Unveiling the Uncertainty in Embodied and Operational Carbon of Large AI Models through a Probabilistic Carbon Accounting Model
Xiaoyang Zhang, He Fang, Yang Deng, Dan Wang
The rapid growth of large AI models has raised significant environmental concerns due to their substantial carbon footprint. Existing carbon accounting methods for AI models are fundamentally deterministic and fail to account for inherent uncertainties in embodied and operational carbon emissions. Our work aims to investigate the effect of these uncertainties on embodied and operational carbon footprint estimates for large AI models. We propose a Probabilistic Carbon Accounting Model (PCAM), which quantifies uncertainties across three dimensions in the carbon accounting for AI models: geotemporal manufacturing variability, dynamic manufacturing evolution, and dynamic operating context. We develop parameter models to quantify key components (processors, memory, storage) in the carbon footprint of AI models. To characterize the distribution of the parameters, we develop a carbon dataset by aggregating related data from various sources. Then, we implement a simple yet effective dual-stage distribution modeling to generate the distribution of the parameters from the collected dataset through Kernel Density Estimate (KDE). We compare the performance of PCAM with LLMCarbon, the state-of-the-art carbon accounting method for large AI models. PCAM achieves $\leq7.44$% error compared to LLMCarbon’s $\leq108.51$%.