  Over the past decade, deep learning (DL) has led to significant advancements
in various fields of artificial intelligence, including machine translation
(MT). These advancements would not be possible without the ever-growing volumes
of data and the hardware that allows large DL models to be trained efficiently.
Due to the large amount of computing cores as well as dedicated memory,
graphics processing units (GPUs) are a more effective hardware solution for
training and inference with DL models than central processing units (CPUs).
However, the former is very power demanding. The electrical power consumption
has economical as well as ecological implications.
  This chapter focuses on the ecological footprint of neural MT systems. It
starts from the power drain during the training of and the inference with
neural MT models and moves towards the environment impact, in terms of carbon
dioxide emissions. Different architectures (RNN and Transformer) and different
GPUs (consumer-grate NVidia 1080Ti and workstation-grade NVidia P100) are
compared. Then, the overall CO2 offload is calculated for Ireland and the
Netherlands. The NMT models and their ecological impact are compared to common
household appliances to draw a more clear picture.
  The last part of this chapter analyses quantization, a technique for reducing
the size and complexity of models, as a way to reduce power consumption. As
quantized models can run on CPUs, they present a power-efficient inference
solution without depending on a GPU.
