Optimizing Memory Usage for Training LLMs and Vision Transformers in PyTorch

Peak GPU memory is a major constraint in training large deep-learning models, and the article demonstrates how cumulative PyTorch techniques can reduce memory use by roughly 20 times while maintaining model accuracy. Using a Vision Transformer finetuning example and the Lightning Fabric library, it examines mixed and bfloat16 precision, smaller batch sizes, gradient accumulation, leaner optimizers, direct model initialization on the target device, Fully Sharded Data Parallelism across GPUs, activation checkpointing, and CPU parameter offloading. Mixed precision substantially lowers memory while improving runtime, whereas microbatching, checkpointing, and offloading trade additional computation time for further savings. The methods are presented as broadly applicable to convolutional networks, transformers, and language models, with the combined optimizations enabling BigBird and DistilBERT classification training on relatively limited GPU memory, including BigBird at about 4 GB of peak GPU RAM.

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