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cs.DC2025
H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips
Ding Tang, Jiecheng Zhou, Jiakai Hu +5
Recent advancements in large language models (LLMs) necessitate extensive computational resources, prompting the use of diverse hardware accelerators from multiple vendors. However…
cs.LG2025★ 1 cited
Towards Efficient Pre-training: Exploring FP4 Precision in Large Language Models
Jiecheng Zhou, Ding Tang, Rong Fu +8
The burgeoning computational demands for training large language models (LLMs) necessitate efficient methods, including quantized training, which leverages low-bit arithmetic opera…