Cloud-native and Distributed Systems for Efficient and Scalable Large Language Models -- A Research Agenda

TL;DR

This paper discusses how cloud-native and distributed systems can address the scalability and efficiency challenges of deploying large language models, outlining research directions and technological solutions.

Contribution

It provides a comprehensive research agenda for integrating cloud and distributed architectures to support scalable, efficient LLM deployment and innovation.

Findings

Highlights the importance of microservices and autoscaling for LLM deployment

Explores emerging trends like serverless inference and federated learning

Proposes a roadmap for future research and standardization in LLM systems

Abstract

The rapid rise of Large Language Models (LLMs) has revolutionized various artificial intelligence (AI) applications, from natural language processing to code generation. However, the computational demands of these models, particularly in training and inference, present significant challenges. Traditional systems are often unable to meet these requirements, necessitating the integration of cloud-native and distributed architectures. This paper explores the role of cloud platforms and distributed systems in supporting the scalability, efficiency, and optimization of LLMs. We discuss the complexities of LLM deployment, including data management, resource optimization, and the need for microservices, autoscaling, and hybrid cloud-edge solutions. Additionally, we examine emerging research trends, such as serverless inference, quantum computing, and federated learning, and their potential to drive the next phase of LLM innovation. The paper concludes with a roadmap for future developments, emphasizing the need for continued research, standardization, and cross-sector collaboration to sustain the growth of LLMs in both research and enterprise applications.