标题： 计算无法处理真相：为什么通信税在现代人工智能基础设施中优先考虑内存和互连 显示英文标题

标题： Compute Can't Handle the Truth: Why Communication Tax Prioritizes Memory and Interconnects in Modern AI Infrastructure

摘要： 现代人工智能工作负载，如大型语言模型（LLMs）和检索增强生成（RAG），对内存、通信带宽和资源灵活性提出了严峻的要求。传统的以GPU为中心的架构由于日益增长的跨GPU通信开销而难以扩展。本报告介绍了关键的人工智能概念，并解释了Transformer如何革新了大型语言模型中的数据表示。我们分析了大规模人工智能硬件和数据中心设计，识别了分层系统中的可扩展性瓶颈。为了解决这些问题，我们提出了一种基于计算快速链接（CXL）的模块化数据中心架构，该架构实现了内存、计算和加速器的解耦扩展。我们进一步探讨了针对加速器优化的互连技术——统称为XLink（例如，UALink、NVLink、NVLink Fusion），并引入了一种CXL-over-XLink的混合设计，在保持内存一致性的同时减少远距离数据传输。我们还提出了一种结合本地内存和池化内存的分层内存模型，并评估了轻量级CXL实现、HBM和硅光子学在高效扩展中的应用。我们的评估结果表明，人工智能基础设施的可扩展性、吞吐量和灵活性得到了改善。 显示英文摘要

摘要： Modern AI workloads such as large language models (LLMs) and retrieval-augmented generation (RAG) impose severe demands on memory, communication bandwidth, and resource flexibility. Traditional GPU-centric architectures struggle to scale due to growing inter-GPU communication overheads. This report introduces key AI concepts and explains how Transformers revolutionized data representation in LLMs. We analyze large-scale AI hardware and data center designs, identifying scalability bottlenecks in hierarchical systems. To address these, we propose a modular data center architecture based on Compute Express Link (CXL) that enables disaggregated scaling of memory, compute, and accelerators. We further explore accelerator-optimized interconnects-collectively termed XLink (e.g., UALink, NVLink, NVLink Fusion)-and introduce a hybrid CXL-over-XLink design to reduce long-distance data transfers while preserving memory coherence. We also propose a hierarchical memory model that combines local and pooled memory, and evaluate lightweight CXL implementations, HBM, and silicon photonics for efficient scaling. Our evaluations demonstrate improved scalability, throughput, and flexibility in AI infrastructure.