  In open-domain question answering (QA), retrieve-and-read mechanism has the
inherent benefit of interpretability and the easiness of adding, removing, or
editing knowledge compared to the parametric approaches of closed-book QA
models. However, it is also known to suffer from its large storage footprint
due to its document corpus and index. Here, we discuss several orthogonal
strategies to drastically reduce the footprint of a retrieve-and-read
open-domain QA system by up to 160x. Our results indicate that
retrieve-and-read can be a viable option even in a highly constrained serving
environment such as edge devices, as we show that it can achieve better
accuracy than a purely parametric model with comparable docker-level system
size.
