随着人工智能推理工作负载的持续增长，数据中心架构面临前所未有的挑战。1AI推理工作负载呈现连续性、地理分散的特点，对响应时间有着极高的敏感度，而数据移动已经成为比计算能力本身更紧迫的制约因素。1现代AI技术如检索增强生成（RAG）要求系统不断扫描海量数据库，这使得传统将内存和存储视为辅助硬件的架构设计理念已经不再适用。1
为了应对这一转变，企业需要将内存和存储重新定位为战略资产，而非辅助组件。1有效的AI基础设施必须把计算、内存、存储和网络作为一个集成系统进行优化，而不是孤立地优化各个组件。1Tirias Research创始人Jim McGregor指出："AI不是单一工作负载，而是数千、数百万、数十亿个不同的工作负载"。1为此，企业在AI基础设施采购时需要定义具体工作负载、构建模块化架构，并持续评估采购策略，以确保基础设施能够适应快速变化的AI应用需求。1
The rise of continuous, geographically distributed AI inference workloads is fundamentally reshaping how organizations must design their data center infrastructure.1 Data movement has emerged as the most critical bottleneck constraining AI system performance, surpassing computational capacity itself as the primary limiting factor.1 Modern AI techniques such as retrieval-augmented generation require systems to continuously scan massive databases, making the traditional approach of treating memory and storage as secondary hardware components increasingly untenable.1
To address these challenges, enterprises must reconceive memory and storage as strategic assets rather than auxiliary infrastructure components.1 Effective AI infrastructure requires optimizing compute, memory, storage, and networking as an integrated system rather than tuning each component independently.1 According to Jim McGregor, founder of Tirias Research, the complexity of modern AI deployment underscores this imperative: "AI is not a single workload, but thousands, millions, billions of different workloads."1 Organizations undertaking AI infrastructure procurement should define specific workloads upfront, construct modular architectures that can adapt to evolving requirements, and continuously reassess purchasing strategies as technologies and use cases develop.1
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