EAQuant: Enhancing Post-Training Quantization for MoE Models via Expert-Aware Optimization
Fuente:
arXiv
Saved in:
| Main Authors: | Fu, Zhongqian, Zhao, Tianyi, Ding, Ning, Yu, Xianzhi, Li, Xiaosong, Tang, Yehui, Wang, Yunhe |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity
by: Tang, Yehui, et al.
Published: (2025)
by: Tang, Yehui, et al.
Published: (2025)
Post-Trained MoE Can Skip Half Experts via Self-Distillation
by: Lv, Xingtai, et al.
Published: (2026)
by: Lv, Xingtai, et al.
Published: (2026)
Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs
by: Tang, Yehui, et al.
Published: (2025)
by: Tang, Yehui, et al.
Published: (2025)
Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts
by: Wu, Haoyuan, et al.
Published: (2025)
by: Wu, Haoyuan, et al.
Published: (2025)
QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts
by: Li, Pingzhi, et al.
Published: (2024)
by: Li, Pingzhi, et al.
Published: (2024)
MoE-Prism: Disentangling Monolithic Experts for Elastic MoE Services via Model-System Co-Designs
by: Xia, Xinfeng, et al.
Published: (2025)
by: Xia, Xinfeng, et al.
Published: (2025)
GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs
by: Deng, Jianing, et al.
Published: (2026)
by: Deng, Jianing, et al.
Published: (2026)
EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference
by: Qian, Yulei, et al.
Published: (2024)
by: Qian, Yulei, et al.
Published: (2024)
GPT4Image: Large Pre-trained Models Help Vision Models Learn Better on Perception Task
by: Ding, Ning, et al.
Published: (2023)
by: Ding, Ning, et al.
Published: (2023)
LLaMA-MoE v2: Exploring Sparsity of LLaMA from Perspective of Mixture-of-Experts with Post-Training
by: Qu, Xiaoye, et al.
Published: (2024)
by: Qu, Xiaoye, et al.
Published: (2024)
DIVE into MoE: Diversity-Enhanced Reconstruction of Large Language Models from Dense into Mixture-of-Experts
by: Feng, Yuchen, et al.
Published: (2025)
by: Feng, Yuchen, et al.
Published: (2025)
Leave It to the Experts: Detecting Knowledge Distillation via MoE Expert Signatures
by: Li, Pingzhi, et al.
Published: (2025)
by: Li, Pingzhi, et al.
Published: (2025)
Steering MoE LLMs via Expert (De)Activation
by: Fayyaz, Mohsen, et al.
Published: (2025)
by: Fayyaz, Mohsen, et al.
Published: (2025)
SlimMoE: Structured Compression of Large MoE Models via Expert Slimming and Distillation
by: Li, Zichong, et al.
Published: (2025)
by: Li, Zichong, et al.
Published: (2025)
$\infty$-MoE: Generalizing Mixture of Experts to Infinite Experts
by: Takashiro, Shota, et al.
Published: (2026)
by: Takashiro, Shota, et al.
Published: (2026)
MH-MoE: Multi-Head Mixture-of-Experts
by: Huang, Shaohan, et al.
Published: (2024)
by: Huang, Shaohan, et al.
Published: (2024)
Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference
by: Liu, Baihui, et al.
Published: (2026)
by: Liu, Baihui, et al.
Published: (2026)
Mixture of Lookup Experts
by: Jie, Shibo, et al.
Published: (2025)
by: Jie, Shibo, et al.
Published: (2025)
Elastic MoE: Unlocking the Inference-Time Scalability of Mixture-of-Experts
by: Gu, Naibin, et al.
Published: (2025)
by: Gu, Naibin, et al.
Published: (2025)
Training Report of TeleChat3-MoE
by: Liu, Xinzhang, et al.
Published: (2025)
by: Liu, Xinzhang, et al.
Published: (2025)
S2MoE: Robust Sparse Mixture of Experts via Stochastic Learning
by: Do, Giang, et al.
Published: (2025)
by: Do, Giang, et al.
Published: (2025)
Skywork-MoE: A Deep Dive into Training Techniques for Mixture-of-Experts Language Models
by: Wei, Tianwen, et al.
Published: (2024)
by: Wei, Tianwen, et al.
Published: (2024)
Advancing Expert Specialization for Better MoE
by: Guo, Hongcan, et al.
Published: (2025)
by: Guo, Hongcan, et al.
Published: (2025)
OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale
by: Shi, Jingze, et al.
Published: (2026)
by: Shi, Jingze, et al.
Published: (2026)
Benchmarking Post-Training Quantization of Large Language Models under Microscaling Floating Point Formats
by: Zhang, Manyi, et al.
Published: (2026)
by: Zhang, Manyi, et al.
Published: (2026)
Post-Training Quantization for Diffusion Transformer via Hierarchical Timestep Grouping
by: Ding, Ning, et al.
Published: (2025)
by: Ding, Ning, et al.
Published: (2025)
Enhancing Post-Training Quantization via Future Activation Awareness
by: Lv, Zheqi, et al.
Published: (2026)
by: Lv, Zheqi, et al.
Published: (2026)
GEM: GPU-Variability-Aware Expert to GPU Mapping for MoE Systems
by: Wawdhane, Sourish, et al.
Published: (2026)
by: Wawdhane, Sourish, et al.
Published: (2026)
$\texttt{MoE-RBench}$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts
by: Chen, Guanjie, et al.
Published: (2024)
by: Chen, Guanjie, et al.
Published: (2024)
An Empirical Study of World Model Quantization
by: Fu, Zhongqian, et al.
Published: (2026)
by: Fu, Zhongqian, et al.
Published: (2026)
GuiLoMo: Allocating Expert Number and Rank for LoRA-MoE via Bilevel Optimization with GuidedSelection Vectors
by: Zhang, Hengyuan, et al.
Published: (2025)
by: Zhang, Hengyuan, et al.
Published: (2025)
Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse
by: Fu, Zizhuo, et al.
Published: (2026)
by: Fu, Zizhuo, et al.
Published: (2026)
Marco-MoE: Open Multilingual Mixture-of-Expert Language Models with Efficient Upcycling
by: Jiang, Fan, et al.
Published: (2026)
by: Jiang, Fan, et al.
Published: (2026)
SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts
by: Muzio, Alexandre, et al.
Published: (2024)
by: Muzio, Alexandre, et al.
Published: (2024)
ExpertWeaver: Unlocking the Inherent MoE in Dense LLMs with GLU Activation Patterns
by: Zhao, Ziyu, et al.
Published: (2026)
by: Zhao, Ziyu, et al.
Published: (2026)
Do Domain-specific Experts exist in MoE-based LLMs?
by: Do, Giang, et al.
Published: (2026)
by: Do, Giang, et al.
Published: (2026)
Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning
by: Bi, Zhenni, et al.
Published: (2024)
by: Bi, Zhenni, et al.
Published: (2024)
TEAM: Temporal-Spatial Consistency Guided Expert Activation for MoE Diffusion Language Model Acceleration
by: Wei, Linye, et al.
Published: (2026)
by: Wei, Linye, et al.
Published: (2026)
MoE-DiffuSeq: Enhancing Long-Document Diffusion Models with Sparse Attention and Mixture of Experts
by: Christoforos, Alexandros, et al.
Published: (2025)
by: Christoforos, Alexandros, et al.
Published: (2025)
LLaDA-MoE: A Sparse MoE Diffusion Language Model
by: Zhu, Fengqi, et al.
Published: (2025)
by: Zhu, Fengqi, et al.
Published: (2025)
Similar Items
-
Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity
by: Tang, Yehui, et al.
Published: (2025) -
Post-Trained MoE Can Skip Half Experts via Self-Distillation
by: Lv, Xingtai, et al.
Published: (2026) -
Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs
by: Tang, Yehui, et al.
Published: (2025) -
Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts
by: Wu, Haoyuan, et al.
Published: (2025) -
QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts
by: Li, Pingzhi, et al.
Published: (2024)