Feb. 12, 2024, 5:43 a.m. | Bowen Tan Yun Zhu Lijuan Liu Hongyi Wang Yonghao Zhuang Jindong Chen Eric Xing Zhiting Hu

cs.LG updates on arXiv.org arxiv.org

The recent progress of AI can be largely attributed to large language models (LLMs). However, their escalating memory requirements introduce challenges for machine learning (ML) researchers and engineers. Addressing this requires developers to partition a large model to distribute it across multiple GPUs or TPUs. This necessitates considerable coding and intricate configuration efforts with existing model parallel tools, such as Megatron-LM, DeepSpeed, and Alpa. These tools require users' expertise in machine learning systems (MLSys), creating a bottleneck in LLM development, …

automate challenges cs.lg developers distributed engineers gpu gpus language language models large language large language models llms machine machine learning memory multiple progress requirements researchers tool tpus training

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