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BaGuaLu-2: Taming Dynamic Memory Fluctuation for Efficient MoE Model Training over 10,000 GPUs
Fine-grained Mixture-of-Experts (MoE) is the de facto architecture for scaling large language models. However, dynamic expert load imbalance causes severe memory fluctuations that degrade training efficiency. Existing systems rely on static memory provisioning: they either over-provision, leaving GP
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