KubeCon + CloudNativeCon + Open Source Summit + AI_dev China 2024

As AI rapidly evolves and embraces cloud-native technologies, inference performance has become crucial for application value. GPU selection, serving framework configuration, and model/data loading significantly impact inference efficiency. We'll focus on cloud-native solutions to storage performance issues and tools for evaluating inference performance across configurations, offering optimal deployment setups integrated into cloud-native workflows. We'll discuss inference performance's impact on user experience and how optimization can reduce costs and improve efficiency. Using technologies like Fluid and model optimization, we'll share strategies to enhance inference performance. Based on performance and cost analysis of various GPUs, we'll guide AI engineers in hardware selection. Additionally, we'll introduce a performance testing tool to evaluate and recommend the best model, hardware, and acceleration scheme combinations, aligning with deployment workflows based on test results.

随着人工智能的快速发展和对云原生技术的采用，推理性能对应用价值变得至关重要。 GPU选择、服务框架配置以及模型/数据加载对推理效率有着重大影响。我们将专注于云原生解决方案，解决存储性能问题，并提供评估不同配置下推理性能的工具，为云原生工作流程提供最佳部署设置。 我们将讨论推理性能对用户体验的影响，以及优化如何降低成本并提高效率。利用Fluid和模型优化等技术，我们将分享增强推理性能的策略。基于各种GPU的性能和成本分析，我们将指导人工智能工程师进行硬件选择。此外，我们将介绍一种性能测试工具，评估并推荐最佳模型、硬件和加速方案组合，根据测试结果与部署工作流程相匹配。