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arXiv · 2504.08784

SLOs-Serve: Optimized Serving of Multi-SLO LLMs

Siyuan Chen·Zhipeng Jia·Samira Khan·Arvind Krishnamurthy·Phillip B. Gibbons
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Abstract

This paper introduces SLOs-Serve, a system designed for serving multi-stage large language model (LLM) requests with application- and stage-specific service level objectives (SLOs). The key idea behind SLOs-Serve is to customize the allocation of tokens to meet these SLO requirements. SLOs-Serve uses a multi-SLO dynamic programming-based algorithm to continuously optimize token allocations under SLO constraints by exploring the full design space of chunked prefill and (optional) speculative decoding. Leveraging this resource planning algorithm, SLOs-Serve effectively supports multi-SLOs and multi-replica serving with dynamic request routing while being resilient to bursty arrivals. Our evaluation across 6 LLM application scenarios (including summarization, coding, chatbot, tool calling, and reasoning) demonstrates that SLOs-Serve improves per-GPU serving capacity by 2.2x on average compared to prior state-of-the-art systems.

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Siyuan Chen, Zhipeng Jia, Samira Khan, Arvind Krishnamurthy, Phillip B. Gibbons. 2025-04-05. SLOs-Serve: Optimized Serving of Multi-SLO LLMs. https://arxiv.org/abs/2504.08784

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