Lilian Weng • 9/24/2021

How to Train Really Large Models on Many GPUs?

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This technical article details the challenges of training large neural networks that exceed single GPU memory limits. It explains various parallelism paradigms like data and model parallelism, synchronization methods (BSP, ASP), and memory-saving designs to efficiently distribute training across many GPUs.

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#large language models #Neural Networks #Gpu Memory
How to Train Really Large Models on Many GPUs?

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