  Recent years have seen a surge in the popularity of commercial AI products
based on generative, multi-purpose AI systems promising a unified approach to
building machine learning (ML) models into technology. However, this ambition
of ``generality'' comes at a steep cost to the environment, given the amount of
energy these systems require and the amount of carbon that they emit. In this
work, we propose the first systematic comparison of the ongoing inference cost
of various categories of ML systems, covering both task-specific (i.e.
finetuned models that carry out a single task) and `general-purpose' models,
(i.e. those trained for multiple tasks). We measure deployment cost as the
amount of energy and carbon required to perform 1,000 inferences on
representative benchmark dataset using these models. We find that
multi-purpose, generative architectures are orders of magnitude more expensive
than task-specific systems for a variety of tasks, even when controlling for
the number of model parameters. We conclude with a discussion around the
current trend of deploying multi-purpose generative ML systems, and caution
that their utility should be more intentionally weighed against increased costs
in terms of energy and emissions. All the data from our study can be accessed
via an interactive demo to carry out further exploration and analysis.
