Staggered Batch Scheduling: Co-optimizing Time-to-First-Token and Throughput for High-Efficiency LLM Inference

TL;DR

This paper introduces Staggered Batch Scheduling (SBS), a novel approach that buffers requests to optimize batch formation, significantly reducing Time-to-First-Token and increasing throughput in large-scale LLM inference systems.

Contribution

The paper presents SBS, a new scheduling mechanism that buffers requests to improve LLM inference efficiency, and a load-aware global allocation strategy for better load balancing.

Findings

Reduces TTFT by 30%-40%

Improves throughput by 15%-20%

Effective in large-scale distributed LLM serving environments

Abstract

The evolution of Large Language Model (LLM) serving towards complex, distributed architectures--specifically the P/D-separated, large-scale DP+EP paradigm--introduces distinct scheduling challenges. Unlike traditional deployments where schedulers can treat instances as black boxes, DP+EP architectures exhibit high internal synchronization costs. We identify that immediate request dispatching in such systems leads to severe in-engine queuing and parallelization bubbles, degrading Time-to-First-Token (TTFT). To address this, we propose Staggered Batch Scheduling (SBS), a mechanism that deliberately buffers requests to form optimal execution batches. This temporal decoupling eliminates internal queuing bubbles without compromising throughput. Furthermore, leveraging the scheduling window created by buffering, we introduce a Load-Aware Global Allocation strategy that balances computational load across DP units for both Prefill and Decode phases. Deployed on a production H800 cluster serving Deepseek-V3, our system reduces TTFT by 30%-40% and improves throughput by 15%-20% compared to state-of-the-art immediate scheduling baselines.