AIBullisharXiv – CS AI · Apr 147/10
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Deep Optimizer States: Towards Scalable Training of Transformer Models Using Interleaved Offloading
Researchers introduce Deep Optimizer States, a technique that reduces GPU memory constraints during large language model training by dynamically offloading optimizer state between host and GPU memory during computation cycles. The method achieves 2.5× faster iterations compared to existing approaches by better managing the memory fluctuations inherent in transformer training pipelines.