Yinmin Zhong
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| title | conference | date |
|---|---|---|
| FastServe: Iteration-Level Preemptive Scheduling for Large Language Model Inference | NSDI '26 | 2026-05-04 |
FastServe implements iteration-level preemption for autoregressive LLMs, letting the system preempt after each token instead of running to completion. It… | ||
| DistRS: Disaggregated Reward Service for RLVR with Batch-Level Constraint | NSDI '26 | 2026-05-04 |
DistRS is a disaggregated reward service that separates reward computation from training, implements a microservice pipeline with per-stage worker pools, and… | ||
| Optimizing RLHF Training for Large Language Models with Stage Fusion | NSDI '25 | 2025-04-28 |
RLHFuse claims up to 3.7× end-to-end RLHF training throughput by splitting tasks into sample-level subtasks and microbatch subtasks. It overlaps generation and… | ||
| DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language... | 18th USENIX Symposium on Operating Systems Design and Implementation (OSDI '24) | 2024-07-10 |
System disaggregates prefill and decoding onto separate GPUs, assigns independent parallelism and resource plans for each phase, and places instances to cut… | ||
| MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs | 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI '24) | 2024-04-16 |
MegaScale is a production system that trains LLMs on more than 10,000 GPUs and achieves concrete gains: 55.2% Model FLOPs Utilization when training a 175B… | ||
| AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving | OSDI 2023 | 2023-07-10 |
AlpaServe uses model parallelism not just to fit big models but to statistically multiplex GPUs across many models, trading partition overhead for burst… | ||
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