# NAIRR Pilot Start-Up Request — Copy/Paste Template Fill in the **[BRACKETED]** parts, then paste each section into the matching field at . Reminders: pick **one** resource (Jetstream2 IU, up to **128,000 SUs**), **3-month** project, **U.S. institution**, **institutional email** (not Gmail), results must be **open/publishable**, and have your **CV** ready to upload (grad students: add a faculty advisor letter). --- ## Project Title ``` Hands-On Introduction to NAIRR & Jetstream2 for [Your Institution] Students and Educators ``` --- ## Abstract (public — ~100–150 words) ``` This project delivers hands-on workshops that introduce students and faculty at [Your Institution] to the National AI Research Resource (NAIRR) and the Jetstream2 cloud computing system. Participants learn to launch cloud instances, run Jupyter notebooks, and compare how AI models perform across multiple inference frameworks — gaining practical experience with national AI cyberinfrastructure they would otherwise be unable to access. By lowering the barrier to entry and openly publishing all teaching materials, the project broadens participation in AI research, strengthens AI readiness among the next generation of researchers and educators, and shows faculty how straightforward it is to bring NAIRR resources into their own courses. ``` --- ## Research Description / Justification ``` Motivation. Many institutions and educators lack access to the GPU and cloud computing needed to teach and explore modern AI, and many faculty assume that obtaining national resources is too complex to attempt. This project removes that barrier by running practical, reproducible workshops on NAIRR's Jetstream2 resource. Activities. We will deliver [number] hands-on workshop sessions for approximately [20] participants each. Using openly published Jupyter notebooks, participants launch their own Jetstream2 instances and run guided exercises — including a comparison of local AI inference frameworks (Ollama, llama.cpp, and vLLM) running the same model — to understand cloud-based AI computing and the trade-offs between tools. Sessions cover both classroom and research use, and how to apply for NAIRR resources independently. Alignment with NAIRR goals. The project directly advances NAIRR's mission to broaden access to AI cyberinfrastructure and build the AI-capable workforce: it reaches learners and educators at [institution type, e.g., a regional public university serving underrepresented students], and it equips faculty to sustain AI education beyond this project. Outcomes. All notebooks, slides, and step-by-step guides are published openly on GitHub for reuse by any educator. We will report participation and lessons learned in the required final report. ``` --- ## Resource & SU Justification **Resource to select:** Indiana University Jetstream2 ``` Each workshop runs ~[20] participants, each on a small CPU instance (m3.quad, 4 vCPUs) for ~[3] hours. At roughly 1 SU per vCPU-hour, that is about 4 SU/hour per instance, or ~240 SU per session for 20 participants. Across [number] planned sessions plus instructor preparation and testing, total usage is on the order of a few thousand SUs — well within the 128,000 SU Start-Up cap, with comfortable headroom for repeat sessions and occasional GPU experimentation. ``` --- ## Funding ``` [List any grants directly supporting this work, or: "No external grant funding directly supports this educational/outreach activity."] ``` --- ## Tips - Lead with **education / broadening access** — squarely in NAIRR's mission. - Emphasize **open outputs** (public GitHub repo + final report). - Keep the resource ask **modest and justified** — a CPU teaching workshop needs very little. - Use your **institutional email** throughout; have your **CV** ready. --- *Part of the AI Horizon project · NSF #2528858 · CSUSB Center for Cyber and AI*