GPU in EDU

Overview Presentations and discussions about AI and data science in research and education. The morning session will focus on NVIDIA overviews and topics of particular concern to IT Professionals. The afternoon will focus on Researcher workloads with additional presentations from NVIDIA, Cambridge, and CUIT as well as opportunities for open discussion. All are welcome to join throughout the day and to come and go as your schedule allows! Room Location: Davis Auditorium, Schapiro CEPSR About Cambridge Cambridge Computer provides IT infrastructure solutions with a focus on the needs of higher education and research computing. We work with our clients and vendors to specify, sell, and deploy hardware. Projects are as simple as individual workstations and as complex as HPC/GPU compute clusters. About NVIDIA We pioneered accelerated computing to tackle challenges no one else can solve. With NVIDIA, healthcare institutions can harness the power of AI and high-performance computing to define the future of medicine. NVIDIA engineers the most advanced chips, systems, and software for the AI factories of the future. We build new AI services that help institutions create their own AI factories.

Overview Presentations and discussions about HPC & AI infrastructure for science and higher education The day will feature a mix of presentations from the Cambridge and Dartmouth teams, along with open discussions with attendees. Everyone is welcome to join the Happy Hour at the end of the event at Murphy's! Agenda 1:00 PM - 2:00 PM — Goldstein 105 Occom Commons (including lunch) Roadmap - NVIDIA Products & Highlights 2025 - Cambridge & NVIDIA Discussion - How to Leverage GH200s (specifically memory) Discussion - Over-Subscribing Systems and Unified Memory 2:00 PM - 2:15 PM — Break; move to Filene 2:15 PM - 3:15 PM — Moore Hall B13 Filene Auditorium Presentation - GPUs/AI - A Scientist's Perspective - Chris Simmons Sneak Peek - Grace Blackwell 3:15 PM - 4:00 PM — Moore Hall B13 Filene Auditorium Dartmouth Presentation Reviewing chat.dartmouth.edu and ai.dartmouth.edu - Dartmouth Team Introduction to Research Computing AI Offerings and Real-world Applications - Simon Stone API access to chat.dartmouth.edu - Jonathan Crossett In-depth Coverage & Future Plans for chat.dartmouth.edu - Tivon Luker Q & A - Jonathan Crossett, Simon Stone, Tivon Luker 4:00 PM - 4:15 PM — Break 4:15 PM - 5:00 PM — (OPTIONAL) Farewell to […]

Join Cambridge Computer, NVIDIA, and the UW-IT Research Computing Team for a day of innovation and learning at the University of Washington. This hybrid event is part of our national GPU in EDU Seminar Series, focusing on the expanding role of AI and data science in academic research and education. What to Expect: Expert Presentations on reproducible software pipelines, GPU computing services, and NVIDIA RAPIDS Hands-on Demonstrations showcasing practical GPU implementations Networking Opportunities with industry leaders and academic researchers Q&A Sessions with Cambridge, NVIDIA, and UW-IT specialists Featured speakers include Dr. Christopher Simmons (Cambridge Computer), Dr. Melisa Alkan (NVIDIA), and Dr. Kristen Finch (UW-IT Research Computing). Whether you're new to GPU computing or scaling advanced research, this seminar offers valuable insights for accelerating your research workflows. Visit Event Page to Register → All registrants will receive Zoom meeting information for remote participation.

This one-day event, presented in collaboration with the Minnesota Supercomputing Institute at the University of Minnesota, features Dr. Christopher S. Simmons leading comprehensive sessions that equip academic researchers with a complete framework for implementing reproducible AI and scientific workflows. Visit Event Page to Register → All registrants will receive Zoom meeting information for remote participation and a link to the recorded sessions.

Overview This meeting is the 2nd annual in-person gathering of the GPU Innovators User Community. In our inaugural meeting last year in Atlanta, we packed the room with passionate discussions about NVIDIA's Grace Hopper and Blackwell architectures—the powerhouse hardware that makes today's AI revolution possible. This year, we're taking the next logical step in our community's journey: transitioning from metal to models. TL;DR We'll tackle the questions keeping you up at night—which models fit your research needs, what hardware specifications actually matter, and how to deploy these systems using the open-source tools you already trust. Lunch is served afterwards at the Blues Museum down the street. Intended Audience This talk is designed for research computing professionals, HPC administrators, academic researchers, and AI enthusiasts seeking practical guidance on deploying open-source LLMs, selecting appropriate models, and integrating them into existing research computing infrastructure. Session Description The rapid evolution of open-source large language models presents researchers with unprecedented opportunities to deploy powerful AI capabilities while maintaining complete control over sensitive data and computational workflows. This tutorial addresses the critical challenge facing research computing centers: how to navigate the complex landscape of open-source LLMs to make informed decisions about model selection, hardware requirements, and […]