Core Viewpoint - The article emphasizes the significance of the latest CXL 4.0 specification in enhancing memory connectivity and performance for high-performance computing, particularly in artificial intelligence applications [2][13]. Group 1: CXL 4.0 Specification Features - CXL 4.0 doubles the bandwidth to 128GTs without additional latency, enhancing data transfer speeds between connected devices [4][11]. - It supports high-speed data transfer between CXL devices, improving overall system performance [7]. - The specification retains full backward compatibility with CXL 3.x, 2.0, 1.1, and 1.0 versions, ensuring a smoother transition for existing deployments [12]. Group 2: Importance of CXL for AI - CXL addresses memory bottlenecks in AI workloads by enabling memory pooling, allowing all processors to access a unified shared memory space, thus improving memory utilization [15][17]. - It facilitates large-scale inference by providing quick access to large datasets without the need for memory duplication across GPUs [18]. - CXL is designed to meet the growing performance and scalability demands of modern workloads, particularly in AI and high-performance computing [19]. Group 3: Future Implications of CXL - The introduction of CXL is seen as a fundamental shift from static, isolated architectures to flexible, network-based computing, paving the way for next-generation AI and data-intensive systems [20]. - CXL enables a unified, flexible AI architecture across server racks, crucial for training large language models efficiently [21]. - Major industry players, including Intel, AMD, and Samsung, are beginning to pilot CXL deployments, indicating its growing importance in the semiconductor landscape [21].