用于科学发现的万亿参数AI服务基础设施：综述与展望
Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision
AI总结：
本文综述并展望了面向科学发现的万亿参数模型服务基础设施，提出了TPM用户与提供者生态系统的愿景，并概述了系统设计中的关键挑战及软件栈需求。
摘要：
深度学习方法正在变革科学研究，催生新技术，并最终带来新发现。随着对更强大AI模型需求的持续增长，我们正进入万亿参数模型（TPM）时代，即参数超过一万亿的模型——例如华为的PanGu-$\Sigma$。我们描绘了一个面向TPM用户和提供者生态系统的愿景，该生态系统旨在满足科学界的具体需求。随后，我们概述了在系统设计方面服务于TPM以支持科学研究与发现的重大技术挑战和开放性问题。具体而言，我们描述了一个全面的软件栈及其接口的需求，以支持研究人员多样化且灵活的要求。
英文摘要：
Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we are now entering an era of Trillion Parameter Models (TPM), or models with more than a trillion parameters -- such as Huawei's PanGu-$Σ$. We describe a vision for the ecosystem of TPM users and providers that caters to the specific needs of the scientific community. We then outline the significant technical challenges and open problems in system design for serving TPMs to enable scientific research and discovery. Specifically, we describe the requirements of a comprehensive software stack and interfaces to support the diverse and flexible requirements of researchers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
