Cover Image

DyMoE: Dynamic Expert Orchestration with Mixed-Precision Quantization for Efficient MoE Inference on Edge

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Huang, Yuegui, Fang, Zhiyuan, Luo, Weiqi, Wu, Ruoyu, Chen, Wuhui, Zheng, Zibin
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Fate: Fast Edge Inference of Mixture-of-Experts Models via Cross-Layer Gate
by: Fang, Zhiyuan, et al.
Published: (2025)

Klotski: Efficient Mixture-of-Expert Inference via Expert-Aware Multi-Batch Pipeline
by: Fang, Zhiyuan, et al.
Published: (2025)

OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale
by: Shi, Jingze, et al.
Published: (2026)

GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs
by: Deng, Jianing, et al.
Published: (2026)

OD-MoE: On-Demand Expert Loading for Cacheless Edge-Distributed MoE Inference
by: Wang, Liujianfu, et al.
Published: (2025)

MoE-Infinity: Efficient MoE Inference on Personal Machines with Sparsity-Aware Expert Cache
by: Xue, Leyang, et al.
Published: (2024)

EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference
by: Qian, Yulei, et al.
Published: (2024)

HOBBIT: A Mixed Precision Expert Offloading System for Fast MoE Inference
by: Tang, Peng, et al.
Published: (2024)

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts
by: Wu, Haoyuan, et al.
Published: (2025)

AdapMoE: Adaptive Sensitivity-based Expert Gating and Management for Efficient MoE Inference
by: Zhong, Shuzhang, et al.
Published: (2024)

SiftMoE: Similarity-Aware Energy-Efficient Expert Selection for Wireless Distributed MoE Inference
by: Chen, Qian, et al.
Published: (2026)

MxMoE: Mixed-precision Quantization for MoE with Accuracy and Performance Co-Design
by: Duanmu, Haojie, et al.
Published: (2025)

MoE-SpAc: Efficient MoE Inference Based on Speculative Activation Utility in Heterogeneous Edge Scenarios
by: Li, Shuhuai, et al.
Published: (2026)

ExpertFlow: Adaptive Expert Scheduling and Memory Coordination for Efficient MoE Inference
by: Shen, Zixu, et al.
Published: (2025)

Accelerating Edge Inference for Distributed MoE Models with Latency-Optimized Expert Placement
by: Wu, Tian, 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)

A Scheduling Framework for Efficient MoE Inference on Edge GPU-NDP Systems
by: Wu, Qi, et al.
Published: (2026)

Accelerating MoE Model Inference with Expert Sharding
by: Balmau, Oana, et al.
Published: (2025)

MiLo: Efficient Quantized MoE Inference with Mixture of Low-Rank Compensators
by: Huang, Beichen, et al.
Published: (2025)

Elastic MoE: Unlocking the Inference-Time Scalability of Mixture-of-Experts
by: Gu, Naibin, et al.
Published: (2025)

MoE-GPS: Guidlines for Prediction Strategy for Dynamic Expert Duplication in MoE Load Balancing
by: Ma, Haiyue, et al.
Published: (2025)

eMoE: Task-aware Memory Efficient Mixture-of-Experts-Based (MoE) Model Inference
by: Tairin, Suraiya, et al.
Published: (2025)

SP-MoE: Speculative Decoding and Prefetching for Accelerating MoE-based Model Inference
by: Chen, Liangkun, et al.
Published: (2025)

MH-MoE: Multi-Head Mixture-of-Experts
by: Huang, Shaohan, et al.
Published: (2024)

Lynx: Enabling Efficient MoE Inference through Dynamic Batch-Aware Expert Selection
by: Gupta, Vima, et al.
Published: (2024)

Efficient MoE Inference with Fine-Grained Scheduling of Disaggregated Expert Parallelism
by: Pan, Xinglin, et al.
Published: (2025)

MoPEQ: Mixture of Mixed Precision Quantized Experts
by: Chitty-Venkata, Krishna Teja, et al.
Published: (2025)

Janus: Disaggregating Attention and Experts for Scalable MoE Inference
by: Zhang, Zhexiang, et al.
Published: (2025)

GRACE-MoE: Grouping and Replication with Locality-Aware Routing for Efficient Distributed MoE Inference
by: Han, Yu, et al.
Published: (2025)

DynaMo: Runtime Switchable Quantization for MoE with Cross-Dataset Adaptation
by: Zheng, Zihao, et al.
Published: (2025)

CoMoE: Collaborative Optimization of Expert Aggregation and Offloading for MoE-based LLMs at Edge
by: Li, Muqing, et al.
Published: (2025)

SliceMoE: Bit-Sliced Expert Caching under Miss-Rate Constraints for Efficient MoE Inference
by: Choi, Yuseon, et al.
Published: (2025)

Hexa-MoE: Efficient and Heterogeneous-aware Training for Mixture-of-Experts
by: Luo, Shuqing, et al.
Published: (2024)

DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models
by: Aghdam, Maryam Akhavan, et al.
Published: (2024)

Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference
by: Liu, Baihui, et al.
Published: (2026)

Orchestrating Heterogeneous Experts: A Scalable MoE Framework with Anisotropy-Preserving Fusion
by: Liu, Ye, et al.
Published: (2025)

MoE-Gen: High-Throughput MoE Inference on a Single GPU with Module-Based Batching
by: Xu, Tairan, et al.
Published: (2025)

MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUs
by: Cao, Shiyi, et al.
Published: (2024)

LExI: Layer-Adaptive Active Experts for Efficient MoE Model Inference
by: Chitty-Venkata, Krishna Teja, et al.
Published: (2025)

MergeMoE: Efficient Compression of MoE Models via Expert Output Merging
by: Miao, Ruijie, et al.
Published: (2025)