5 citations · 5 across the 8 of their papers we have counts for
4 papers · 1 filter
xHC: Expanded Hyper-Connections
Xiangdong Zhang, Xiaohan Qin, Sunan Zou +10
Hyper-Connections (HC) expand the residual stream of Transformers into parallel streams, providing a form of memory scaling beyond model width and depth. Manifold-Constrained H…
UltraEP: Unleash MoE Training and Inference on Rack-Scale Nodes with Near-Optimal Load Balancing
Xinming Wei, Chao Jin, Tuo Dai +10
Large-scale expert parallelism (EP) is becoming pivotal for training and serving frontier MoE models, but it also amplifies device-level expert load imbalance into compute straggle…
BigMac: Breaking the Pareto Frontier of Compute and Memory in Multimodal LLM Training
Zili Zhang, Chengxu Yang, Shenglong Zhang +8
Training multimodal large language models (MLLMs) is challenged by both model and data heterogeneity. Existing systems redesign the training pipeline to address these challenges, b…
PROBE: Co-Balancing Computation and Communication in MoE Inference via Real-Time Predictive Prefetching
Qianchao Zhu, Xucheng Ye, Yuliang Liu +2
Mixture-of-Experts models have become a dominant architecture for scaling Large Language Models by activating only a sparse subset of experts per token. However, latency-critical M…