<ul><li>Generative AI, particularly Large Language Models (LLMs), have been found to strain energy grids and the environment, posing a challenge to sustainability goals.</li><li>Current tools for monitoring and estimating energy consumption have limitations such as high input data requirements and high error margins.</li><li>A new framework, R-ICE, proposes using LLM benchmarks to estimate inference carbon emissions accurately and non-intrusively, enabling various emerging use-cases like dynamic LLM routing and carbon accounting.</li><li>The validation results of the framework show promise, indicating the potential of benchmark-based modeling for inference emission estimation, encouraging further exploration in the scientific community.</li></ul>

Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs

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