Prompt-Aware Scheduling for Low-Latency LLM Serving

• URL: http://arxiv.org/abs/2510.03243v2
• Date: Fri, 10 Oct 2025 04:42:42 GMT
• Title: Prompt-Aware Scheduling for Low-Latency LLM Serving
• Authors: Yiheng Tao, Yihe Zhang, Matthew T. Dearing, Xin Wang, Yuping Fan, Zhiling Lan,
• Abstract summary: We introduce PARS, a prompt-aware LLM task scheduler.<n>It approximats shortest-job-first (SJF) scheduling through pairwise ranking with margin ranking loss.<n>It effectively predicts response-length-based task ordering, reducing latency with minimal overhead.
• Score: 4.410280212028576
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficient scheduling of LLM inference tasks is essential for achieving low latency and high throughput, particularly with the growing use of reasoning-capable LLMs. Traditional strategies like First-Come-First-Serve (FCFS) often suffer from Head-of-Line (HOL) blocking, where long-running tasks delay shorter ones queued behind them. In this paper, we introduce PARS, a prompt-aware LLM task scheduler that improves serving efficiency by approximating shortest-job-first (SJF) scheduling through pairwise ranking with margin ranking loss. PARS focuses on impactful scheduling decisions and is seamlessly integrated into the state-of-the-art LLM serving system vLLM. It effectively predicts response-length-based task ordering, reducing latency with minimal overhead. Extensive experiments across multiple LLMs and real-world inference datasets show that PARS significantly improves performance, including for reasoning workloads. Furthermore, our cross-model evaluations demonstrate that the design generalizes well, enabling effective scheduling even when predictors are trained on different LLMs.

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