中经记者罗辑上海报道 当然，整体而言，仍需正视当前AI算力基座、机器人行业在规模、长期生存力、算法等相关方面，与 海外发达国家存在的一定差距。但国内科创企业正在迎难而上，正通过应用端牵引、协同研发和持续投 入创新，有望快速缩小差距。 算力基座多领域突破显现 在下游客户对先进制程工艺和自主可控的迫切性增强、需求快速增长的背景下，在AI算力基座领域， 从芯片设计到存储、EDA（电子设计自动化）软件，海光信息、佰维存储、概伦电子正在为中国AI的 加速发展提供关键支撑。 "海光信息作为拥有CPU（高端通用处理器）与GPGPU（协处理器）双产品线的企业，已成功实现了多 代际产品的研发创新和商业化落地，广泛应用于金融、通信、互联网、教育等关键行业。"海光信息架 构师王博士介绍，除产品线外，海光信息已布局形成"芯片设计—整机交付—软件生态"的完整链条。 "从以'计算为主'转向'AI强化'——大幅提升AI计算性能，公司提前布局高带宽低延迟的相关技术，并与 上下游产业链深度协作。"王博士称。 佰维存储则聚焦半导体存储，产品覆盖NAND Flash（一种非易失性存储技术）与DRAM（动态随机存 取存储器），深耕手机、个人计算机、 ...

中国人工智能、机器人行业发展如何？核心企业取得了哪些重要成果或进展？国内企业和国际企业技术 差距多大？产业链上下游协同如何？…… "从以'计算为主'转向'AI强化'——大幅提升AI计算性能，公司提前布局高带宽低延迟的相关技术，并与 上下游产业链深度协作。"王博士称。 佰维存储则聚焦半导体存储，产品覆盖NAND Flash（一种非易失性存储技术）与DRAM（动态随机存 取存储器），深耕手机、个人计算机、服务器三大传统市场，并在AI穿戴、工业与智能汽车两大新兴 领域快速突破。 "2024年，佰维存储AI新兴端侧业务收入超10亿元，且具备主控芯片设计、封测等全产业链服务能 力。"佰维存储总经理何瀚表示，AI本身的发展对存储提出了更高的要求。同时AI带来了一些新的应 用，需要开发新的解决方案。此外，AI时代存和算需要更多的整合。 2025年9月10日下午，上海浦东香格里拉大酒店南京厅内，一场聚焦人工智能与机器人产业链的闭门研 讨会如期举行。围绕"左手是AI（人工智能）的基座，右手是机器人和应用"的多项议题，来自AI算力基 座、机器人领域及AI应用领域的科创板上市公司核心高层，与众多机构投资者进行了深入交流。其 中，上 ...

公众号记得加星标⭐️，第一时间看推送不会错过。 可 以 肯 定 的 是 ， 纳 米 尺 寸 越 小 ， 芯 片 就 越 先 进 。 当 今 最 先 进 的 主 流 芯 片 是 3 纳 米 工 艺 ， 例 如 苹 果 iPhone和其他消费设备中使用的芯片。 自2022年ChatGPT首次亮相以来，GPU已成为人工智能的关键——这使英伟达的价值增长了十倍， 并使其成为全球市值第二高的公司。尽管印度拥有强大的芯片设计人才，但缺乏自主研发的GPU专 利，导致其在核心人工智能技术方面依赖美国公司。印度正在寻求改变这种依赖。 上述第二位官员表示，该芯片的早期预览版将于2025年底展示，Minthad上个月已报道过此事。然 而，即使C-Dac开发出2纳米芯片，印度在未来五年内也不太可能拥有一家能够生产此类芯片的国内 制造工厂，因此"我们很可能会与台积电（TSMC）合作进行大规模生产"，第二位官员表示，并补充 说，印度GPU的成本"将比英伟达目前的芯片零售价低50%"。 截至发稿时，一封要求Meity和C-Dac就此事置评的电子邮件仍未得到回复。 芯片客户 诚然，印度历来是美国芯片制造商英特尔、AMD、高通和英伟达的客户， ...

证券研究报告 [Table_MacroStrategy] 海外周报 20250309：紧财政冲击美股情绪，非农暂缓衰退担忧 核心观点：本周公布的美国经济数据喜忧参半，非农就业略不及预期但走 弱幅度相对可控，一定程度上缓解了近期市场对于美国经济过于悲观的 预期。但同时，特朗普及其内阁"紧财政"思路逐步浮出水面，欧美财政 叙事的分化给美股市场情绪带来更大的冲击，美元、美股大跌。下周关注 2 月美国 CPI 和 3 月 14 日再度到来的政府停摆风波。向前看，我们认为 美国经济仍有韧性，但特朗普政策对市场情绪的扰动仍是当前大类资产 的主导因素。虽然短期美国财政赤字实质性削减的空间十分有限，但考虑 到本届共和党在众议院较为微弱的优势、债务上限的掣肘等因素，不可忽 视"紧财政"预期对市场情绪和风险资产的打压。 宏观量化经济指数周报 20250309：新增贷款：2 月同比少增，1-2 月同比 持平 2 月国内挖机销量同比增长 99.4%，指向项目开工进程或开始提速 宏观点评 20250307：财政扩张令德国国债利率飙升 东吴证券晨会纪要 东吴证券晨会纪要 2025-03-14 宏观策略 德国国债利率飙升：德国新领导人在周 ...

证券研究报告 东吴证券晨会纪要 东吴证券晨会纪要 2025-03-13 宏观策略 [Table_MacroStrategy] 海外周报 20250309：紧财政冲击美股情绪，非农暂缓衰退担忧 核心观点：本周公布的美国经济数据喜忧参半，非农就业略不及预期但走 弱幅度相对可控，一定程度上缓解了近期市场对于美国经济过于悲观的 预期。但同时，特朗普及其内阁"紧财政"思路逐步浮出水面，欧美财政 叙事的分化给美股市场情绪带来更大的冲击，美元、美股大跌。下周关注 2 月美国 CPI 和 3 月 14 日再度到来的政府停摆风波。向前看，我们认为 美国经济仍有韧性，但特朗普政策对市场情绪的扰动仍是当前大类资产 的主导因素。虽然短期美国财政赤字实质性削减的空间十分有限，但考虑 到本届共和党在众议院较为微弱的优势、债务上限的掣肘等因素，不可忽 视"紧财政"预期对市场情绪和风险资产的打压。 宏观量化经济指数周报 20250309：新增贷款：2 月同比少增，1-2 月同比 持平 [Table_FixedGain] 固收点评 20250312：1.9%以上的 10 年期国债具有配置价值 10 年期国债收益率的合理点位在 2.0-2.2% ...

Investment Rating - The report maintains an "Overweight" investment rating for the electronic industry [1] Core Viewpoints - The competition between GPGPU and ASIC chips is highlighted, with ASICs focusing on low-precision tasks and showing better power efficiency, but still lagging behind GPGPU in certain performance metrics [5][8] - Major companies are increasingly investing in self-developed AI chips to meet the growing demand for AI applications, with significant capital expenditures expected to cover initial development costs [5][16] - The report recommends investing in companies like Cambricon and Haiguang Information, while also suggesting to pay attention to ZTE, Aojie Technology, and Chipone [5] Summary by Sections 1. GPGPU vs ASIC Performance Comparison - ASICs primarily target low-precision data types, which are sufficient for large model training, while GPGPU excels in high-precision tasks [8] - In terms of power efficiency, ASICs generally have better power control and efficiency ratios compared to GPGPU [8][11] - GPGPU's memory bandwidth and capacity still surpass those of ASICs, although ASICs have higher computational density [11][12] 2. Reasons for Major Companies Developing AI Chips - The cost structure for chip companies includes employee salaries, EDA and IP costs, manufacturing expenses, and sales costs, with salaries making up a significant portion [16][17] - The report estimates that a digital chip Fabless company requires approximately 9.7 billion yuan for salaries alone for a development team [17][18] - The demand for AI inference is expected to grow significantly, with major companies building large-scale clusters to support this demand [18][19] 3. Who Can Manufacture AI Chips for Major Companies? - Broadcom is identified as a leader in AI interconnect technology, with a strong IP ecosystem and significant market share in AI custom chip services [21][24] - Marvell is noted for its rapid growth in the AI chip market, with a significant increase in AI-related revenue and partnerships with major cloud service providers [25][27] - AIchip is recognized for its advanced 3DIC and process technology, addressing efficiency and performance challenges in AI and high-performance computing [28][29]

Investment Rating - The report maintains an "Overweight" investment rating for the electronic industry [1]. Core Viewpoints - The competition between GPGPU and ASIC chips is highlighted, with ASICs focusing on low-precision tasks and showing better power efficiency, but still lagging behind GPGPU in certain performance metrics [5][8]. - Major companies are increasingly investing in self-developed AI chips to meet the growing demand for AI applications, with significant capital expenditures expected to cover initial development costs [5][16]. - The report recommends investing in companies like Cambricon and Haiguang Information, while also suggesting to pay attention to ZTE, Aojie Technology, and Chipone [5]. Summary by Sections 1. GPGPU vs. ASIC Performance Comparison - ASICs primarily target low-precision data types, which are sufficient for large model training, while GPGPU excels in high-precision tasks [8]. - ASICs have better power control and efficiency for specific tasks, but GPGPU still outperforms in certain metrics, such as NVIDIA's GB200 [8][11]. - The report notes that ASICs have high computational density but face challenges in memory bandwidth compared to GPGPU [5][11]. 2. Reasons for Major Companies to Develop AI Chips - The report outlines the cost structure of chip companies, emphasizing that employee salaries constitute a significant portion of expenses [16][17]. - It estimates that a digital chip Fabless company requires approximately 9.7 billion yuan for employee salaries over a two-year product development cycle [18]. - The demand for AI inference is expected to grow significantly, with NVIDIA reporting that 40% of its data center revenue comes from inference business [18] . 3. Who Can Manufacture AI Chips for Major Companies? - Broadcom is identified as a leader in AI interconnect technology, with a strong IP ecosystem and significant market share in AI custom chip services [21][24]. - Marvell is noted for its rapid growth in the AI chip market, with a significant increase in AI-related revenue and partnerships with major cloud service providers [25][27]. - AIchip is recognized for its advanced 3DIC and process technology, which addresses efficiency and performance challenges in AI and high-performance computing [28][29].