As organizations across sectors grapple with the opportunities and challenges presented by using large language models (LLMs), the infrastructure needed to build, train, test, and deploy LLMs presents its own unique challenges. As part of the SEI’s recent investigation into use cases for LLMs withing the Intelligence Community (IC), we needed to deploy compliant, cost-effective infrastructure for research and development. In this post, we describe current challenges and state of the art of cost-effective AI infrastructure, and we share five lessons learned from our own experiences standing up an LLM for a specialized use case.
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