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.

大型语言模型的碳足迹估算对于新的神经网络的训练前有重要意义，现有研究已报告了训练过程中的碳足迹，然而现有工具在估算碳足迹时存在一些严重限制，为填补这些空白，我们引入了 LLMCarbon，一个端到端的碳足迹预测模型，可以显著提高大型语言模型的碳足迹估算的准确性。