  Large Language Models (LLMs) have demonstrated extraordinary performance
across a broad array of applications, from traditional language processing
tasks to interpreting structured sequences like time-series data. Yet, their
effectiveness in fast-paced, online decision-making environments requiring
swift, accurate, and concurrent responses poses a significant challenge. This
paper introduces TStreamLLM, a revolutionary framework integrating
Transactional Stream Processing (TSP) with LLM management to achieve remarkable
scalability and low latency. By harnessing the scalability, consistency, and
fault tolerance inherent in TSP, TStreamLLM aims to manage continuous &
concurrent LLM updates and usages efficiently. We showcase its potential
through practical use cases like real-time patient monitoring and intelligent
traffic management. The exploration of synergies between TSP and LLM management
can stimulate groundbreaking developments in AI and database research. This
paper provides a comprehensive overview of challenges and opportunities in this
emerging field, setting forth a roadmap for future exploration and development.

Databases;Artificial Intelligence;Distributed, Parallel, and Cluster Computing