Market Performance - Major stock indices in Shanghai and Shenzhen opened lower on June 3 but experienced upward fluctuations during the morning session, with the Shanghai Composite Index and ChiNext Index consolidating at relatively high levels in the afternoon [1] - By the close, the Shanghai Composite Index was at 3361.98 points, up 0.43%, with a trading volume of approximately 468.3 billion; the Shenzhen Component Index closed at 10057.17 points, up 0.16%, with a trading volume of about 673.1 billion; the ChiNext Index ended at 2002.70 points, up 0.48%, with a trading volume of around 307.1 billion [1] Sector Performance - The beauty care and immunotherapy sectors saw significant gains, while other sectors such as precious metals, innovative drugs, CRO concepts, cultivated diamonds, COVID-19 drug concepts, pet economy, weight loss drugs, gaming, dairy, and small banks also experienced notable increases [1] - Conversely, sectors like steel, home appliances, and coal faced declines, although the overall drop was not substantial [1] Institutional Insights - Jifeng Investment Advisory noted that the market is gradually finding a bottom, with medium to long-term investment opportunities emerging, particularly in sectors benefiting from policy support such as consumer goods, semiconductors, and robotics [2] - Guojin Securities highlighted opportunities in gold and innovative drugs, emphasizing the potential for margin improvement and revenue growth in the pharmaceutical sector due to policy guidance [2] - Dongwu Securities recommended focusing on technology growth sectors, thematic catalysts, and areas with improving fundamentals, such as innovative drugs and military electronics [3] Industry Developments - The National Federation of Automobile Dealers Association called for a resistance against "involution-style" competition primarily driven by price wars, advocating for high-quality development in the automotive industry [4] - The National Energy Administration announced measures to optimize the electricity market access environment and enhance the effectiveness of qualification management, including exemptions for new business models in distributed energy [5]

以下文章来源于36氪财经 ，作者范亮 郑怀舟 36氪财经 . 36氪旗下官方账号。洞见市场，比99%的投资者更聪明。 支撑市场上涨无非两方面因素，盈利和估值。 盈利端，2024年万得全A（除金融、石油石化）归母扣非净利润同比下滑10.7%，同时24Q1-24Q4利润单季度降幅逐季扩大。但在"924"政策催化下，市场 估值大幅抬升，万得全A（剔除金融、石油石化）当年反而上涨6.15%。 寻求确定性。 文 ｜ 范亮 编辑 ｜ 郑怀舟 来源｜ 36氪财经（ID： krfinance） 封面来源 ｜ 视觉中国 上证指数本周再度转跌之际，微盘股又创历史新高。 2025年以来，万得微盘股的涨幅为23.04%，已经连续7年实现上涨。北证50年内涨幅已经达到32%,周中一度突破历史新高。另一方面，万得银行指数在本 周亦创下新高，年内涨幅9%。 这个市场状态与2023年颇为相似。2023年受疫后经济复苏预期影响，上证开盘一度从3000点出头冲高至5月份的3400点以上，市场成交额连续多个交易日 突破万亿，但随后因弱复苏年末回落至3000点附近。期间市场活跃度较高+上市公司弱盈利使得资金涌入交易性质更强的小微盘股，促成小微盘股的 ...

以下文章来源于傅里叶的猫 ，作者小小 傅里叶的猫 . 芯片EDA大厂资深工程师，半导体AI行业解读及研报分享 申请入围"中国IC独角兽" 是说芯语，欢迎关注分享 美国或设 GPU 内存带宽上限 1.7-1.8TB/s，若如此英伟达 H20 可能弃用 HBM 内存改用 GDDR6，降级 版 H20 性能仍可能强于使用 GDDR6 的游戏 GPU，如RTX5090D。 今天又看到另一篇研报，是广发海外的一个分析，认为该 GPU可能命名为 6000D，即 B40，极有可能 在 7 月初发布，其搭载的 GDDR7 速率约为 1.7TB/s（对比 H20 的 4TB/s）。在 NVLink 单向速度约 550GB/s 及 CUDA 的支持下，广发预计到 2025 年底出货量将达约 100 万片。 对于很多没有公布的数据，我们都要结合多家的报告来看，因为每家的分析可能多多少少有些不同的地 方。就像我们上篇文章中提到的华为昇腾系列2025年出货量调研的文章，几家机构给出的数据都是在 70w~85w之间，而在IDC的一个分析中，提到2024年华为昇腾的出货量已经达到了64万，那按正常的增 长速度，加上H20被禁，今年绝对不会 ...

Core Viewpoint - The article discusses the innovative approach of a company, 行云集成电路, led by its founder, 季宇, in developing a cost-effective AI computing solution through the integration of CPU and memory technologies, challenging the traditional reliance on GPUs for large model deployments [5][10][19]. Group 1: Company Overview - 行云集成电路 was founded by 季宇, a former Huawei expert, focusing on self-developed GPU technology [5]. - The company aims to create a DeepSeek integrated machine, which is a high-performance computing device designed for local deployment of AI models [8][19]. Group 2: Technology and Innovation - The DeepSeek integrated machine, referred to as "组装机," combines various hardware components, including Intel or domestic CPUs and NVIDIA GPUs, but aims to reduce costs significantly [9][19]. - 季宇 argues that modern large models can run efficiently on CPUs, leveraging their high memory bandwidth, which can exceed that of high-end GPUs like the RTX 4090 [10][13]. - The company plans to design a custom chip that optimizes CPU performance for AI applications, moving away from traditional GPU reliance [13][24]. Group 3: Market Strategy - The goal is to make AI technology accessible at consumer electronics price points, transforming the market from supercomputing to widespread use [18][25]. - By lowering the cost of AI computing solutions to around 100,000 yuan, the company aims to enable more startups to enter the AI space [19][25]. - The strategy includes using common components to promote widespread adoption and avoid creating high barriers to entry for other players in the industry [22][23]. Group 4: Competitive Landscape - 季宇 believes that simply following NVIDIA's path will not lead to success, emphasizing the need for innovative approaches to challenge established players [17]. - The company seeks to demonstrate the feasibility of its approach through proof-of-concept products, aiming to gain acceptance from industry players [14][18].

Group 1: Company Performance and Financials - The company achieved a revenue of 46,634.24 million CNY in 2024 and 10,245.83 million CNY in the first quarter of 2025 [3] - R&D investment over the last three fiscal years totaled 92,725.91 million CNY, accounting for 39.74% of the cumulative revenue during the same period [5] - In Q1 2025, R&D expenditure was 6,984.74 million CNY, representing 68.17% of the company's revenue [5] Group 2: Product Development and Market Strategy - The JM11 series products are being promoted for applications in servers, workstations, desktops, and laptops, but market demand and user experience may affect business outcomes [3] - The company focuses on innovation and product iteration to enhance market competitiveness and optimize product structure based on market needs [2] - The company is committed to developing high-performance GPU chips for various applications, including gaming, AI, and autonomous driving [5] Group 3: Industry Outlook and Competitive Position - The company emphasizes the importance of R&D management and efficiency to maintain competitiveness against peers [6] - There is a focus on attracting high-end R&D talent to strengthen the core development team and enhance product innovation capabilities [6] - The company is optimistic about the future of the GPU market, particularly in AI applications, and plans to continue investing in R&D [5]

编译 | 华尔街大事件 基于对NAND市场复苏的预期，分析师于2024年10月给予 Lam Research ( NASDAQ: LRCX ) "强力买入" 评级。相信技术升级周期将继续推动 该公司未来的增长。然而，由于 风险逐渐上升，分析师将其评级下调至"持有"，其合理价值为73美元。 4月23日，Lam Research公布了其 2025财年第三季度 业绩，如下图所示，营收增长24.2%，营业利润增长34.7%。强劲增长的动力源于市场对 其系统（包括用于沉积、蚀刻、清洗和其他晶圆制造的设备）的强劲需求。整体系统收入同比增长26.7%，值得关注。 强劲的系统增长反映了市场对NAND技术驱动的系统升级的强劲需求。正如 财报电话会议 中所强调的，DRAM和代工逻辑客户正在积极升级其 现有系统，以适应更先进的NAND技术，并满足对AI计算和GPU的强劲需求。管理层指出，鉴于Lam Research在先进NAND技术领域的强大市 场地位，系统升级可能会推动其未来的增长。 本季度的另一大亮点是管理层对全球晶圆厂设备 (WFE) 支出充满信心，预计2025财年支出约为1000亿美元。尽管地缘政治风险和关税战不断升 级， ...

以下文章来源于直面AI ，作者王木木 直面AI . 聚焦前沿科技，抢先看到未来。 作者： 王木木 来源： 直面AI 剥离非核心资产，押注AI芯片与代工业务……上任14天后，陈立武首次以英特尔CEO的身份于英 特尔 Vision 2025活动上公开露面，发表了约30分钟的公开演讲，向外界传递出重振这家老牌芯 片巨头的决心。 自此，美国四大芯片巨头英伟达、AMD、博通和英特尔的"话事人"都成了华人。华人在硅谷的话 语权和影响力达到了史无前例的高峰。 随着华人CEO的崛起，曾经由印裔CEO主导硅谷的权力格局，也迎来了新的改变。 印度人掌权硅谷的里程碑事件发生在2014年。46岁的萨提亚·纳德拉（Satya Nadella）接替鲍 尔默执掌微软，成为首个领导全球科技巨头的印裔CEO。 事实上，当纳德拉接替鲍尔默执掌微软时，印裔CEO已占据硅谷科技巨头1/3的席位。 印裔CEO走向硅谷权力中心，既是时间的复利，也是时代的注脚。 图源：AI制作 20世纪80年代起，技术移民进入硅谷的印度裔工程师们从基层程序员做起，逐步晋升为中高层管 理者，开始主导公司的关键项目。 AMD 的苏姿丰和博通的陈福阳也都成为硅谷科技巨头们的" ...

上海AI产业版图。 作者 | 刘博 报道 | 投资界PEdaily 这是一笔鲜少被关注到的融资。 投资界获悉，近日上海国投先导人工智能产业母基金联合领投壁仞科技，数家知名投资 机构及产业资本跟投。值得一提的是，这是上海国投先导人工智能产业母基金首个直投 项目。 壁仞科技，创投圈并不陌生——身后掌舵者是哈佛博士张文，他职业生涯始于华尔街， 后来回国连续创业，于2019年创立壁仞科技，闯入AI芯片赛道。走过五年历程，壁仞科 技经历着种种风波，至今已融资超50亿元。 由此望去，上海AI产业版图一一浮现。 张文带队 壁仞要去IPO了 张文，无疑是壁仞科技的灵魂人物。 他拥有哈佛大学法学博士学位，早年前曾是华尔街一名资深投资人。2011年，中芯国际 创始人张汝京再度创业，张文受邀回国出任公司CEO。随后在2 0 1 8年，他又担任商汤科 技总裁，主导商汤科技总部落地上海。 目睹着AI芯片的发展大势，张文心中也萌生创业的念头。在他看来，每一个AI应用的底 层硬件都是GPU，GPU会成为像水电煤一样，成为无处不在的基础设施。而中国最大的 优势之一是数据，AI芯片恰恰最需要场景和数据迭代。在《澎湃新闻》的采访中，张文 曾透露 ...

Core Viewpoint - Apple's decision to purchase approximately $1 billion worth of NVIDIA's GB300 NVL72 GPU cluster servers marks a significant shift in its AI strategy, acknowledging the advantages of the GPU ecosystem and generative AI paradigm over its self-developed chips [1][3][21]. Group 1: Apple's Shift to NVIDIA - Apple has historically relied on its self-developed chips, achieving great success with its Apple Silicon series in mobile and edge computing [3]. - The recent order for NVIDIA's GPUs indicates Apple's recognition of the GPU ecosystem's superiority in the generative AI space, driven by urgent market demands for high-performance computing [3][4]. - Analysts suggest that Apple plans to order around 250 NVL72 servers, with each server costing between $3.7 million and $4 million, totaling nearly $1 billion [3]. Group 2: Implications of Siri's Performance - Siri's declining competitiveness against rivals like Google Assistant and Alexa has prompted Apple to reassess its AI hardware strategy [4][5]. - The anticipated updates to Siri have been delayed, reflecting the challenges Apple faces in enhancing its AI capabilities [4][5]. Group 3: Generative AI and Market Dynamics - The rise of generative AI has redefined user expectations for intelligent assistants, shifting from simple command execution to intelligent collaboration [5]. - Apple's investment in NVIDIA GPUs is speculated to support the development of an Apple LLM, enhance Siri, and integrate AI into various applications [5][6]. Group 4: GPU vs. ASIC - The choice of NVIDIA's GPUs over self-developed ASICs highlights the critical importance of time and performance in the AI race, with NVIDIA's established ecosystem providing immediate solutions [8][16]. - NVIDIA's GPUs have become the de facto standard for training large language models (LLMs), showcasing their performance and ecosystem maturity [8][11]. - The high cost of NVIDIA GPUs, which have surged to $90,000 each, reflects their dominant market position, with NVIDIA reporting a revenue of $39.3 billion and a gross margin exceeding 70% [8][9]. Group 5: Future Outlook - Despite the current preference for NVIDIA GPUs, Apple may still pursue a hybrid strategy, utilizing NVIDIA for model training while relying on its own chips for inference [6][19]. - The ongoing competition between ASICs and GPUs suggests that while ASICs may face challenges now, they are not entirely out of the picture for future applications [19][21].