  The energy requirements of current natural language processing models
continue to grow at a rapid, unsustainable pace. Recent works highlighting this
problem conclude there is an urgent need for methods that reduce the energy
needs of NLP and machine learning more broadly. In this article, we investigate
techniques that can be used to reduce the energy consumption of common NLP
applications. In particular, we focus on techniques to measure energy usage and
different hardware and datacenter-oriented settings that can be tuned to reduce
energy consumption for training and inference for language models. We
characterize the impact of these settings on metrics such as computational
performance and energy consumption through experiments conducted on a high
performance computing system as well as popular cloud computing platforms.
These techniques can lead to significant reduction in energy consumption when
training language models or their use for inference. For example,
power-capping, which limits the maximum power a GPU can consume, can enable a
15\% decrease in energy usage with marginal increase in overall computation
time when training a transformer-based language model.
