On-premise AI: what it actually takes on a campus

On-premise AI is achievable on a normal engineering campus, and it is not free. The honest version of the conversation covers four things: compute, power and cooling, network, and operations.

Compute is sized against concurrent usage, not headcount. A department pilot with a few hundred daily users needs far less than most vendors imply. Institution-wide rollouts are where sizing discipline matters.

Power and cooling is where most campus plans fail. A rack with sustained load needs conditioned power and a cooling budget that the existing server room may not have. This is a facilities decision as much as an IT one.

Network matters because retrieval is chatty. Keeping the index and the model on the same segment removes most latency complaints before they start.

Operations is the part institutions underestimate: patching, monitoring, backups, and someone accountable at two in the morning. This is exactly why hybrid is often the right first step — knowledge and retrieval stay on campus, elastic capacity sits in a private cloud, and the institution builds operational maturity before taking on the full stack.