UNDERSTANDING MOE INFERENCE ENERGY CONSUMPTION VIA EXPERT ACTIVATION TRAJECTORY ANALYSIS

Large language model inference incurs substantial operational cost because deployed models must serve requests continuously over long periods. Existing work often analyzes request-level inference energy as an aggregate GPU-level measurement. This black-box view is limited for Mixture-of-Experts (MoE) models, where different inputs dynamically activate different experts and form different internal computation paths. This paper studies the inference energy of a Mixtral-style MoE model through a mechanism-oriented lens. We decompose MoE inference into an input-routing-output-energy chain: input prompts induce prefill expert activation trajectories, these trajectories predict later output behavior, output behavior directly drives decode energy, deployment configuration changes the hardware energy coefficient, and correctness determines whether the consumed energy is useful. Across request-level energy traces, reconstructed expert routing features, deployment comparisons, full benchmark correctness results, and expert intervention experiments, we find that output behavior is the strongest direct driver of decode energy; prefill expert activation substantially improves output prediction; expert importance cannot be captured by activation frequency alone; deployment configuration changes energy consumption even when output behavior and correctness are nearly unchanged; and a small number of experts have disproportionate effects on output quality under intervention. These results suggest that MoE inference energy is not merely a single aggregate energy number, but an interpretable and potentially optimizable process shaped by routing, output behavior, deployment, and correctness.