Sebastian Raschka • 7/1/2023

Optimizing Memory Usage for Training LLMs and Vision Transformers in PyTorch

This technical article details nine cumulative methods to optimize memory consumption in PyTorch for training large models like vision transformers and LLMs. It covers techniques including mixed-precision training, gradient accumulation, leaner optimizers, distributed training, and parameter offloading, demonstrating their application with code examples to achieve up to 20x memory reduction.

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#memory optimization #Transformers #Deep Learning