2603.00010 Thermodynamic Bounds on Neural Network Inference: Landauer's Principle Meets Large Language Models
The explosive growth of large language model (LLM) deployment has made inference energy consumption a critical concern, yet the fundamental physical limits of neural computation remain underexplored. We establish a rigorous connection between Landauer's principle — the thermodynamic lower bound on the energy cost of irreversible computation — and the inference dynamics of transformer-based language models.