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This paper reports on the lessons learned from developing and deploying campus-wide large language model (LLM) services at Purdue University for generative AI (GenAI) applications in education and research. We present a frame- work for identifying an LLM solution suite and identify key considerations related to developing custom solutions. While the GenAI ecosystem continues to evolve, the framework is intended to provide a tool- and organization-agnostic approach to guide leaders in conversations and strategy for future work and collaboration in this emerging field.
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This content will become publicly available on July 18, 2026
Providing On-Prem GenAI Inference Services to a Campus Community
The Rosen Center for Advanced Computing at Purdue University has recently released two Generative AI inference tools, AnvilGPT and Purdue GenAI Studio, to the research and campus communities. These services support over 1000 users who use 10+ open-source GenAI models to aid their work. Building on HPC’s long history of using open-source tools, these services are based on customized open-source frameworks and hosted entirely on-prem. This pa- per argues that building custom GenAI services from open-source frameworks is a scalable and cost-effective solution for providing access to Generative AI models. This paper shares the methodology and resources required to develop and host these services and seeks to be a resource for other research computing centers that wish to leverage their HPC investment to create similar services.
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- Award ID(s):
- 2005632
- PAR ID:
- 10639609
- Editor(s):
- PEARC'25
- Publisher / Repository:
- ACM
- Date Published:
- Page Range / eLocation ID:
- 1 to 4
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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