Smart Routing: Cost-Effective Multi-LLM Serving for Multi-Core AIOS

• URL: http://arxiv.org/abs/2502.20576v4
• Date: Wed, 02 Apr 2025 19:33:06 GMT
• Title: Smart Routing: Cost-Effective Multi-LLM Serving for Multi-Core AIOS
• Authors: Kai Mei, Wujiang Xu, Shuhang Lin, Yongfeng Zhang,
• Abstract summary: Existing scheduling frameworks mainly target at latency optimization.<n>This paper proposes an efficient capability-cost coordinated scheduling framework, ECCOS, for multi-LLM serving.
• Score: 31.60019342381251
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As large language models (LLMs) are increasingly deployed as service endpoints in systems, the surge in query volume creates significant scheduling challenges. Existing scheduling frameworks mainly target at latency optimization while neglecting the capability of LLMs to serve different level of queries, which could lead to computational resource waste. For example, those simple queries can be safely handled by small, fast and cheap LLMs, while those complex and difficult queries need to be handled by large, slow, and expensive LLMs. This paper addresses this challenge by proposing an efficient capability-cost coordinated scheduling framework, ECCOS, for multi-LLM serving, which explicitly constrains response quality and workload to optimize LLM inference cost. Specifically, it introduces the two-stage scheduling by designing a multi-objective predictor and a constrained optimizer. The predictor estimates both model capabilities and computational costs through training-based and retrieval-based approaches, while the optimizer determines cost-optimal assignments under quality and workload constraints. It also introduces QAServe, a dataset for sample-wise response quality and costs collected by zero-shot prompting different LLMs on knowledge QA and mathematical reasoning. Extensive experiments demonstrate that ECCOS improves success rates by 6.30% while reducing costs by 10.15% compared to existing methods, consuming less than 0.5% of LLM response time. The code is available at: https://github.com/agiresearch/ECCOS, and the proposed smart routing mechanism has been integrated into AIOS, the AI Agent Operating System, at https://github.com/agiresearch/AIOS.

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