Measured Joules, Learned Routes: Learning to Route for Energy-Efficient LLM Serving
Large language models (LLMs) and agentic AI systems are creating rapidly growing inference energy demands as model sizes grow and reasoning trajectories extend. While in practice, many queries do not require the capabilities of the largest available model, and routinely directing such queries to a high-capability model can introduce unnecessary, considerable computation and energy consumption. In this paper, we investigate whether adaptive routing across a heterogeneous pool of LLMs can reduce this energy burden without substantially compromising task performance. We design a language-model-based router that reads in each query and selects an answer model from a fixed candidate pool. The ...
| Authors | Muhammad Abdur Rab Siddiqui, Daniela Rojas, Chen Yang et al. |
|---|---|
| Primary category | cs.PF |
| Published | 19 September 2026 |
| Last updated at source | 19 September 2026 |
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