华为云黄瑾：传统计算架构难支撑AI代际跃迁，超节点架构是创新

Core Insights - The rapid growth in demand for AI computing power has outpaced the capabilities of traditional computing architectures, necessitating the development of new solutions like the super node architecture [1] - Huawei Cloud's CloudMatrix 384 super node addresses key technical challenges in AI computing, including communication efficiency, memory limitations, and reliability, achieving a computing power scale of up to 300 Pflops, surpassing NVIDIA's NVL72 by 67% [1] - The introduction of distributed inference platforms and innovative technologies such as Elastic Memory Storage (EMS) significantly enhances resource utilization and performance, reducing latency and improving fault detection rates [2] Group 1 - The demand for AI computing power has increased by 10,000 times, while hardware capabilities have only improved by 40 times in the last eight years [1] - The CloudMatrix 384 super node connects 384 cards into a single super cloud server using a new high-speed interconnect bus [1] - The super node features six technical advantages, including MoE affinity and high reliability [1] Group 2 - The distributed inference platform allows for efficient distributed inference with one card acting as one expert, significantly improving MoE computation and communication efficiency [2] - The MatrixLink service consists of two network layers, enabling high-speed interconnection within the super node and low latency communication [2] - The EMS technology decouples memory from computing power, enhancing resource utilization and reducing the first token latency by up to 80% [2]