MoBiE: Efficient Inference of Mixture of Binary Experts under Post-Training Quantization
Fuente:
arXiv
Saved in:
| Main Authors: | Zhao, Zhixiong, Xu, Zukang, Chen, Zhixuan, Yang, Dawei |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
by: Xu, Zukang, et al.
Published: (2026)
by: Xu, Zukang, et al.
Published: (2026)
BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs
by: Zhao, Zhixiong, et al.
Published: (2026)
by: Zhao, Zhixiong, et al.
Published: (2026)
MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance
by: Hu, Xing, et al.
Published: (2025)
by: Hu, Xing, et al.
Published: (2025)
TORQ: Two-Level Orthogonal Rotation for MXFP4 Quantization
by: Xu, Zukang, et al.
Published: (2026)
by: Xu, Zukang, et al.
Published: (2026)
QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts
by: Li, Pingzhi, et al.
Published: (2024)
by: Li, Pingzhi, et al.
Published: (2024)
Dynamic Expert Quantization for Scalable Mixture-of-Experts Inference
by: Chu, Kexin, et al.
Published: (2025)
by: Chu, Kexin, et al.
Published: (2025)
RWKVQuant: Quantizing the RWKV Family with Proxy Guided Hybrid of Scalar and Vector Quantization
by: Xu, Chen, et al.
Published: (2025)
by: Xu, Chen, et al.
Published: (2025)
MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods
by: Xu, Zukang, et al.
Published: (2025)
by: Xu, Zukang, et al.
Published: (2025)
PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling
by: Yue, Yuxuan, et al.
Published: (2025)
by: Yue, Yuxuan, et al.
Published: (2025)
RQ-MoE: Residual Quantization via Mixture of Experts for Efficient Input-Dependent Vector Compression
by: Zhong, Zhengjia, et al.
Published: (2026)
by: Zhong, Zhengjia, et al.
Published: (2026)
BuddyMoE: Exploiting Expert Redundancy to Accelerate Memory-Constrained Mixture-of-Experts Inference
by: Wang, Yun, et al.
Published: (2025)
by: Wang, Yun, 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)
Efficient Quantization of Mixture-of-Experts with Theoretical Generalization Guarantees
by: Chowdhury, Mohammed Nowaz Rabbani, et al.
Published: (2026)
by: Chowdhury, Mohammed Nowaz Rabbani, et al.
Published: (2026)
OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting
by: Hu, Xing, et al.
Published: (2025)
by: Hu, Xing, et al.
Published: (2025)
SpecMoE: A Fast and Efficient Mixture-of-Experts Inference via Self-Assisted Speculative Decoding
by: Bang, Jehyeon, et al.
Published: (2026)
by: Bang, Jehyeon, 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)
ProbMoE: Differentiable Probabilistic Routing for Mixture-of-Experts
by: Zhao, Heng, et al.
Published: (2026)
by: Zhao, Heng, et al.
Published: (2026)
PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference
by: Zhao, Yushu, et al.
Published: (2025)
by: Zhao, Yushu, et al.
Published: (2025)
HyperMoE: Towards Better Mixture of Experts via Transferring Among Experts
by: Zhao, Hao, et al.
Published: (2024)
by: Zhao, Hao, 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)
MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
by: Jin, Peng, et al.
Published: (2024)
by: Jin, Peng, et al.
Published: (2024)
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)
OrdMoE: Preference Alignment via Hierarchical Expert Group Ranking in Multimodal Mixture-of-Experts LLMs
by: Gao, Yuting, et al.
Published: (2025)
by: Gao, Yuting, et al.
Published: (2025)
Klotski: Efficient Mixture-of-Expert Inference via Expert-Aware Multi-Batch Pipeline
by: Fang, Zhiyuan, et al.
Published: (2025)
by: Fang, Zhiyuan, et al.
Published: (2025)
PWC-MoE: Privacy-Aware Wireless Collaborative Mixture of Experts
by: Su, Yang, et al.
Published: (2025)
by: Su, Yang, et al.
Published: (2025)
BigMac: A Communication-Efficient Mixture-of-Experts Model Structure for Fast Training and Inference
by: Jin, Zewen, et al.
Published: (2025)
by: Jin, Zewen, et al.
Published: (2025)
Efficiently Editing Mixture-of-Experts Models with Compressed Experts
by: He, Yifei, et al.
Published: (2025)
by: He, Yifei, et al.
Published: (2025)
Accelerating Mixture-of-Expert Inference with Adaptive Expert Split Mechanism
by: Yan, Jiaming, et al.
Published: (2025)
by: Yan, Jiaming, et al.
Published: (2025)
MoNTA: Accelerating Mixture-of-Experts Training with Network-Traffc-Aware Parallel Optimization
by: Guo, Jingming, et al.
Published: (2024)
by: Guo, Jingming, et al.
Published: (2024)
EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models
by: Chen, Yuanteng, et al.
Published: (2025)
by: Chen, Yuanteng, et al.
Published: (2025)
LightMoE: Reducing Mixture-of-Experts Redundancy through Expert Replacing
by: Hao, Jiawei, et al.
Published: (2026)
by: Hao, Jiawei, et al.
Published: (2026)
MoQE: Improve Quantization Model performance via Mixture of Quantization Experts
by: Zhang, Jinhao, et al.
Published: (2025)
by: Zhang, Jinhao, et al.
Published: (2025)
Speculating Experts Accelerates Inference for Mixture-of-Experts
by: Madan, Vivan, et al.
Published: (2026)
by: Madan, Vivan, et al.
Published: (2026)
Mixture of Experts (MoE): A Big Data Perspective
by: Gan, Wensheng, et al.
Published: (2025)
by: Gan, Wensheng, et al.
Published: (2025)
SDG-MoE: Signed Debate Graph Mixture-of-Experts
by: Kulibaba, Stepan, et al.
Published: (2026)
by: Kulibaba, Stepan, et al.
Published: (2026)
L-MoE: End-to-End Training of a Lightweight Mixture of Low-Rank Adaptation Experts
by: Ji, Shihao, et al.
Published: (2025)
by: Ji, Shihao, et al.
Published: (2025)
BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization
by: Zhao, Jiayu, et al.
Published: (2026)
by: Zhao, Jiayu, et al.
Published: (2026)
MoE-Health: A Mixture of Experts Framework for Robust Multimodal Healthcare Prediction
by: Wang, Xiaoyang, et al.
Published: (2025)
by: Wang, Xiaoyang, 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)
Post-Trained MoE Can Skip Half Experts via Self-Distillation
by: Lv, Xingtai, et al.
Published: (2026)
by: Lv, Xingtai, et al.
Published: (2026)
Similar Items
-
KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
by: Xu, Zukang, et al.
Published: (2026) -
BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs
by: Zhao, Zhixiong, et al.
Published: (2026) -
MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance
by: Hu, Xing, et al.
Published: (2025) -
TORQ: Two-Level Orthogonal Rotation for MXFP4 Quantization
by: Xu, Zukang, et al.
Published: (2026) -
QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts
by: Li, Pingzhi, et al.
Published: (2024)