Cover Image

MoE-PHDS: One MoE checkpoint for flexible runtime sparsity

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hannah, Lauren. A, Zibakhsh, Soheil, Nishu, Kumari, Kundu, Arnav, Razlighi, Mohammad Samragh, Farajtabar, Mehrdad, Cho, Minsik
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

MoEs Are Stronger than You Think: Hyper-Parallel Inference Scaling with RoE
by: Zibakhsh, Soheil, et al.
Published: (2025)

Your LLM Knows the Future: Uncovering Its Multi-Token Prediction Potential
by: Samragh, Mohammad, et al.
Published: (2025)

DOT-MoE: Differentiable Optimal Transport for MoEfication
by: Bamba, Udbhav, et al.
Published: (2026)

SPD: Sync-Point Drop for Efficient Tensor Parallelism of Large Language Models
by: Kim, Han-Byul, et al.
Published: (2025)

LLaDA-MoE: A Sparse MoE Diffusion Language Model
by: Zhu, Fengqi, et al.
Published: (2025)

GW-MoE: Resolving Uncertainty in MoE Router with Global Workspace Theory
by: Wu, Haoze, et al.
Published: (2024)

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

GazeFormer-MoE: Context-Aware Gaze Estimation via CLIP and MoE Transformer
by: Zhao, Xinyuan, et al.
Published: (2026)

DTop-p MoE: Sparsity-Controlled Dynamic Top-p MoE for Foundation Model Pre-training
by: Jin, Can, et al.
Published: (2025)

From Dense to Dynamic: Token-Difficulty Driven MoEfication of Pre-Trained LLMs
by: Nishu, Kumari, et al.
Published: (2025)

GRIN: GRadient-INformed MoE
by: Liu, Liyuan, et al.
Published: (2024)

KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
by: Xu, Zukang, et al.
Published: (2026)

Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization
by: Samragh, Mohammad, et al.
Published: (2024)

Mix-MoE: Improving Multilingual Machine Translation of Large Language Models through Mixed MoEs
by: Li, Bo, et al.
Published: (2026)

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

D$^{2}$MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving
by: Wang, Haodong, et al.
Published: (2025)

MoE-Lens: Towards the Hardware Limit of High-Throughput MoE LLM Serving Under Resource Constraints
by: Yuan, Yichao, et al.
Published: (2025)

Sigma-MoE-Tiny Technical Report
by: Hu, Qingguo, et al.
Published: (2025)

MemoryLLM: Plug-n-Play Interpretable Feed-Forward Memory for Transformers
by: Jaiswal, Ajay, et al.
Published: (2026)

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

SonicMoE: Accelerating MoE with IO and Tile-aware Optimizations
by: Guo, Wentao, et al.
Published: (2025)

Barriers for Learning in an Evolving World: Mathematical Understanding of Loss of Plasticity
by: Joudaki, Amir, et al.
Published: (2025)

Recursive Language Models Meet Uncertainty: The Surprising Effectiveness of Self-Reflective Program Search for Long Context
by: Alizadeh, Keivan, et al.
Published: (2026)

TIDE: Every Layer Knows the Token Beneath the Context
by: Jaiswal, Ajay, et al.
Published: (2026)

MoE-Loco: Mixture of Experts for Multitask Locomotion
by: Huang, Runhan, et al.
Published: (2025)

SpecMD: A Comprehensive Study On Speculative Expert Prefetching
by: Hoang, Duc, et al.
Published: (2026)

Exploiting the Experts: Unauthorized Compression in MoE-LLMs
by: Neogi, Pinaki Prasad Guha, et al.
Published: (2025)

(GG) MoE vs. MLP on Tabular Data
by: Chernov, Andrei
Published: (2025)

MiM-DiT: MoE in MoE with Diffusion Transformers for All-in-One Image Restoration
by: Kong, Lingshun, et al.
Published: (2026)

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge
by: Hu, Gang, et al.
Published: (2025)

MoESD: Unveil Speculative Decoding's Potential for Accelerating Sparse MoE
by: Huang, Zongle, et al.
Published: (2025)

MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learning
by: Yang, Shu, et al.
Published: (2024)

REAP the Experts: Why Pruning Prevails for One-Shot MoE compression
by: Lasby, Mike, et al.
Published: (2025)

Uni-MoE-2.0-Omni: Scaling Language-Centric Omnimodal Large Model with Advanced MoE, Training and Data
by: Li, Yunxin, et al.
Published: (2025)

Towards an empirical understanding of MoE design choices
by: Fan, Dongyang, et al.
Published: (2024)

MoEITS: A Green AI approach for simplifying MoE-LLMs
by: Balderas, Luis, et al.
Published: (2026)

ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model
by: Xu, Yuhao, et al.
Published: (2026)

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

MoE-Prefill: Zero Redundancy Overheads in MoE Prefill Serving
by: Su, Zhaoyuan, et al.
Published: (2026)

SecMoE: Communication-Efficient Secure MoE Inference via Select-Then-Compute
by: Shen, Bowen, et al.
Published: (2026)