12 citations · 12 across the 8 of their papers we have counts for
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cs.DC2025
Efficient MoE Inference with Fine-Grained Scheduling of Disaggregated Expert Parallelism
Xinglin Pan, Shaohuai Shi, Wenxiang Lin +4
The mixture-of-experts (MoE) architecture scales model size with sublinear computational increase but suffers from memory-intensive inference due to KV caches and sparse expert act…
cs.DC2025
HierMoE: Accelerating MoE Training with Hierarchical Token Deduplication and Expert Swap
Wenxiang Lin, Xinglin Pan, Lin Zhang +3
The sparsely activated mixture-of-experts (MoE) transformer has become a common architecture for large language models (LLMs) due to its sparsity, which requires fewer computationa…
cs.LG2025★ 12 cited
FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models
Xinglin Pan, Wenxiang Lin, Lin Zhang +5
Recent large language models (LLMs) have tended to leverage sparsity to reduce computations, employing the sparsely activated mixture-of-experts (MoE) technique. MoE introduces fou…