Universal Model Routing for Efficient LLM Inference

• URL: http://arxiv.org/abs/2502.08773v2
• Date: Tue, 22 Jul 2025 15:27:33 GMT
• Title: Universal Model Routing for Efficient LLM Inference
• Authors: Wittawat Jitkrittum, Harikrishna Narasimhan, Ankit Singh Rawat, Jeevesh Juneja, Congchao Wang, Zifeng Wang, Alec Go, Chen-Yu Lee, Pradeep Shenoy, Rina Panigrahy, Aditya Krishna Menon, Sanjiv Kumar,
• Abstract summary: Model routing is a technique for reducing the inference cost of large language models (LLMs)<n>We propose UniRoute, a new approach to the problem of dynamic routing, where new, previously unobserved LLMs are available at test time.<n>We show that these are estimates of a theoretically optimal routing rule, and quantify their errors via an excess risk bound.
• Score: 69.86195589350264
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model routing is a simple technique for reducing the inference cost of large language models (LLMs), wherein one maintains a pool of candidate LLMs, and learns to route each prompt to the smallest feasible LLM. Existing works focus on learning a router for a fixed pool of LLMs. In this paper, we consider the problem of dynamic routing, where new, previously unobserved LLMs are available at test time. We propose UniRoute, a new approach to this problem that relies on representing each LLM as a feature vector, derived based on predictions on a set of representative prompts. Based on this, we detail two effective instantiations of UniRoute, relying on cluster-based routing and a learned cluster map respectively. We show that these are estimates of a theoretically optimal routing rule, and quantify their errors via an excess risk bound. Experiments on a range of public benchmarks show the effectiveness of UniRoute in routing amongst more than 30 unseen LLMs.

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