Core Insights - The report highlights the growing demand for Scale up switching chips in data centers, with a projected global market size nearing $18 billion by 2030 and an annual CAGR of approximately 28% from 2022 to 2030 [1][3] - The development of AI hardware capabilities is driven by three key components: computing power, storage capacity, and communication capacity, with a focus on domestic solutions for communication capacity [2] - The traditional computing architecture is insufficient for the demands of AI training, leading to the trend of super nodes and large clusters, which significantly boost the demand for Scale up hardware [3] Industry Trends - The collaboration of computing power, storage, and communication is essential for enhancing AI hardware capabilities, with a particular emphasis on domestic advancements in communication capacity [2] - The emergence of super nodes is reshaping the market, as they enhance single-node computing capabilities and drive the demand for Scale up hardware [3] - Different communication protocols are being developed for Scale up and Scale out scenarios, with major companies focusing on proprietary protocols while smaller firms promote public protocols [4] Market Opportunities - The low domestic production rate of communication hardware presents a significant opportunity for domestic companies to fill the gap, particularly in the switching chip market where major players like Broadcom and Marvell dominate [5] - Companies such as Shudao Technology and Shengke Communication are making strides in product commercialization, indicating a growing domestic market for communication hardware [5] - Investment opportunities are identified in companies benefiting from PCIe hardware and Ethernet hardware, including Wantong Development and ZTE [6]

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]