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cs.DC2025
Zeppelin: Balancing Variable-length Workloads in Data Parallel Large Model Training
Chang Chen, Tiancheng Chen, Jiangfei Duan +7
Training large language models (LLMs) with increasingly long and varying sequence lengths introduces severe load imbalance challenges in large-scale data-parallel training. Recent…
cs.LG2025★ 1 cited
MxMoE: Mixed-precision Quantization for MoE with Accuracy and Performance Co-Design
Haojie Duanmu, Xiuhong Li, Zhihang Yuan +4
Mixture-of-Experts (MoE) models face deployment challenges due to their large parameter counts and computational demands. We explore quantization for MoE models and highlight two k…