Challenges in Scaling and Deploying LLMs

While the integration of LLMs into enterprise operations holds transformative potential, the deployment and scaling of these technologies present a number of significant challenges, as shown in Figure 2.7 which shows that using a foundational model is only a small fraction of what goes into building an end-to-end generative AI application that can scale for the enterprise. Addressing these challenges is crucial, as it not only provides a balanced view of LLM capabilities but also prepares enterprises for the realities and complexities involved in implementing this advanced technology effectively.

Figure 2.7: the challenges in deployment LLMs ^[2]

Deploying LLMs in production requires effective strategies for data preprocessing, bias detection, and mitigation. Also, LLMs require substantial computational resources for fine-turning and inference, leading to high infrastructure costs. Managing these expenses, whether through cloud services, dedicated...