  In recent years, large-scale auto-regressive models have made significant
progress in various tasks, such as text or video generation. However, the
environmental impact of these models has been largely overlooked, with a lack
of assessment and analysis of their carbon footprint. To address this gap, we
introduce OpenCarbonEval, a unified framework for integrating large-scale
models across diverse modalities to predict carbon emissions, which could
provide AI service providers and users with a means to estimate emissions
beforehand and help mitigate the environmental pressure associated with these
models. In OpenCarbonEval, we propose a dynamic throughput modeling approach
that could capture workload and hardware fluctuations in the training process
for more precise emissions estimates. Our evaluation results demonstrate that
OpenCarbonEval can more accurately predict training emissions than previous
methods, and can be seamlessly applied to different modal tasks. Specifically,
we show that OpenCarbonEval achieves superior performance in predicting carbon
emissions for both visual models and language models. By promoting sustainable
AI development and deployment, OpenCarbonEval can help reduce the environmental
impact of large-scale models and contribute to a more environmentally
responsible future for the AI community.
