Navigating Challenges and Technical Debt in Large Language Models Deployment

Large Language Models (LLMs) have become an essential tool in advancing artificial intelligence and machine learning, enabling outstanding capabilities in natural language processing, and understanding. However, the efficient deployment of LLMs in production environments reveals a complex landscape of challenges and technical debt. In this paper, we aim to highlight unique forms of challenges and technical debt associated with the deployment of LLMs, including those related to memory management, parallelism strategies, model compression, and attention optimization. These challenges emphasize the necessity of custom approaches to deploying LLMs, demanding customization and sophisticated engineering solutions not readily available in broad-use machine learning libraries or inference engines.