The GPUs, sure. The mainboards and CPUs can be used in clusters for general-purpose computing, which is still more prevalent in most scientific research as far as I am aware. My alma mater has a several-thousand-core cluster that any student can request time on as long as they have reason to do so, and it's all CPU compute. Getting non-CS majors to write GPU code is unlikely in that scenario.
> Getting non-CS majors to write GPU code is unlikely in that scenario.
People mostly use a GPU-enabled liblaplac. Physics, chemistry, biology, and medicine departments can absolutely use the GPUs.
I provide infrastructure for such a cluster that is also available to anyone at the university free of charge. Every year we swap out the oldest 20% of the cluster as we run a five year depreciation schedule. In the last three years, we’ve mostly been swapping in GPU resources at a ration of about 3:1. That’s in response to both usage reports and community surveys.