# CO2-Meter: A Comprehensive Carbon Footprint Estimator for LLMs on Edge Devices

arXiv: https://arxiv.org/abs/2511.08575 · HTML: https://mlsanity.com/paper/2511.08575/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2025.

## Built on by (most cited first, 1 of 1)

- [LLMSpace: Carbon Footprint Modeling for Large Language Model Inference on LEO Satellites](https://mlsanity.com/paper/2605.05615/) (2026)

## Abstract (arXiv)

LLMs have transformed NLP, yet deploying them on edge devices poses great carbon challenges. Prior estimators remain incomplete, neglecting peripheral energy use, distinct prefill/decode behaviors, and SoC design complexity. This paper presents CO2-Meter, a unified framework for estimating operational and embodied carbon in LLM edge inference. Contributions include: (1) equation-based peripheral energy models and datasets; (2) a GNN-based predictor with phase-specific LLM energy data; (3) a unit-level embodied carbon model for SoC bottleneck analysis; and (4) validation showing superior accuracy over prior methods. Case studies show CO2-Meter's effectiveness in identifying carbon hotspots and guiding sustainable LLM design on edge platforms. Source code: https://github.com/fuzhenxiao/CO2-Meter