Cambridge Computer and NVIDIA teamed to lead a seminar last week at Carnegie Mellon University to discuss how new HPC and GPU architectures impact research computing in higher ed. We had a full room plus a bunch of people attending remotely. There was some lively debate and a lot of great ideas shared. We gave our own version of the NVIDIA GTC recap and led a discussion on the new chip architectures (Grace, Hopper, Blackwell). We also had presentations from Nvidia on the various products and services that they offer that are lesser known to their client base. This was a great chance for different stakeholders in the university to get together in person and bat around ideas, common challenges, etc.

Cambridge and NVIDIA had a full house at Harvard Med for the seminar entitled AI Futures at Harvard Medical School. GPU in EDU is a seminar series co-produced by NVIDIA and Cambridge Computer. We travel to universities and research institutions across the country to share ideas and insights related to AI and ML at the intersection of science and IT infrastructure. The content of each session is tailored to the specific interests of the institution hosting the event. Harvard Med chose to focus on deep learning for genomics, NVIDIA software frameworks, and GPU resource management. We'd like to thank our friends at Harvard, Run:ai, and NVIDIA for making this event possible. Their support and collaboration were instrumental in making it a memorable and informative event. Special thanks for Run:ai for co-sponsoring the event and sharing insights into their amazing technology for pooling and scheduling GPU resources. We'd also like to thank Christos Alexiadis (Cambridge) and Eliot Eshelman (NVIDIA) for all their work behind the scenes.