  As opportunities for AI-assisted healthcare grow steadily, model deployment
faces challenges due to the specific characteristics of the industry. The
configuration choice for a production device can impact model performance while
influencing operational costs. Moreover, in healthcare some situations might
require fast, but not real time, inference. We study different configurations
and conduct a cost-performance analysis to determine the optimized hardware for
the deployment of a model subject to healthcare domain constraints. We observe
that a naive performance comparison may not lead to an optimal configuration
selection. In fact, given realistic domain constraints, CPU execution might be
preferable to GPU accelerators. Hence, defining beforehand precise expectations
for model deployment is crucial.
