9 citations · 11 across the 12 of their papers we have counts for
8 papers · 1 filter
Specter: Fast MoE Inference via Quantization-Enabled Multi-Step Speculative Offloading
Wenfeng Wang, Jiacheng Liu, Xiaofeng Hou +7
Mixture-of-experts (MoE) models often exceed a single accelerator's memory capacity. Offloading experts to host memory creates a severe PCIe I/O bottleneck because data-dependent r…
MoE-Prism: Disentangling Monolithic Experts for Elastic MoE Services via Model-System Co-Designs
Xinfeng Xia, Xiaofeng Hou, Jiacheng Liu +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…
Boosting Embodied AI Agents through Perception-Generation Disaggregation and Asynchronous Pipeline Execution
Shulai Zhang, Ao Xu, Quan Chen +6
Embodied AI systems operate in dynamic environments, requiring seamless integration of perception and generation modules to process high-frequency input and output demands. Traditi…
Kairos: Low-latency Multi-Agent Serving with Shared LLMs and Excessive Loads in the Public Cloud
Jinyuan Chen, Jiuchen Shi, Quan Chen +1
Multi-agent applications utilize the advanced capabilities of large language models (LLMs) for intricate task completion through agent collaboration in a workflow. Under this situa…
Towards Resource-Efficient Serverless LLM Inference with SLINFER
Chuhao Xu, Zijun Li, Quan Chen +3
The rise of LLMs has driven demand for private serverless deployments, characterized by moderate-sized models and infrequent requests. While existing serverless solutions follow ex…
Survey of Disaggregated Memory: Cross-layer Technique Insights for Next-Generation Datacenters
Jing Wang, Chao Li, Taolei Wang +4
The growing scale of data requires efficient memory subsystems with large memory capacity and high memory performance. Disaggregated architecture has become a promising solution fo…
Efficient Function-as-a-Service for Large Language Models with TIDAL
Weihao Cui, Ziyi Xu, Han Zhao +4
Large Language Model (LLM) applications have emerged as a prominent use case for Function-as-a-Service (FaaS) due to their high computational demands and sporadic invocation patter…
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…