智通财经网·2025-06-26 00:29Core Viewpoint - The report from CITIC Securities indicates that the demand for AI large model training and inference is continuously growing, with system-level computing expected to become the next generation of AI computing infrastructure [1] Group 1: System-Level Computing - System-level computing is anticipated to become the next generation of AI computing infrastructure, driven by the need for generality in foundational infrastructure to address future model developments [1] - The scaling law is rapidly evolving in post-training and online inference stages, with innovations in model architecture enhancing training capabilities [1] - The focus on hardware deployment for achieving higher throughput and lower latency in inference is becoming critical, with a shift towards cluster-based inference models [1] Group 2: Technical Aspects - The development of single-chip computing capabilities is outpacing advancements in communication technology, making communication efficiency a key factor for cluster performance [3] - Two primary methods for building large clusters are identified: Scale up (increasing resources per node) and Scale out (increasing the number of nodes), with Scale up being a significant future direction [3] - Notable examples include NVIDIA's NVL72 system and Huawei's CloudMatrix384 super node, which provide insights into industry development [3] Group 3: Industry Dynamics - The semiconductor industry typically utilizes mergers and acquisitions for technology integration and market expansion, with leading companies often pursuing these strategies to enhance their market position [4] - NVIDIA's acquisition of Mellanox exemplifies this strategy, expanding its NVLink technology to include RDMA networks for large-scale computing [4] - AMD's acquisition of ZT Systems has strengthened its system architecture design capabilities and data center solution delivery experience, contributing to the core of AI solutions [4][5]