Yinmin Zhong

Ph.D. Student

Peking University

zhongyinmin [at] pku.edu.cn

About Me

I am a third-year Ph.D. student studying computer science in the Computer Systems Research Group at Peking University where I am advised by Xin Jin. Before that, I received my B.S. in Computer Science from Peking University.

I have a broad interest in building efficient systems for training and serving deep learning models, with a primary focus on large language models (LLMs) currently.

I am also an enthusiastic self-learner and interested in various fields of computer science. I have built a website to share my self-learning experiences and resources.

Interests

Machine Learning Systems
Distributed Systems
Large Language Models

Education

Peking University
Ph.D. in Computer Science, Sep 2022 - Present
Peking University
B.S. in Computer Science, Sep 2018 - June 2022

Experience

StepFun System Team
Research Intern, June 2024 - Present
ByteDance AML Team
Research Intern, Aug 2023 - May 2024
Alibaba DAMO Academy
Research Intern, Sep 2021 - Sep 2022
AI Innovation Center, Peking University
Software Engineer Intern, Sep 2020 - Mar 2021

Publications

RLHFuse: Efficient RLHF Training for Large Language Models with Inter- and Intra-Stage Fusion.

Yinmin Zhong, Zili Zhang, Bingyang Wu, Shengyu Liu, Yukun Chen, Changyi Wan, Hanpeng Hu, Lei Xia, Ranchen Ming, Yibo Zhu, Xin Jin

In NSDI 2025. PDF Cite

DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving.

Yinmin Zhong, Shengyu Liu, Junda Chen, Jianbo Hu, Yibo Zhu, Xuanzhe Liu, Xin Jin, Hao Zhang

In OSDI 2024. PDF Cite Code Slides Video

DistTrain: Addressing Model and Data Heterogeneity with Disaggregated Training for Multimodal Large Language Models.

Zili Zhang, Yinmin Zhong, Ranchen Ming, Hanpeng Hu, Jianjian Sun, Zheng Ge, Yibo Zhu, Xin Jin

In Preprint. PDF Cite

LoongServe: Efficiently Serving Long-context Large Language Models with Elastic Sequence Parallelism.

Bingyang Wu, Shengyu Liu, Yinmin Zhong, Peng Sun, Xuanzhe Liu, Xin Jin

In SOSP 2024. PDF Cite

MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs.

Ziheng Jiang, Haibin Lin, Yinmin Zhong, Qi Huang, Yangrui Chen, Zhi Zhang, Yanghua Peng, Xiang Li, Cong Xie, Shibiao Nong, Yulu Jia, Sun He, Hongmin Chen, Zhihao Bai, Qi Hou, Shipeng Yan, Ding Zhou, Yiyao Sheng, Zhuo Jiang, Haohan Xu, Haoran Wei, Zhang Zhang, Pengfei Nie, Leqi Zou, Sida Zhao, Liang Xiang, Zherui Liu, Zhe Li, Xiaoying Jia, Jianxi Ye, Xin Jin, Xin Liu

In NSDI 2024. PDF Cite Code Slides Video

Aquifer: Transparent Microsecond-scale Scheduling for vRAN Workloads.

Yunshan Jia, Yinmin Zhong, Meng Wang, Jiaqi Gao, Pengyu Zhang, Xuanzhe Liu, Xin Jin

In TSC 2024. PDF Cite

DistMind: Efficient Resource Disaggregation for Deep Learning Workloads.

Xin Jin, Zhihao Bai, Zhen Zhang, Yibo Zhu, Yinmin Zhong, Xuanzhe Liu

In TON 2024. PDF Cite

Fast Distributed Inference Serving for Large Language Models.

Bingyang Wu, Yinmin Zhong, Zili Zhang, Gang Huang, Xuanzhe Liu, Xin Jin

In Preprint. PDF Cite

AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving.