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.

翻译：大语言模型（LLM）的迅速崛起已深刻变革了从自然语言处理到代码生成等各类人工智能应用。然而，这些模型在训练与推理过程中的计算需求带来了重大挑战。传统系统往往难以满足这些要求，亟需整合云原生与分布式架构。本文探讨了云平台与分布式系统在支撑大语言模型可扩展性、效率及优化方面的关键作用。我们讨论了LLM部署的复杂性，包括数据管理、资源优化，以及微服务、自动缩放和混合云-边缘解决方案的必要性。此外，我们审视了无服务器推理、量子计算与联邦学习等新兴研究趋势，以及它们推动LLM创新下一阶段的潜力。本文最终提出了未来发展的路线图，强调需持续开展研究、推进标准化并加强跨部门协作，以支撑LLM在科研与企业应用中的持续增长。