A Comprehensive Survey of Streaming Large Language Models: Architectures, Applications, and Future Directions

Abstract

This comprehensive article examines the current state of streaming Large Language Models, synthesizing research across technical implementations, application domains, and performance optimization techniques. It systematically review the transition from batch to streaming architectures, analyzes enabling technologies, and identify emerging research directions in this rapidly evolving field. The use of Large Language Models (LLMs) in streaming systems signifies a paradigm shift in how institutions process and generate value from continually produced information. This detailed article discusses the shift to real-time implementation based on streaming rather than classical batch processing, covering the technical foundations of such implementations, their strengths, and challenges. It examines how these systems allow organizations to analyze logs, conversations, and transactional events on the fly to immediately provide actionable insights in areas such as customer support, financial compliance, cybersecurity, e-commerce, and content moderation. By analyzing enabling technologies such as model distillation, hybrid deployment architectures, and specialized infrastructure components, this article sheds light on how organizations address challenges inherent in latency management, resource optimization, and scalability. Possible future directions are discussed, including adaptive learning capabilities, multi-modal integration, and increased explainability mechanisms, providing a research roadmap for advancing streaming LLM technologies and their organizational impacts on real-time intelligence and decision facilitation.