SiDA-MoE: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models
Fuente:
arXiv
Saved in:
| Main Authors: | Du, Zhixu, Li, Shiyu, Wu, Yuhao, Jiang, Xiangyu, Sun, Jingwei, Zheng, Qilin, Wu, Yongkai, Li, Ang, Li, Hai "Helen", Chen, Yiran |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
DualSparse-MoE: Coordinating Tensor/Neuron-Level Sparsity with Expert Partition and Reconstruction
by: Cai, Weilin, et al.
Published: (2025)
by: Cai, Weilin, 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)
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)
Janus: Disaggregating Attention and Experts for Scalable MoE Inference
by: Zhang, Zhexiang, et al.
Published: (2025)
by: Zhang, Zhexiang, 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)
MoEless: Efficient MoE LLM Serving via Serverless Computing
by: Yu, Hanfei, et al.
Published: (2026)
by: Yu, Hanfei, et al.
Published: (2026)
LAER-MoE: Load-Adaptive Expert Re-layout for Efficient Mixture-of-Experts Training
by: Liu, Xinyi, et al.
Published: (2026)
by: Liu, Xinyi, et al.
Published: (2026)
EC2MoE: Adaptive End-Cloud Pipeline Collaboration Enabling Scalable Mixture-of-Experts Inference
by: Yang, Zheming, et al.
Published: (2025)
by: Yang, Zheming, et al.
Published: (2025)
X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms
by: Yuan, Yueming, et al.
Published: (2025)
by: Yuan, Yueming, et al.
Published: (2025)
MoE-SpeQ: Speculative Quantized Decoding with Proactive Expert Prefetching and Offloading for Mixture-of-Experts
by: Wang, Wenfeng, et al.
Published: (2025)
by: Wang, Wenfeng, 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)
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 MoE Serving in the Memory-Bound Regime: Balance Activated Experts, Not Tokens
by: Yu, Yanpeng, et al.
Published: (2025)
by: Yu, Yanpeng, et al.
Published: (2025)
MoE-CAP: Benchmarking Cost, Accuracy and Performance of Sparse Mixture-of-Experts Systems
by: Jiang, Yinsicheng, et al.
Published: (2025)
by: Jiang, Yinsicheng, et al.
Published: (2025)
MoE-CAP: Benchmarking Cost, Accuracy and Performance of Sparse Mixture-of-Experts Systems
by: Jiang, Yinsicheng, et al.
Published: (2024)
by: Jiang, Yinsicheng, et al.
Published: (2024)
ReaLB: Real-Time Load Balancing for Multimodal MoE Inference
by: Wang, Yingping, et al.
Published: (2026)
by: Wang, Yingping, et al.
Published: (2026)
UniEP: Unified Expert-Parallel MoE MegaKernel for LLM Training
by: Zheng, Size, et al.
Published: (2026)
by: Zheng, Size, et al.
Published: (2026)
MoETuner: Optimized Mixture of Expert Serving with Balanced Expert Placement and Token Routing
by: Go, Seokjin, et al.
Published: (2025)
by: Go, Seokjin, et al.
Published: (2025)
ViBE: Co-Optimizing Workload Skew and Hardware Variability for MoE Serving
by: Go, Seokjin, et al.
Published: (2026)
by: Go, Seokjin, et al.
Published: (2026)
Stable-MoE: Lyapunov-based Token Routing for Distributed Mixture-of-Experts Training over Edge Networks
by: Shi, Long, et al.
Published: (2025)
by: Shi, Long, et al.
Published: (2025)
BrownoutServe: SLO-Aware Inference Serving under Bursty Workloads for MoE-based LLMs
by: Hu, Jianmin, et al.
Published: (2025)
by: Hu, Jianmin, et al.
Published: (2025)
Federated Unsupervised Visual Representation Learning via Exploiting General Content and Personal Style
by: Yang, Yuewei, et al.
Published: (2022)
by: Yang, Yuewei, et al.
Published: (2022)
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)
MegaScale-MoE: Large-Scale Communication-Efficient Training of Mixture-of-Experts Models in Production
by: Jin, Chao, et al.
Published: (2025)
by: Jin, Chao, et al.
Published: (2025)
Elastic Mixture of Rank-Wise Experts for Knowledge Reuse in Federated Fine-Tuning
by: Wu, Yebo, et al.
Published: (2025)
by: Wu, Yebo, et al.
Published: (2025)
Toward Cost-Efficient Serving of Mixture-of-Experts with Asynchrony
by: Wang, Shaoyu, et al.
Published: (2025)
by: Wang, Shaoyu, et al.
Published: (2025)
eMoE: Task-aware Memory Efficient Mixture-of-Experts-Based (MoE) Model Inference
by: Tairin, Suraiya, et al.
Published: (2025)
by: Tairin, Suraiya, 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)
MixServe: An Automatic Distributed Serving System for MoE Models with Hybrid Parallelism Based on Fused Communication Algorithm
by: Zhou, Bowen, et al.
Published: (2026)
by: Zhou, Bowen, et al.
Published: (2026)
LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing
by: Nie, Xiaonan, et al.
Published: (2024)
by: Nie, Xiaonan, et al.
Published: (2024)
MegaScale-Infer: Serving Mixture-of-Experts at Scale with Disaggregated Expert Parallelism
by: Zhu, Ruidong, et al.
Published: (2025)
by: Zhu, Ruidong, et al.
Published: (2025)
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)
MoE-Lens: Towards the Hardware Limit of High-Throughput MoE LLM Serving Under Resource Constraints
by: Yuan, Yichao, et al.
Published: (2025)
by: Yuan, Yichao, et al.
Published: (2025)
FlowMoE: A Scalable Pipeline Scheduling Framework for Distributed Mixture-of-Experts Training
by: Gao, Yunqi, et al.
Published: (2025)
by: Gao, Yunqi, et al.
Published: (2025)
Remoe: Towards Efficient and Low-Cost MoE Inference in Serverless Computing
by: Liu, Wentao, et al.
Published: (2025)
by: Liu, Wentao, et al.
Published: (2025)
CRAFT: Fine-Grained Cost-Aware Expert Replication For Efficient Mixture-of-Experts Serving
by: Zhao, Adrian, et al.
Published: (2026)
by: Zhao, Adrian, et al.
Published: (2026)
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)
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)
Optimal Expert Selection for Distributed Mixture-of-Experts at the Wireless Edge
by: Qin, Shengling, et al.
Published: (2025)
by: Qin, Shengling, et al.
Published: (2025)
Similar Items
-
DualSparse-MoE: Coordinating Tensor/Neuron-Level Sparsity with Expert Partition and Reconstruction
by: Cai, Weilin, et al.
Published: (2025) -
Expert-as-a-Service: Towards Efficient, Scalable, and Robust Large-scale MoE Serving
by: Liu, Ziming, et al.
Published: (2025) -
Hexa-MoE: Efficient and Heterogeneous-aware Training for Mixture-of-Experts
by: Luo, Shuqing, et al.
Published: (2024) -
Janus: Disaggregating Attention and Experts for Scalable MoE Inference
by: Zhang, Zhexiang, et al.
Published: (2025) -
DuoServe-MoE: Dual-Phase Expert Prefetch and Caching for LLM Inference QoS Assurance
by: Zhang, Yuning, et al.
Published: (2025)