Energy-Aware Routing to Large Reasoning Models

2025-12-23

arXiv.org

AI Summary

This work addresses the challenge of balancing energy supply and stochastic fluctuations in scheduling heterogeneous large inference models. It proposes a variance-aware routing strategy that optimizes model selection and execution under critical operating conditions to minimize system energy consumption. For the first time, a variance-absorption mechanism is integrated into model routing design, leveraging second-order performance characteristics within critical energy consumption intervals to establish a theoretical foundation for energy-aware scheduling. By combining computational scaling laws, stochastic process modeling, and critical systems theory, the study reveals fundamental principles governing how system performance is constrained by energy variability, thereby offering quantifiable design guidelines for energy-efficient large-model scheduling.

Abstract

Large reasoning models (LRMs) have heterogeneous inference energy costs based on which model is used and how much it reasons. To reduce energy, it is important to choose the right LRM and operate it in the right way. As a result, the performance of systems that dispatch tasks to different individual LRMs depend on the balance between mean energy provisioning and stochastic fluctuations. The critical regime is the unique operating point at which neither auxiliary energy nor baseline energy is systematically wasted. Increasing baseline supply shifts the system toward persistent over-supply and baseline-energy waste, while reducing supply induces persistent reliance on auxiliary energy. Yet in this regime, performance remains volatility-limited and so a second-order characterization provides further insights that we develop. Here, performance is governed by how variability is absorbed across time, models, and execution choices. This perspective highlights variance-aware routing and dispatch as a principled design axis, and provides a theoretical basis for developing energy-aware model routing policies. Routing behavior is characterized when dispatch policies are based on training-compute and inference-compute scaling laws for LRMs.

Problem

• energy-aware routing
• large reasoning models
• energy provisioning
• stochastic fluctuations
• variance-aware dispatch

Innovation

• energy-aware routing
• large reasoning models
• critical regime
• variance-aware dispatch
• scaling laws