Training foundation models consumes a signiﬁcant amount of energy, resulting in carbon dioxide emissions.
Patterson et al. (2021) propose a methodology to report an estimation of the carbon emitted during the
training of a model based on the speciﬁcs of the data center and its power grid. This computation informs
the design of the data center used for the training of models and the choice of location for data centers.
This methodology requires to know the speciﬁcs of the data center used for training, which can be complex
when multiple data centers are involved over time. Additionally, these speciﬁcs are most often not in the
control of the AI practitioner, and hence, this methodology is less helpful when practioners make technical
decisions about future trainings. Instead, in this section, we follow an alternative that reports the potential
carbon emission of retraining a similar model in an average data center located in the US. This methodology
DINOv2- Learning Robust Visual Features without Supervision