149 citations · 156 across the 7 of their papers we have counts for
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cs.DC2026
ReaLB: Real-Time Load Balancing for Multimodal MoE Inference
Yingping Wang, Yi Wu, Xiangyu Wu +4
Mixture-of-Experts (MoE) architectures are widely used in modern large language models and multimodal models. However, inference efficiency is often limited by highly dynamic and s…
cs.DC2025
Accelerating Mixture-of-Experts Inference by Hiding Offloading Latency with Speculative Decoding
Zhibin Wang, Zhonghui Zhang, Yuhang Zhou +8
Recent advancements in Mixture of Experts (MoE) models have significantly increased their parameter scale as well as model performance. Extensive offloading techniques have been pr…
cs.DC2024★ 5 cited
MoC-System: Efficient Fault Tolerance for Sparse Mixture-of-Experts Model Training
Weilin Cai, Le Qin, Jiayi Huang
As large language models continue to scale up, distributed training systems have expanded beyond 10k nodes, intensifying the importance of fault tolerance. Checkpoint has emerged a…