Nvidia licensed technology from Groq and absorbed much of its team as the company hedges on inference—the stage of AI that turns models into recurring services. Nvidia executives framed inference as the sector’s key profit center and said it already accounts for a large share of AI revenue. For universities and research centers, the move signals that procurement choices will increasingly hinge on inference economics: institutions running public‑facing AI tools or campus chatbots must balance latency, throughput and cost. Campus computing centers will need to reassess GPU/accelerator mixes, vendor lock‑in risks, and long‑term budgets as chip specialization intensifies. Academic researchers should expect fresher hardware options for inference experiments, but also a more complex vendor landscape that complicates reproducibility and long‑term access to specific accelerator architectures.