Cover Image

MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cao, Shiyi, Liu, Shu, Griggs, Tyler, Schafhalter, Peter, Liu, Xiaoxuan, Sheng, Ying, Gonzalez, Joseph E., Zaharia, Matei, Stoica, Ion
Format:	Preprint
Published:	2024
Subjects:	Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

OD-MoE: On-Demand Expert Loading for Cacheless Edge-Distributed MoE Inference
by: Wang, Liujianfu, et al.
Published: (2025)

MoE-Gen: High-Throughput MoE Inference on a Single GPU with Module-Based Batching
by: Xu, Tairan, et al.
Published: (2025)

SP-MoE: Speculative Decoding and Prefetching for Accelerating MoE-based Model Inference
by: Chen, Liangkun, et al.
Published: (2025)

MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model?
by: Ma, Songkai, et al.
Published: (2025)

GRACE-MoE: Grouping and Replication with Locality-Aware Routing for Efficient Distributed MoE Inference
by: Han, Yu, et al.
Published: (2025)

LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing
by: Nie, Xiaonan, et al.
Published: (2024)

EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference
by: Qian, Yulei, et al.
Published: (2024)

MoEBlaze: Breaking the Memory Wall for Efficient MoE Training on Modern GPUs
by: Zhang, Jiyuan, et al.
Published: (2026)

Accelerating Distributed MoE Training and Inference with Lina
by: Li, Jiamin, et al.
Published: (2022)

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

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs
by: Zhang, Qijun, et al.
Published: (2026)

HarMoEny: Efficient Multi-GPU Inference of MoE Models
by: Doucet, Zachary, et al.
Published: (2025)

Staleness-Centric Optimizations for Parallel Diffusion MoE Inference
by: Luo, Jiajun, et al.
Published: (2024)

Janus: Disaggregating Attention and Experts for Scalable MoE Inference
by: Zhang, Zhexiang, et al.
Published: (2025)

MPipeMoE: Memory Efficient MoE for Pre-trained Models with Adaptive Pipeline Parallelism
by: Zhang, Zheng, et al.
Published: (2025)

Efficient CPU-GPU Collaborative Inference for MoE-based LLMs on Memory-Limited Systems
by: Huang, En-Ming, et al.
Published: (2025)

Surviving Partial Rank Failures in Wide Expert-Parallel MoE Inference
by: Sun, Xun, et al.
Published: (2026)

ReviveMoE: Fast Recovery for Hardware Failures in Large-Scale MoE LLM Inference Deployments
by: Li, Haley, et al.
Published: (2026)

MemFine: Memory-Aware Fine-Grained Scheduling for MoE Training
by: Zhao, Lu, et al.
Published: (2025)

eMoE: Task-aware Memory Efficient Mixture-of-Experts-Based (MoE) Model Inference
by: Tairin, Suraiya, et al.
Published: (2025)

ReaLB: Real-Time Load Balancing for Multimodal MoE Inference
by: Wang, Yingping, et al.
Published: (2026)

Sparse Checkpointing for Fast and Reliable MoE Training
by: Gandhi, Swapnil, et al.
Published: (2024)

EC2MoE: Adaptive End-Cloud Pipeline Collaboration Enabling Scalable Mixture-of-Experts Inference
by: Yang, Zheming, et al.
Published: (2025)

PROBE: Co-Balancing Computation and Communication in MoE Inference via Real-Time Predictive Prefetching
by: Zhu, Qianchao, et al.
Published: (2026)

MoE Parallel Folding: Heterogeneous Parallelism Mappings for Efficient Large-Scale MoE Model Training with Megatron Core
by: Liu, Dennis, et al.
Published: (2025)

Accelerating Edge Inference for Distributed MoE Models with Latency-Optimized Expert Placement
by: Wu, Tian, et al.
Published: (2025)

Making MoE-based LLM Inference Resilient with Tarragon
by: Zhang, Songyu, et al.
Published: (2026)

Remoe: Towards Efficient and Low-Cost MoE Inference in Serverless Computing
by: Liu, Wentao, et al.
Published: (2025)

Fine-grained MoE Load Balancing with Linear Programming
by: Zhao, Chenqi, et al.
Published: (2025)

Multi-Layer Scheduling for MoE-Based LLM Reasoning
by: Sun, Yifan, 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)

Revealing the Challenges of Attention-FFN Disaggregation for Modern MoE Models and Hardware Systems
by: Liu, Guowei, et al.
Published: (2026)

TriMoE: Augmenting GPU with AMX-Enabled CPU and DIMM-NDP for High-Throughput MoE Inference via Offloading
by: Pan, Yudong, et al.
Published: (2026)

HybriMoE: Hybrid CPU-GPU Scheduling and Cache Management for Efficient MoE Inference
by: Zhong, Shuzhang, et al.
Published: (2025)

Hexa-MoE: Efficient and Heterogeneous-aware Training for Mixture-of-Experts
by: Luo, Shuqing, et al.
Published: (2024)

MoE-Hub: Taming Software Complexity for Seamless MoE Overlap with Hardware-Accelerated Communication on Multi-GPU Systems
by: Zhou, Zhuoshan, et al.
Published: (2026)

ExpertFlow: Adaptive Expert Scheduling and Memory Coordination for Efficient MoE Inference
by: Shen, Zixu, et al.
Published: (2025)

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

FEPLB: Exploiting Copy Engines for Nearly Free MoE Load Balancing in Distributed Training
by: Qi, Shuyao, et al.
Published: (2026)

DuoServe-MoE: Dual-Phase Expert Prefetch and Caching for LLM Inference QoS Assurance
by: Zhang, Yuning, et al.
Published: (2025)