  Human-produced emissions are growing at an alarming rate, causing already
observable changes in the climate and environment in general. Each year global
carbon dioxide emissions hit a new record, and it is reported that 0.5% of
total US greenhouse gas emissions are attributed to data centres as of 2021.
The release of ChatGPT in late 2022 sparked social interest in Large Language
Models (LLMs), the new generation of Language Models with a large number of
parameters and trained on massive amounts of data. Currently, numerous
companies are releasing products featuring various LLMs, with many more models
in development and awaiting release. Deep Learning research is a competitive
field, with only models that reach top performance attracting attention and
being utilized. Hence, achieving better accuracy and results is often the first
priority, while the model's efficiency and the environmental impact of the
study are neglected. However, LLMs demand substantial computational resources
and are very costly to train, both financially and environmentally. It becomes
essential to raise awareness and promote conscious decisions about algorithmic
and hardware choices. Providing information on training time, the approximate
carbon dioxide emissions and power consumption would assist future studies in
making necessary adjustments and determining the compatibility of available
computational resources with model requirements. In this study, we infused T5
LLM with external knowledge and fine-tuned the model for Question-Answering
task. Furthermore, we calculated and reported the approximate environmental
impact for both steps. The findings demonstrate that the smaller models may not
always be sustainable options, and increased training does not always imply
better performance. The most optimal outcome is achieved by carefully
considering both performance and efficiency factors.
