QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Pingzhi, Jin, Xiaolong, Tan, Zhen, Cheng, Yu, Chen, Tianlong |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM
by: Wang, Dongwei, et al.
Published: (2026)
by: Wang, Dongwei, et al.
Published: (2026)
Can GRPO Help LLMs Transcend Their Pretraining Origin?
by: Ni, Kangqi, et al.
Published: (2025)
by: Ni, Kangqi, et al.
Published: (2025)
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
by: Xiao, Guangxuan, et al.
Published: (2022)
by: Xiao, Guangxuan, et al.
Published: (2022)
MoBiE: Efficient Inference of Mixture of Binary Experts under Post-Training Quantization
by: Zhao, Zhixiong, et al.
Published: (2026)
by: Zhao, Zhixiong, et al.
Published: (2026)
Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design
by: Zhang, Mohan, et al.
Published: (2025)
by: Zhang, Mohan, et al.
Published: (2025)
DOGe: Defensive Output Generation for LLM Protection Against Knowledge Distillation
by: Li, Pingzhi, et al.
Published: (2025)
by: Li, Pingzhi, et al.
Published: (2025)
MobileMoE: Scaling On-Device Mixture of Experts
by: Chen, Yanbei, et al.
Published: (2026)
by: Chen, Yanbei, et al.
Published: (2026)
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)
Post-Trained MoE Can Skip Half Experts via Self-Distillation
by: Lv, Xingtai, et al.
Published: (2026)
by: Lv, Xingtai, et al.
Published: (2026)
Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy
by: Li, Pingzhi, et al.
Published: (2023)
by: Li, Pingzhi, et al.
Published: (2023)
MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods
by: Xu, Zukang, et al.
Published: (2025)
by: Xu, Zukang, et al.
Published: (2025)
MoIN: Mixture of Introvert Experts to Upcycle an LLM
by: Tejankar, Ajinkya, et al.
Published: (2024)
by: Tejankar, Ajinkya, et al.
Published: (2024)
ButterflyQuant: Ultra-low-bit LLM Quantization through Learnable Orthogonal Butterfly Transforms
by: Xu, Bingxin, et al.
Published: (2025)
by: Xu, Bingxin, et al.
Published: (2025)
Skill-Based Mixture-of-Experts: Adaptive Routing for Heterogeneous Reasoning via Inferred Skills
by: Chen, Justin Chih-Yao, et al.
Published: (2025)
by: Chen, Justin Chih-Yao, et al.
Published: (2025)
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)
D$^2$Quant: Accurate Low-bit Post-Training Weight Quantization for LLMs
by: Yan, Xianglong, et al.
Published: (2026)
by: Yan, Xianglong, et al.
Published: (2026)
Linear-MoE: Linear Sequence Modeling Meets Mixture-of-Experts
by: Sun, Weigao, et al.
Published: (2025)
by: Sun, Weigao, et al.
Published: (2025)
$\texttt{MoE-RBench}$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts
by: Chen, Guanjie, et al.
Published: (2024)
by: Chen, Guanjie, et al.
Published: (2024)
Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient
by: Ludziejewski, Jan, et al.
Published: (2025)
by: Ludziejewski, Jan, et al.
Published: (2025)
MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts
by: Pióro, Maciej, et al.
Published: (2024)
by: Pióro, Maciej, et al.
Published: (2024)
$μ$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts
by: Koike-Akino, Toshiaki, et al.
Published: (2025)
by: Koike-Akino, Toshiaki, et al.
Published: (2025)
Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations
by: Jaiswal, Ajay, et al.
Published: (2025)
by: Jaiswal, Ajay, et al.
Published: (2025)
DLO: Dynamic Layer Operation for Efficient Vertical Scaling of LLMs
by: Tan, Zhen, et al.
Published: (2024)
by: Tan, Zhen, et al.
Published: (2024)
PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches
by: Khan, Rana Muhammad Shahroz, et al.
Published: (2024)
by: Khan, Rana Muhammad Shahroz, et al.
Published: (2024)
Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge
by: Shen, Xuan, et al.
Published: (2023)
by: Shen, Xuan, et al.
Published: (2023)
MoESD: Mixture of Experts Stable Diffusion to Mitigate Gender Bias
by: Wang, Guorun, et al.
Published: (2024)
by: Wang, Guorun, et al.
Published: (2024)
MoECollab: Democratizing LLM Development Through Collaborative Mixture of Experts
by: Harshit
Published: (2025)
by: Harshit
Published: (2025)
LogQuant: Log-Distributed 2-Bit Quantization of KV Cache with Superior Accuracy Preservation
by: Chen, Han, et al.
Published: (2025)
by: Chen, Han, et al.
Published: (2025)
MoLEx: Mixture of Layer Experts for Finetuning with Sparse Upcycling
by: Teo, Rachel S. Y., et al.
Published: (2025)
by: Teo, Rachel S. Y., et al.
Published: (2025)
Examining Reasoning LLMs-as-Judges in Non-Verifiable LLM Post-Training
by: Liu, Yixin, et al.
Published: (2026)
by: Liu, Yixin, et al.
Published: (2026)
Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts
by: Yun, Sukwon, et al.
Published: (2024)
by: Yun, Sukwon, et al.
Published: (2024)
Enhancing Quantum Security over Federated Learning via Post-Quantum Cryptography
by: Li, Pingzhi, et al.
Published: (2024)
by: Li, Pingzhi, et al.
Published: (2024)
MoE-DisCo:Low Economy Cost Training Mixture-of-Experts Models
by: Ye, Xin, et al.
Published: (2026)
by: Ye, Xin, et al.
Published: (2026)
KUET at StanceNakba Shared Task: StanceMoE: Mixture-of-Experts Architecture for Stance Detection
by: Shafi, Abdullah Al, et al.
Published: (2026)
by: Shafi, Abdullah Al, et al.
Published: (2026)
A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning
by: Yadav, Prateek, et al.
Published: (2024)
by: Yadav, Prateek, et al.
Published: (2024)
MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
by: Jin, Peng, et al.
Published: (2024)
by: Jin, Peng, et al.
Published: (2024)
MoM: Linear Sequence Modeling with Mixture-of-Memories
by: Du, Jusen, et al.
Published: (2025)
by: Du, Jusen, et al.
Published: (2025)
Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models
by: Pan, Bowen, et al.
Published: (2024)
by: Pan, Bowen, et al.
Published: (2024)
MetaMoE: Diversity-Aware Proxy Selection for Privacy-Preserving Mixture-of-Experts Unification
by: Jiang, Weisen, et al.
Published: (2026)
by: Jiang, Weisen, et al.
Published: (2026)
Similar Items
-
Leave It to the Experts: Detecting Knowledge Distillation via MoE Expert Signatures
by: Li, Pingzhi, et al.
Published: (2025) -
MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM
by: Wang, Dongwei, et al.
Published: (2026) -
Can GRPO Help LLMs Transcend Their Pretraining Origin?
by: Ni, Kangqi, et al.
Published: (2025) -
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
by: Xiao, Guangxuan, et al.
Published: (2022) -
MoBiE: Efficient Inference of Mixture of Binary Experts under Post-Training Quantization
by: Zhao, Zhixiong, et al.
Published: (2026)