以下文章来源于More Than Semi ，作者猫叔 More Than Semi . 我们之前写过一篇IDC出的国内AI芯片出货量的文章，当时有些读者反馈说那个数据可能有些问 题，最近Bernstein也出了一份国内AI芯片出货量的数据，但跟IDC的那个数据有些出入，当然这个 报告的内容非常全，不只包含出货量，还有供需关系，各国产GPU的情况等。 基于这个报告整理的数据已经放到星球，有兴趣的读者可以到星球查看我们整理出来的数据。还是 提醒一下，由于各GPU厂官方并未公布出货量，因此第三方给的数据仅供参考。 H20 & B30 在H20禁令前，Bernstein预计2025年中国AI加速器市场将达到395亿美元，其中主要由Nvidia H20 （229亿美元）、AMD MI308（20亿美元）和本土厂商（如华为Ascend、寒武纪、海光，总计146亿 美元）构成。禁令后，Nvidia H20损失16.8亿美元，AMD MI308损失1.5亿美元，部分订单预计将转 移至本土厂商，导致2025年本土厂商收入增加约10%。然而，由于7nm晶圆和CoWoS技术的生产瓶 颈，Bernstein认为本土厂商无法完全填补 ...

2025 年 Q2 全球 GB200 NVL72 机架月产能达 2000-2500 台，Q2 总产量预计 5000-6000 台，鸿海目标交付 3000-4000 台，广达 GB300 计划 9 月进入量产。采购主力为北美四大云厂商（微软/谷歌 /AWS/Meta）及 OpenAI，中东"主权 AI"订单成为重要新增量。 市场研究部 2025 年 7 月 21 日 计算机行业周度：英伟达 GB300 上线 本周计算机行业指数表现 本周（7.14-7.18）计算机（申万）板块上涨 2.12%，沪深 300 指数 上涨 1.09%，计算机板块跑赢沪深 300 指数 1.03 个百分点。和申万 其他行业对比，计算机行业涨幅排名位列第 8 位。 本周涨幅前 3 名 分 别为 熙 菱 信息 （+ 27.08%）、ST 立方（+ 26.86%）、延华智能（+ 24.04%），跌幅前 3 名分别为大智慧（- 17.51%）、金证股份（-10.85%）、京北方（-10.61%）。 本周关注 GB300 服务器技术进展与产业链影响分析 英伟达 GB300 基于 Blackwell Ultra 架构，采用台积电 4NP 制程集 ...

Investment Rating - The report maintains a "Positive" investment rating for the supernode industry, driven by the explosive growth of model parameters and the shift in computing power demand from single points to system-level integration [3]. Core Insights - The supernode trend is characterized by a dual expansion of high-density single-cabinet and multi-cabinet interconnection, balancing communication protocols and engineering costs [4][5]. - Domestic supernode solutions, represented by Huawei's CloudMatrix 384, achieve a breakthrough in computing power scale, surpassing single-card performance limitations [4][5]. - The industrialization of supernodes will reshape the computing power industry chain, creating investment opportunities in server integration, optical communication, and liquid cooling penetration [4][5][6]. - Current market perceptions underestimate the cost-performance advantages of domestic solutions in inference scenarios and overlook the transformative impact of computing network architecture on the industry chain [4][7]. Summary by Sections 1. Supernode: New Trends in AI Computing Networks - The growth of large model parameters and architectural changes necessitate understanding the two dimensions of computing power expansion: Scale-up and Scale-out [15]. - Scale-up focuses on tightly coupled hardware, while Scale-out emphasizes elastic expansion to support loosely coupled tasks [15][18]. 2. Huawei's Response to Supernode Challenges - Huawei's CloudMatrix 384 represents a domestic paradigm for cross-cabinet supernodes, achieving a computing power scale 1.7 times that of NVIDIA's NVL72 [4][5][6]. - The design of supernodes must balance model training and inference performance with engineering costs, particularly in multi-GPU inference scenarios [69][77]. 3. Impact on the Industry Chain - The industrialization of supernodes will lead to a more refined division of labor across the computing power industry chain, with significant implications for server integration and optical communication [6][4]. - The demand for optical modules driven by Huawei's CloudMatrix is expected to reach a ratio of 1:18 compared to GPU demand [6]. 4. Key Company Valuations - The report suggests focusing on companies involved in optical communication, network devices, data center supply chains, copper connections, and AI chip and server suppliers [5][6].

Core Viewpoint - The article discusses the emergence and significance of Super Nodes in addressing the increasing computational demands of AI, highlighting their advantages over traditional server architectures in terms of efficiency and performance [4][10][46]. Group 1: Definition and Characteristics of Super Nodes - Super Nodes are defined as highly efficient structures that integrate numerous high-speed computing chips to meet the growing computational needs of AI tasks [6][10]. - Key features of Super Nodes include extreme computing density, powerful internal interconnects using technologies like NVLink, and deep optimization for AI workloads [10][16]. Group 2: Evolution and Historical Context - The concept of Super Nodes evolved from earlier data center designs focused on resource pooling and space efficiency, with significant advancements driven by the rise of GPUs and their parallel computing capabilities [12][13]. - The transition to Super Nodes is marked by the need for high-speed interconnects to facilitate massive data exchanges between GPUs during model parallelism [14][21]. Group 3: Advantages of Super Nodes - Super Nodes offer superior deployment and operational efficiency, leading to cost savings [23]. - They also provide lower energy consumption and higher energy efficiency, with potential for reduced operational costs through advanced cooling technologies [24][30]. Group 4: Technical Challenges - Super Nodes face several technical challenges, including power supply systems capable of handling high wattage demands, advanced cooling solutions to manage heat dissipation, and efficient network systems to ensure high-speed data transfer [31][32][30]. Group 5: Current Trends and Future Directions - The industry is moving towards centralized power supply systems and higher voltage direct current (DC) solutions to improve efficiency [33][40]. - Next-generation cooling solutions, such as liquid cooling and innovative thermal management techniques, are being developed to support the increasing power density of Super Nodes [41][45]. Group 6: Market Leaders and Innovations - NVIDIA's GB200 NVL72 is highlighted as a leading example of Super Node technology, showcasing high integration and efficiency [37][38]. - Huawei's CloudMatrix 384 represents a strategic approach to achieving competitive performance through large-scale chip deployment and advanced interconnect systems [40].