KubeCon + CloudNativeCon + Open Source Summit + AI_dev China 2024

With the rise of generative AI technology, more and more applications are starting to integrate with the capabilities of generative AI. However, the high costs of training and inference can be daunting for developers. In this talk, we will discuss the issues and solutions that need additional consideration when using elastic scaling in generative AI scenarios, including: ● How to enhance the elastic startup efficiency of generative AI ● How to address the efficiency of inference when separating compute and storage in generative AI ● How to reduce the costs of training and inference ● How to solve the interruption problem in AI training scenarios using Spot instances ● How to address the issue of capacity elasticity in LLM scenarios Finally, we will introduce the practical experience of the world's leading generative AI service provider: HaiYi (seaart.ai), allowing more developers to understand the architectural methods of elastic cloud AI infrastructure.

随着生成式人工智能技术的兴起，越来越多的应用程序开始与生成式人工智能的能力集成。然而，训练和推理的高成本可能会让开发人员望而却步。在这次演讲中，我们将讨论在生成式人工智能场景中使用弹性扩展时需要额外考虑的问题和解决方案，包括： ● 如何提高生成式人工智能的弹性启动效率 ● 如何在生成式人工智能中分离计算和存储时解决推理效率的问题 ● 如何降低训练和推理的成本 ● 如何使用Spot实例解决AI训练场景中的中断问题 ● 如何解决LLM场景中的容量弹性问题 最后，我们将介绍世界领先的生成式人工智能服务提供商海艺（seaart.ai）的实际经验，让更多开发人员了解弹性云AI基础设施的架构方法。