The widespread adoption of the large language model (LLM), e.g. Generative Pre-trained Transformer (GPT), deployed on cloud computing environment (e.g. Azure) has led to a huge increased demand for resources. This surge in demand poses significant challenges to resource management in clouds. This paper aims to highlight these challenges by first identifying the unique characteristics of resource management for the GPT-based model. Building upon this understanding, we analyze the specific challenges faced by resource management in the context of GPT-based model deployed on clouds, and propose corresponding potential solutions. To facilitate effective resource management, we introduce a comprehensive resource management framework and present resource scheduling algorithms specifically designed for the GPT-based model. Furthermore, we delve into the future directions for resource management in the GPT-based model, highlighting potential areas for further exploration and improvement. Through this study, we aim to provide valuable insights into resource management for GPT-based models deployed in clouds and promote their sustainable development for GPT-based models and applications.

翻译：大语言模型（LLM）如生成式预训练Transformer（GPT）在云计算环境（如Azure）中的广泛部署，显著增加了对资源的需求。这种需求激增给云端资源管理带来了严峻挑战。本文通过首先识别基于GPT模型的资源管理的独特特征来着重阐明这些挑战。基于此理解，我们分析了在云端部署GPT模型背景下资源管理面临的具体挑战，并提出相应的潜在解决方案。为促进有效的资源管理，我们引入了一个全面的资源管理框架，并提出了专为GPT模型设计的资源调度算法。此外，我们深入探讨了GPT模型资源管理的未来方向，指出了有待进一步探索和改进的潜在领域。通过本研究，我们旨在为云端GPT模型的资源管理提供有价值的见解，并推动GPT模型及其应用的可持续发展。