Efficient MoE Inference with Fine-Grained Scheduling of Disaggregated Expert Parallelism
Fuente:
arXiv
Saved in:
| Main Authors: | Pan, Xinglin, Shi, Shaohuai, Lin, Wenxiang, Wang, Yuxin, Tang, Zhenheng, Wang, Wei, Chu, Xiaowen |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
HierMoE: Accelerating MoE Training with Hierarchical Token Deduplication and Expert Swap
by: Lin, Wenxiang, et al.
Published: (2025)
by: Lin, Wenxiang, et al.
Published: (2025)
DreamDDP: Accelerating Data Parallel Distributed LLM Training with Layer-wise Scheduled Partial Synchronization
by: Tang, Zhenheng, et al.
Published: (2025)
by: Tang, Zhenheng, et al.
Published: (2025)
Parm: Efficient Training of Large Sparsely-Activated Models with Dedicated Schedules
by: Pan, Xinglin, et al.
Published: (2024)
by: Pan, Xinglin, et al.
Published: (2024)
Janus: Disaggregating Attention and Experts for Scalable MoE Inference
by: Zhang, Zhexiang, et al.
Published: (2025)
by: Zhang, Zhexiang, et al.
Published: (2025)
ZipCCL: Efficient Lossless Data Compression of Communication Collectives for Accelerating LLM Training
by: Lin, Wenxiang, et al.
Published: (2026)
by: Lin, Wenxiang, et al.
Published: (2026)
Fault-Tolerant Hybrid-Parallel Training at Scale with Reliable and Efficient In-memory Checkpointing
by: Wang, Yuxin, et al.
Published: (2023)
by: Wang, Yuxin, et al.
Published: (2023)
Bandwidth-Aware and Overlap-Weighted Compression for Communication-Efficient Federated Learning
by: Tang, Zichen, et al.
Published: (2024)
by: Tang, Zichen, et al.
Published: (2024)
MemFine: Memory-Aware Fine-Grained Scheduling for MoE Training
by: Zhao, Lu, et al.
Published: (2025)
by: Zhao, Lu, et al.
Published: (2025)
EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference
by: Qian, Yulei, et al.
Published: (2024)
by: Qian, Yulei, et al.
Published: (2024)
OD-MoE: On-Demand Expert Loading for Cacheless Edge-Distributed MoE Inference
by: Wang, Liujianfu, et al.
Published: (2025)
by: Wang, Liujianfu, et al.
Published: (2025)
ExpertFlow: Adaptive Expert Scheduling and Memory Coordination for Efficient MoE Inference
by: Shen, Zixu, et al.
Published: (2025)
by: Shen, Zixu, et al.
Published: (2025)
Surviving Partial Rank Failures in Wide Expert-Parallel MoE Inference
by: Sun, Xun, et al.
Published: (2026)
by: Sun, Xun, et al.
Published: (2026)
Staleness-Centric Optimizations for Parallel Diffusion MoE Inference
by: Luo, Jiajun, et al.
Published: (2024)
by: Luo, Jiajun, et al.
Published: (2024)
GRACE-MoE: Grouping and Replication with Locality-Aware Routing for Efficient Distributed MoE Inference
by: Han, Yu, et al.
Published: (2025)
by: Han, Yu, et al.
Published: (2025)
Hexa-MoE: Efficient and Heterogeneous-aware Training for Mixture-of-Experts
by: Luo, Shuqing, et al.
Published: (2024)
by: Luo, Shuqing, et al.
Published: (2024)
Taming Request Imbalance: SLO-Aware Scheduling for Disaggregated LLM Inference
by: Wang, Qipeng
Published: (2026)
by: Wang, Qipeng
Published: (2026)
Revealing the Challenges of Attention-FFN Disaggregation for Modern MoE Models and Hardware Systems
by: Liu, Guowei, et al.
Published: (2026)
by: Liu, Guowei, et al.
Published: (2026)
Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference
by: Luo, Shuqing, et al.
Published: (2025)
by: Luo, Shuqing, et al.
Published: (2025)
Semantic Parallelism: Redefining Efficient MoE Inference via Model-Data Co-Scheduling
by: Li, Yan, et al.
Published: (2025)
by: Li, Yan, et al.
Published: (2025)
Accelerating Edge Inference for Distributed MoE Models with Latency-Optimized Expert Placement
by: Wu, Tian, et al.
Published: (2025)
by: Wu, Tian, et al.
Published: (2025)
UniEP: Unified Expert-Parallel MoE MegaKernel for LLM Training
by: Zheng, Size, et al.
Published: (2026)
by: Zheng, Size, et al.
Published: (2026)
HybriMoE: Hybrid CPU-GPU Scheduling and Cache Management for Efficient MoE Inference
by: Zhong, Shuzhang, et al.
Published: (2025)
by: Zhong, Shuzhang, et al.
Published: (2025)
MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model?
by: Ma, Songkai, et al.
Published: (2025)
by: Ma, Songkai, et al.
Published: (2025)
FedImpro: Measuring and Improving Client Update in Federated Learning
by: Tang, Zhenheng, et al.
Published: (2024)
by: Tang, Zhenheng, et al.
Published: (2024)
A Scheduling Framework for Efficient MoE Inference on Edge GPU-NDP Systems
by: Wu, Qi, et al.
Published: (2026)
by: Wu, Qi, et al.
Published: (2026)
DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism
by: Zeng, Zhichen, et al.
Published: (2026)
by: Zeng, Zhichen, et al.
Published: (2026)
HarMoEny: Efficient Multi-GPU Inference of MoE Models
by: Doucet, Zachary, et al.
Published: (2025)
by: Doucet, Zachary, et al.
Published: (2025)
Expert-as-a-Service: Towards Efficient, Scalable, and Robust Large-scale MoE Serving
by: Liu, Ziming, et al.
Published: (2025)
by: Liu, Ziming, et al.
Published: (2025)
Accelerating Distributed MoE Training and Inference with Lina
by: Li, Jiamin, et al.
Published: (2022)
by: Li, Jiamin, et al.
Published: (2022)
D$^{2}$MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving
by: Wang, Haodong, et al.
Published: (2025)
by: Wang, Haodong, et al.
Published: (2025)
Arrow: Adaptive Scheduling Mechanisms for Disaggregated LLM Inference Architecture
by: Wu, Yu, et al.
Published: (2025)
by: Wu, Yu, et al.
Published: (2025)
MoEntwine: Unleashing the Potential of Wafer-scale Chips for Large-scale Expert Parallel Inference
by: Tang, Xinru, et al.
Published: (2025)
by: Tang, Xinru, et al.
Published: (2025)
MPipeMoE: Memory Efficient MoE for Pre-trained Models with Adaptive Pipeline Parallelism
by: Zhang, Zheng, et al.
Published: (2025)
by: Zhang, Zheng, et al.
Published: (2025)
FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression
by: Tang, Zhenheng, et al.
Published: (2024)
by: Tang, Zhenheng, et al.
Published: (2024)
HOBBIT: A Mixed Precision Expert Offloading System for Fast MoE Inference
by: Tang, Peng, et al.
Published: (2024)
by: Tang, Peng, et al.
Published: (2024)
SP-MoE: Speculative Decoding and Prefetching for Accelerating MoE-based Model Inference
by: Chen, Liangkun, et al.
Published: (2025)
by: Chen, Liangkun, et al.
Published: (2025)
Efficient CPU-GPU Collaborative Inference for MoE-based LLMs on Memory-Limited Systems
by: Huang, En-Ming, et al.
Published: (2025)
by: Huang, En-Ming, et al.
Published: (2025)
Multi-Layer Scheduling for MoE-Based LLM Reasoning
by: Sun, Yifan, et al.
Published: (2026)
by: Sun, Yifan, et al.
Published: (2026)
HAP: Hybrid Adaptive Parallelism for Efficient Mixture-of-Experts Inference
by: Lin, Haoran, et al.
Published: (2025)
by: Lin, Haoran, et al.
Published: (2025)
DuoServe-MoE: Dual-Phase Expert Prefetch and Caching for LLM Inference QoS Assurance
by: Zhang, Yuning, et al.
Published: (2025)
by: Zhang, Yuning, et al.
Published: (2025)
Similar Items
-
HierMoE: Accelerating MoE Training with Hierarchical Token Deduplication and Expert Swap
by: Lin, Wenxiang, et al.
Published: (2025) -
DreamDDP: Accelerating Data Parallel Distributed LLM Training with Layer-wise Scheduled Partial Synchronization
by: Tang, Zhenheng, et al.
Published: (2025) -
Parm: Efficient Training of Large Sparsely-Activated Models with Dedicated Schedules
by: Pan, Xinglin, et al.
Published: (2024) -
Janus: Disaggregating Attention and Experts for Scalable MoE Inference
by: Zhang, Zhexiang, et al.
Published: (2025) -
ZipCCL: Efficient Lossless Data Compression of Communication Collectives for Accelerating LLM Training
by: Lin, Wenxiang, et al.
Published: (2026)