  In recent times, there has been definitive progress in the field of NLP, with
its applications growing as the utility of our language models increases with
advances in their performance. However, these models require a large amount of
computational power and data to train, consequently leading to large carbon
footprints. Therefore, it is imperative that we study the carbon efficiency and
look for alternatives to reduce the overall environmental impact of training
models, in particular large language models. In our work, we assess the
performance of models for machine translation, across multiple language pairs
to assess the difference in computational power required to train these models
for each of these language pairs and examine the various components of these
models to analyze aspects of our pipeline that can be optimized to reduce these
carbon emissions.
