Core Viewpoint - The 2025 China AI Computing Power Conference will be held on June 26 in Beijing, focusing on the evolving landscape of AI computing power driven by DeepSeek technology [1][2]. Group 1: Conference Overview - The conference will feature nearly 30 prominent speakers delivering keynotes, reports, and discussions on AI computing power [1]. - It includes a main venue for high-level forums and specialized discussions, as well as closed-door workshops for select attendees [2]. Group 2: Keynote Speakers - Notable speakers include Li Wei from the China Academy of Information and Communications Technology, who will discuss cloud computing standards [4][8]. - Wang Hua, Vice President of Moore Threads, will present on training large models using FP8 precision [12][13]. - Yang Gongyifan, CEO of Zhonghao Xinying, will share insights on high-end chip design and development [14][16]. - Xu Lingjie, CEO of Magik Compute, will address the evolution of compilation technology in AI infrastructure [18][22]. - Chen Xianglin from Qujing Technology will discuss innovations in optimizing large model inference [28][31]. Group 3: Specialized Forums - The conference will host specialized forums on AI inference computing power and smart computing centers, featuring industry leaders discussing cutting-edge technologies [2][4]. - The closed-door workshops will focus on heterogeneous training technologies and supernode technologies, aimed at industry professionals [2][67][71]. Group 4: Ticketing and Participation - The conference offers various ticket types, including free audience tickets and paid VIP tickets, with an application process for attendance [72].

Core Viewpoint - Morgan Stanley's recent report provides a detailed analysis of TSMC, highlighting its current challenges and forecasts for 2nm capacity and wafer pricing [1][2]. Group 1: Stock Performance and Market Comparison - TSMC's stock price has increased by 31% over the past three months, outperforming Taiwan's weighted index (TAIEX) which rose by 27% [2]. - In comparison, NVIDIA's stock surged by 53% during the same period, with currency pressures, particularly the appreciation of the New Taiwan Dollar (TWD) against the US Dollar (USD), contributing to TSMC's relative underperformance [2]. Group 2: Financial Forecast Adjustments - The appreciation of TWD by 8.1% has negatively impacted TSMC's gross margin by over 3%, leading to a downward revision of its gross margin expectations for 2025 from 58-59% to 55-56% [2]. - EPS forecasts for 2025 and 2026 have been reduced by 6% and 12%, respectively, due to the adverse effects of exchange rates [2]. Group 3: AI Semiconductor Market Position - TSMC holds a dominant position in the AI semiconductor market, with projected revenue growth from cloud AI semiconductor business at a compound annual growth rate (CAGR) of 40% over the next five years [3]. - By 2027, revenue from cloud AI is expected to account for 34% of TSMC's total revenue, up from 13% in 2024 and 25% in 2025 [3]. Group 4: Strategic Partnerships and Production Capacity - Intel's decision to outsource the production of its NovaLake CPU and GPU chips to TSMC using 2nm technology reflects high industry recognition of TSMC's advanced manufacturing capabilities [6]. - TSMC is poised to capture a share of the AI GPU market in mainland China, particularly if NVIDIA secures export licenses for its B30 chips, with a potential demand of 500,000 units [6]. Group 5: Industry Trends and Pricing Strategy - The semiconductor industry's inventory levels are declining, indicating a potential recovery in non-AI semiconductor demand [7]. - TSMC plans to increase wafer prices by 3-5% globally in 2026, with potential increases exceeding 10% at its US facilities, which may help mitigate gross margin pressures from currency appreciation [7]. Group 6: Capital Expenditure and Production Plans - TSMC plans to maintain a capital expenditure level of $40 billion in 2026, primarily to expand 2nm capacity to 90,000 wafers per month [9]. - The investment strategy reflects a balance between meeting future market demand and maintaining financial discipline, contrasting with the high volatility of capital expenditure cycles in the semiconductor industry [9]. Group 7: Key Issues Impacting Investor Confidence - Four key issues will significantly influence investor confidence in TSMC by 2026: growth in AI semiconductor business, uncertainty regarding Intel's outsourcing scale, the total addressable market for AI GPUs in mainland China, and TSMC's wafer pricing strategy [11][12]. - Successful implementation of a 3-5% price increase globally will be crucial for TSMC to offset rising costs and currency impacts [12]. Group 8: Geopolitical Risk Management - TSMC's $165 billion investment in the US enhances its ability to address geopolitical risks, particularly concerning semiconductor tariffs [15]. - If TSMC can secure exemptions for equipment and chemical imports, it may maintain a long-term gross margin above 53%, which is vital for its profitability [15].

想要看外资研报的同学，给大家推荐一个星球，在星球中每天都会上传几百篇外资顶尖投行的原文研 报：大摩、小摩、UBS、高盛、Jefferies、HSBC、花旗、BARCLAYS 等。 还有专注于半导体行业分析的SemiAnalysis的全部分析报告： 现在星球中领券后只需要390元，即可每天都能看到上百篇外资顶尖投行科技行业的分析报告和每天的 精选报告，无论是我们自己做投资，还是对行业有更深入的研究，都是非常值得的。 ...

Core Viewpoint - The article discusses the evolving landscape of AI networking, highlighting the challenges and opportunities presented by AI workloads that require fundamentally different networking architectures compared to traditional applications [2][3][6]. Group 1: AI Networking Challenges - AI workloads create unique demands on networking, requiring more resources and a different architecture than traditional data center networks, which are not designed for the collective communication patterns of AI [2][3]. - The performance requirements for AI training are extreme, with latency needs in microseconds rather than milliseconds, making traditional networking solutions inadequate [5][6]. - The bandwidth requirements for AI are exponentially increasing, creating a mismatch between AI demands and traditional network capabilities, which presents opportunities for companies that can adapt [6]. Group 2: Key Players in AI Networking - NVIDIA's acquisition of Mellanox Technologies for $7 billion was a strategic move to enhance its AI workload infrastructure by integrating high-performance networking capabilities [7][9]. - NVIDIA's AI networking solutions leverage three key innovations: NVLink for GPU-to-GPU communication, InfiniBand for low-latency cluster communication, and SHARP for reducing communication rounds in AI operations [11][12]. - Broadcom's dominance in the Ethernet switch market is challenged by the need for lower latency in AI workloads, leading to the development of Jericho3-AI, a solution designed specifically for AI [13][14]. Group 3: Competitive Dynamics - The competition between NVIDIA, Broadcom, and Arista highlights the tension between performance optimization and operational familiarity, with traditional network solutions struggling to meet the demands of AI workloads [16][24]. - Marvell and Credo Technologies play crucial supporting roles in AI networking, with Marvell focusing on DPU designs and Credo on optical signal processing technologies that could transform AI networking economics [17][19]. - Cisco's traditional networking solutions face challenges in adapting to AI workloads due to architectural mismatches, as their designs prioritize flexibility and security over the low latency required for AI [21][22]. Group 4: Future Disruptions - Potential disruptions in AI networking include the transition to optical interconnects, which could alleviate the limitations of copper interconnects, and the emergence of alternative AI architectures that may favor different networking solutions [30][31]. - The success of open standards like UCIe and CXL could enable interoperability among different vendor components, potentially reshaping the competitive landscape [31]. - The article emphasizes that companies must anticipate shifts in AI networking demands to remain competitive, as current optimizations may become constraints in the future [35][36].

Core Viewpoint - The article discusses the projected growth in HBM (High Bandwidth Memory) consumption, particularly driven by major players like NVIDIA, AMD, Google, and AWS, highlighting the increasing demand for AI-related applications and the evolving product landscape. Group 1: HBM Consumption Projections - In 2024, overall HBM consumption is expected to reach 6.47 billion Gb, a year-on-year increase of 237.2%, with NVIDIA and AMD's GPUs accounting for 62% and 9% of the consumption, respectively [1] - By 2025, total HBM consumption is projected to rise to 16.97 billion Gb, reflecting a year-on-year growth of 162.2%, with NVIDIA, AMD, Google, AWS, and others contributing 70%, 7%, 10%, 8%, and 5% respectively [1] Group 2: NVIDIA's HBM Demand - NVIDIA's HBM demand for 2024 is estimated at 6.47 billion Gb, with a recent adjustment bringing the total capacity to 6.55 billion Gb [2] - In 2025, NVIDIA's HBM demand is expected to decrease to 2.53 billion Gb, with HBM3e 8hi and 12hi versions making up 36% and 64% of the demand, respectively [2] - Key suppliers for NVIDIA include Samsung and SK hynix, which play crucial roles in the HBM supply chain [2] Group 3: AMD's HBM Demand - AMD's HBM demand for 2025 is projected at 0.20 billion Gb for the MI300 series and 0.37 billion Gb for the higher-end MI350 series [3] - Specific models like MI300X and MI325 are designed to enhance storage density, with capacities reaching 192GB and 288GB, respectively [3] - AMD relies on SK hynix and Samsung for HBM3e 8hi and 12hi versions, which are vital for its production plans [3] Group 4: Google and AWS HBM Demand - Google's HBM demand for 2025 is expected to be 0.41 billion Gb, primarily driven by TPU v5 and v6 training needs [4] - AWS's HBM demand is estimated at 0.28 billion Gb, with Trainium v2 and v3 versions accounting for 0.20 billion Gb and 0.08 billion Gb, respectively [6] - Both companies utilize HBM configurations that enhance their AI training and inference capabilities, with a focus on reducing reliance on external suppliers [5][6] Group 5: Intel's HBM Demand - Intel's HBM demand is relatively small, accounting for about 10% of total demand in 2025, primarily focusing on HBM3e versions [7] - Key suppliers for Intel include SK hynix and Micron, with Intel exploring in-house chip development to reduce supply chain dependencies [7]

以下文章来源于AI半导体专研 ，作者专研 AI半导体专研 . AI与半导体行业研究分享 上一篇 910B和910C 2025年预计出货情况和客户分布 对910B和910C的情况做了详细解读，本篇还是基 于本营本周发布的两份纪要，来聊一聊910D和920的情况： 910D已基本确定为四个Die的设计。 与910C相⽐，主要调整包括由两个Die升级为四个Die；性能方面， 910D在单卡综合性能上，应该会超过H100。预计最乐观情况下明年第⼆季度，最晚2026年第⼆季度末 可以实现市场出货。 920与910⼀样会有多个版本，⾸个版本将直接 采⽤双Die设计，⼯艺也会优化。架构调整幅度最⼤，将 从现有架构全⾯转向 GPGPU架构，⽣态上也会与NVIDIA的⽣态进⾏互通， GPU服务器 我们目前也在跟国内的数据中心厂家合作，提供GPU租赁服务，有兴趣的朋友可以加微信： 知识星球 星球中每天都会更新上百篇科技行业外资投行的研报，有全部的SemiAnalysis的分析报告，还会不定期更 新Seeking Alpha、Substack、 stratechery的精选文章。 每天还会推送精选的外资投行/国内券商的优质研报，省 ...

Core Viewpoint - Nvidia's CEO Jensen Huang indicated that future forecasts will exclude the Chinese market, yet the significance of China to Nvidia remains critical, as evidenced by the emphasis on Huawei as a competitive threat [3] Group 1: Nvidia's Strategy in China - Nvidia is developing a new generation of chips for the Chinese market, based on the GB202 GPU architecture, with plans to launch these new processors as early as July 2024 [3] - The new chips will include two models, referred to as B20 and B40/B30, which may be marketed as variants of the RTX 6000 series to obscure their Blackwell lineage [4] - Recent U.S. export controls have imposed restrictions on memory bandwidth and interconnect speed, leading to the use of GDDR memory in the new chips instead of HBM memory [4] Group 2: Chip Specifications - The B20 chip will utilize Nvidia's ConnectX-8 for interconnect functionality, optimized for small-scale clusters with 8 to 16 cards, primarily for inference tasks [6] - The B30/B40 models will support NVLink interconnect but at reduced speeds compared to standard specifications, with expected bandwidth similar to the H20's 900Gbps [7] - Memory configurations for the new chips are anticipated to include 24GB, 36GB, and 48GB, with the 48GB option being the most likely [8] Group 3: Market Demand and Pricing - The new chips are expected to be priced between $6,500 and $8,000, significantly lower than the H20's price range of $10,000 to $12,000, which may drive sustained customer demand [9] - Full server configurations with these new chips are estimated to cost between $80,000 and $100,000, depending on the connectivity options [9] Group 4: Customer Interest and Market Dynamics - Major Chinese tech companies have shown varying interest in the new chip models, with Tencent favoring the B20 for its cost-effectiveness in inference tasks, while ByteDance is more interested in the B30 and B40 to meet market demand left by the H20's discontinuation [10][11] - Alibaba has not specified a preference for particular models but indicates a strong overall demand for the chips [11] Group 5: Current Situation and Challenges - The true test for Nvidia will come once major Chinese customers receive testing cards, as the evaluation process typically takes about a month before large orders can be placed [12] - Despite Huang's comments, the Chinese market remains a vital revenue source for Nvidia, and competitors like Huawei continue to advance their own R&D efforts [12]

Core Viewpoint - The article discusses the strategic importance of rare earth elements in the context of US-China relations, emphasizing that China's control over rare earth resources gives it significant leverage in negotiations with the US and Europe [1][2][10]. Historical Background and Policy Evolution - Rare earth elements, despite being labeled "rare," are not particularly scarce in the Earth's crust, with some like cerium being as abundant as copper [2]. - China dominated the global rare earth market in the late 20th century, capturing about 97% of the market share through low-cost exports, which led to environmental issues [2][3]. - Since 1998, China has gradually tightened control over rare earth exports through quota systems to stabilize prices and reduce environmental damage [2][3]. Current Control Mechanism - China's rare earth industry is strictly regulated through a quota system, which is adjusted annually based on global demand to maintain supply-demand balance and price stability [5]. - The state-owned enterprises, China Rare Earth (Southern) and Northern Rare Earth, control the entire production chain from mining to final product [5]. Export Restrictions and Strategic Impact - Starting April 2024, China will implement a licensing system for the export of seven heavy rare earth elements, which are crucial for both civilian and military applications [6]. - The approval process for export licenses takes about 45 working days, causing potential production disruptions for overseas manufacturers [6][10]. - China allows the export of finished products containing rare earth magnets, providing a workaround for foreign manufacturers to access rare earth materials indirectly [6]. Challenges in Replacing Chinese Supply - Despite the abundance of rare earth elements, the extraction and processing are environmentally challenging, leading many countries to avoid developing their resources [8][9]. - New mining projects typically take 3 to 5 years to develop, with significant delays in regions with strict environmental regulations [8]. - China currently holds a 90% market share in rare earth magnets, making it difficult for other countries to compete due to technological and scale disadvantages [9]. Long-term Strategic Implications - China's control over rare earth resources provides it with a strong geopolitical leverage, but this leverage may diminish over time as other countries invest in alternative supply chains [10][11]. - The ongoing supply chain disruptions have significantly impacted industries reliant on rare earth elements, highlighting their critical role in modern technology [11]. - The article contrasts China's strategic management of rare earth resources with the US's challenges in regulating high-end chip exports, suggesting that China's approach may offer more effective control [11].

随着美国针对GPU的限制越来越多，大家对国产GPU尤其是华为昇腾系列GPU的表现和出货量都非 常关心，前段时间老黄还说华为芯片性能已超H200，华为的CloudMatrix云架构也已经超英伟达。当 然老黄这么说肯定是有夸张的成分，他一直对美国政府出台的这些限制政策很不爽，既让英伟达损 失了很多中国的大客户，又让国产GPU在这段时间取得了突飞猛进的发展。 由于华为从未对外公布过昇腾系列的出货量，因此我们只能参考第三方的调研，本文中关于昇腾910 系列出货量的数据，是参考自本营最近的一份调研纪要。 国产GPU采购情况 相信本文的读者们应该都看到过关于CSP大厂的资本开支，字节预计2025年投入1600亿，阿里宣布 未来3年投入3800亿建设云和AI硬件基础设施，腾讯预计2025投入900亿。根据公众号"AI半导体专 研"的一个调研，在H20被禁后，国内大厂对于国产GPU卡的态度和采购计划如下： 字节最为积极，主要采用寒武纪和昇腾，并广泛测试国内其他品牌，2025年计划增加采购这些品牌 的卡。 阿里则较为谨慎，除了自研的平头哥外，还上线了部分昇腾卡，但性能表现不佳；2025年预计首次 采购海光和寒武纪的卡，同时继续 ...

还有专注于半导体行业分析的SemiAnalysis的全部分析报告： 星球中每日还会更新Seeking Alpha、Substack、 stratechery的精选付费文章， 现在星球中领券后只需要 390元，即可每天都能看到上百篇外资顶尖投行科技行业的分析报告和每天的精选报告，无论是我们自 己做投资，还是对行业有更深入的研究，都是非常值得的。 想要看外资研报的同学，给大家推荐一个星球，在星球中每天都会上传几百篇外资顶尖投行的原文研 报：大摩、小摩、UBS、高盛、Jefferies、HSBC、花旗、BARCLAYS 等。 ...