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

TL;DR

This paper explores the potential of using LLM benchmarks to estimate inference-related carbon emissions, proposing a new framework that offers a practical, non-intrusive alternative to existing energy monitoring tools.

Contribution

It introduces R-ICE, a novel benchmark-based framework for estimating LLM inference carbon emissions, addressing current limitations of high data requirements and intrusiveness.

Findings

Benchmark-based modelling shows promising accuracy for emission estimation.

R-ICE enables dynamic LLM routing and carbon accounting.

Validation results indicate potential for practical deployment.

Abstract

While Generative AI stands to be one of the fastest adopted technologies ever, studies have made evident that the usage of Large Language Models (LLMs) puts significant burden on energy grids and our environment. It may prove a hindrance to the Sustainability goals of any organization. A crucial step in any Sustainability strategy is monitoring or estimating the energy consumption of various components. While there exist multiple tools for monitoring energy consumption, there is a dearth of tools/frameworks for estimating the consumption or carbon emissions. Current drawbacks of both monitoring and estimation tools include high input data points, intrusive nature, high error margin, etc. We posit that leveraging emerging LLM benchmarks and related data points can help overcome aforementioned challenges while balancing accuracy of the emission estimations. To that extent, we discuss the challenges of current approaches and present our evolving framework, R-ICE, which estimates prompt level inference carbon emissions by leveraging existing state-of-the-art(SOTA) benchmark. This direction provides a more practical and non-intrusive way to enable emerging use-cases like dynamic LLM routing, carbon accounting, etc. Our promising validation results suggest that benchmark-based modelling holds great potential for inference emission estimation and warrants further exploration from the scientific community.