Core Viewpoint - The traditional computing architecture is insufficient for the efficient, low-energy, and large-scale collaborative AI training needs, leading to the trend of supernodes which significantly boosts the demand for Scale up-related hardware [1][3] Group 1: AI Hardware Capabilities - AI hardware capabilities are driven by three main factors: computing power (determined by GPU performance and quantity), storage capacity (high-bandwidth memory cache close to GPUs), and communication capacity (encompassing Scale up, Scale out, and Scale across scenarios) [1][2] Group 2: Market Trends and Projections - The market for Scale up switching chips is expected to reach nearly $18 billion by 2030, with a CAGR of approximately 28% from 2022 to 2030, driven by the demand for supernodes [3] - The construction of large-scale AI clusters necessitates extensive interconnectivity between nodes, leading to increased demand for Scale out hardware, while power resource limitations in single regions will promote the adoption of Scale across solutions [3] Group 3: Communication Protocols - Different communication protocols are required for Scale up and Scale out, with major companies developing proprietary protocols alongside third-party and smaller firms promoting public protocols [4] - Notable proprietary protocols for Scale up include NVIDIA's NVlink and AMD's Infinity Fabric, while public protocols include Broadcom's SUE and PCIe [4] Group 4: Domestic Hardware Development - The domestic production rate of communication hardware is currently very low, presenting a significant opportunity for domestic replacement in the market [5] - Companies like Shudao Technology and Shengke Communication are advancing towards commercialization of their products, indicating a growing domestic market potential [5] Group 5: Investment Opportunities - Beneficiaries of PCIe hardware include Wantong Development and Lanke Technology, while Ethernet hardware beneficiaries include Shengke Communication and ZTE [6]