Elastic MoE: Unlocking the Inference-Time Scalability of Mixture-of-Experts
Fuente:
arXiv
Saved in:
| Main Authors: | Gu, Naibin, Zhang, Zhenyu, Feng, Yuchen, Chen, Yilong, Fu, Peng, Lin, Zheng, Wang, Shuohuan, Sun, Yu, Wu, Hua, Wang, Weiping, Wang, Haifeng |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
Mixture of Universal Experts: Scaling Virtual Width via Depth-Width Transformation
by: Chen, Yilong, et al.
Published: (2026)
by: Chen, Yilong, et al.
Published: (2026)
Advantageous Parameter Expansion Training Makes Better Large Language Models
by: Gu, Naibin, et al.
Published: (2025)
by: Gu, Naibin, et al.
Published: (2025)
BeamLoRA: Beam-Constraint Low-Rank Adaptation
by: Gu, Naibin, et al.
Published: (2025)
by: Gu, Naibin, et al.
Published: (2025)
V-ITI: Mitigating Hallucinations in Multimodal Large Language Models via Visual Inference-Time Intervention
by: Sun, Nan, et al.
Published: (2025)
by: Sun, Nan, et al.
Published: (2025)
CBP-Tuning: Efficient Local Customization for Black-box Large Language Models
by: Zhao, Jiaxuan, et al.
Published: (2025)
by: Zhao, Jiaxuan, et al.
Published: (2025)
Upcycling Instruction Tuning from Dense to Mixture-of-Experts via Parameter Merging
by: Hui, Tingfeng, et al.
Published: (2024)
by: Hui, Tingfeng, 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)
Janus: Disaggregating Attention and Experts for Scalable MoE Inference
by: Zhang, Zhexiang, et al.
Published: (2025)
by: Zhang, Zhexiang, et al.
Published: (2025)
Training Matryoshka Mixture-of-Experts for Elastic Inference-Time Expert Utilization
by: Wang, Yaoxiang, et al.
Published: (2025)
by: Wang, Yaoxiang, et al.
Published: (2025)
Mixture of Hidden-Dimensions Transformer
by: Chen, Yilong, et al.
Published: (2024)
by: Chen, Yilong, et al.
Published: (2024)
Light-PEFT: Lightening Parameter-Efficient Fine-Tuning via Early Pruning
by: Gu, Naibin, et al.
Published: (2024)
by: Gu, Naibin, et al.
Published: (2024)
Adapt Once, Thrive with Updates: Transferable Parameter-Efficient Fine-Tuning on Evolving Base Models
by: Gu, Naibin, et al.
Published: (2025)
by: Gu, Naibin, 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)
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)
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)
$μ$-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)
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)
DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts
by: Feng, Jiarui, et al.
Published: (2026)
by: Feng, Jiarui, et al.
Published: (2026)
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
by: Lin, Bin, et al.
Published: (2024)
by: Lin, Bin, et al.
Published: (2024)
Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detection
by: Lei, Tianwu, et al.
Published: (2024)
by: Lei, Tianwu, et al.
Published: (2024)
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference
by: Hwang, Ranggi, et al.
Published: (2023)
by: Hwang, Ranggi, et al.
Published: (2023)
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)
NACL: A General and Effective KV Cache Eviction Framework for LLMs at Inference Time
by: Chen, Yilong, et al.
Published: (2024)
by: Chen, Yilong, et al.
Published: (2024)
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
by: Shi, Xiaoming, et al.
Published: (2024)
by: Shi, Xiaoming, et al.
Published: (2024)
Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging
by: Li, Lujun, et al.
Published: (2025)
by: Li, Lujun, et al.
Published: (2025)
MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
by: Jin, Peng, et al.
Published: (2024)
by: Jin, Peng, et al.
Published: (2024)
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)
Symphony-MoE: Harmonizing Disparate Pre-trained Models into a Coherent Mixture-of-Experts
by: Wang, Qi, et al.
Published: (2025)
by: Wang, Qi, et al.
Published: (2025)
Causal Path Alignment: Anchoring the Optimization Trajectory for Controllable In-Parameter Knowledge Editing
by: Liu, Xiyu, et al.
Published: (2025)
by: Liu, Xiyu, et al.
Published: (2025)
Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts
by: Li, Yunxin, et al.
Published: (2024)
by: Li, Yunxin, et al.
Published: (2024)
ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model
by: Xu, Yuhao, et al.
Published: (2026)
by: Xu, Yuhao, 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)
Horseshoe Mixtures-of-Experts (HS-MoE)
by: Polson, Nick, et al.
Published: (2026)
by: Polson, Nick, et al.
Published: (2026)
Efficient MoE Inference with Fine-Grained Scheduling of Disaggregated Expert Parallelism
by: Pan, Xinglin, et al.
Published: (2025)
by: Pan, Xinglin, et al.
Published: (2025)
MoE-Infinity: Efficient MoE Inference on Personal Machines with Sparsity-Aware Expert Cache
by: Xue, Leyang, et al.
Published: (2024)
by: Xue, Leyang, et al.
Published: (2024)
Astro-MoE: Mixture of Experts for Multiband Astronomical Time Series
by: Cádiz-Leyton, Martina, et al.
Published: (2025)
by: Cádiz-Leyton, Martina, et al.
Published: (2025)
Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity
by: Tang, Yehui, et al.
Published: (2025)
by: Tang, Yehui, 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)
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)
Similar Items
-
DIVE into MoE: Diversity-Enhanced Reconstruction of Large Language Models from Dense into Mixture-of-Experts
by: Feng, Yuchen, et al.
Published: (2025) -
Mixture of Universal Experts: Scaling Virtual Width via Depth-Width Transformation
by: Chen, Yilong, et al.
Published: (2026) -
Advantageous Parameter Expansion Training Makes Better Large Language Models
by: Gu, Naibin, et al.
Published: (2025) -
BeamLoRA: Beam-Constraint Low-Rank Adaptation
by: Gu, Naibin, et al.
Published: (2025) -
V-ITI: Mitigating Hallucinations in Multimodal Large Language Models via Visual Inference-Time Intervention
by: Sun, Nan, et al.
Published: (2025)