisation through AllReduce, plus the optimisation overlapping gradient computation with communication. A worked three-GPU example carries it. It closes on the limit that motivates model parallelism - an 8B Llama needs roughly 151GB, more than an 80GB H100 holds. Billed as Part 1, with the code in Part 2.