5 papers
VisMMOE: Exploiting Visual-Expert Affinity for Efficient Visual-Language MoE Offloading
Cheng Xu, Xiaofeng Hou, Jiacheng Liu +1
Large-scale vision-language mixture-of-experts (VL-MoE) models provide strong multimodal capability, but efficient deployment on memory-constrained platforms remains difficult. Exi…
MoE-SpeQ: Speculative Quantized Decoding with Proactive Expert Prefetching and Offloading for Mixture-of-Experts
Wenfeng Wang, Jiacheng Liu, Xiaofeng Hou +5
The immense memory requirements of state-of-the-art Mixture-of-Experts (MoE) models present a significant challenge for inference, often exceeding the capacity of a single accelera…
MoE-Prism: Disentangling Monolithic Experts for Elastic MoE Services via Model-System Co-Designs
Xinfeng Xia, Jiacheng Liu, Xiaofeng Hou +5
Mixture-of-Experts (MoE) scales model capacity through sparse activation, and is becoming an important architecture for large language models (LLMs). However, existing MoE serving…
A Survey on Inference Optimization Techniques for Mixture of Experts Models
Jiacheng Liu, Peng Tang, Wenfeng Wang +5
The emergence of large-scale Mixture of Experts (MoE) models represents a significant advancement in artificial intelligence, offering enhanced model capacity and computational eff…
HOBBIT: A Mixed Precision Expert Offloading System for Fast MoE Inference
Peng Tang, Jiacheng Liu, Xiaofeng Hou +5
The Mixture-of-Experts (MoE) architecture has demonstrated significant advantages in the era of Large Language Models (LLMs), offering enhanced capabilities with reduced inference…