Key Enterprise Considerations for Inference Deployment of Large Language Models

This technical report outlines key considerations for enterprise leaders in deploying inference solutions for large language models (LLMs). It begins by discussing the transition from AI training to inference, emphasizing the importance of operationalizing trained models to generate real-time insights. The report identifies four fundamental factors that leaders should evaluate: Pace, Predictability, Performance, and Accuracy. It details how the pace of innovation in LLMs is constrained by hardware capabilities and the necessity for a robust inference strategy to ensure successful deployment. The report also highlights the challenges associated with achieving predictable performance metrics and the need for clarity in understanding workload performance. Additionally, it addresses potential pain points in deployment, such as data privacy, human capital requirements, and infrastructure costs. The document concludes by presenting critical questions for leaders to consider when planning their inference strategies, ensuring they can effectively leverage LLMs in their operations.