2 citations · 2 across the 2 of their papers we have counts for
4 papers
OpWeave: Flexible Operator Disaggregation for Heterogeneous LLM Serving
Zikun Li, Yixuan Mei, Shiqi Pan +9
LLM serving systems increasingly disaggregate inference into finer-grained stages, with recent approaches separating attention from FFN or MoE execution during decode. This operato…
Coral: Cost-Efficient Multi-LLM Serving over Heterogeneous Cloud GPUs
Yixuan Mei, Zikun Li, Zixuan Chen +5
The usage of large language models (LLMs) has grown increasingly fragmented, with no single model dominating. Meanwhile, cloud providers offer a wide range of mid-tier and older-ge…
Helix: Serving Large Language Models over Heterogeneous GPUs and Network via Max-Flow
Yixuan Mei, Yonghao Zhuang, Xupeng Miao +3
This paper introduces Helix, a distributed system for high-throughput, low-latency large language model (LLM) serving in heterogeneous GPU clusters. The key idea behind Helix is to…
Quarl: A Learning-Based Quantum Circuit Optimizer
Zikun Li, Jinjun Peng, Yixuan Mei +4
Optimizing quantum circuits is challenging due to the very large search space of functionally equivalent circuits and the necessity of applying transformations that temporarily dec…