  The carbon footprint associated with large language models (LLMs) is a
significant concern, encompassing emissions from their training, inference,
experimentation, and storage processes, including operational and embodied
carbon emissions. An essential aspect is accurately estimating the carbon
impact of emerging LLMs even before their training, which heavily relies on GPU
usage. Existing studies have reported the carbon footprint of LLM training, but
only one tool, mlco2, can predict the carbon footprint of new neural networks
prior to physical training. However, mlco2 has several serious limitations. It
cannot extend its estimation to dense or mixture-of-experts (MoE) LLMs,
disregards critical architectural parameters, focuses solely on GPUs, and
cannot model embodied carbon footprints. Addressing these gaps, we introduce
\textit{LLMCarbon}, an end-to-end carbon footprint projection model designed
for both dense and MoE LLMs. Compared to mlco2, LLMCarbon significantly
enhances the accuracy of carbon footprint estimations for various LLMs.

Computation and Language;Artificial Intelligence;Computers and Society;Machine Learning