01服务器CPU，变革丛生 "2030 年，数据中心服务器半导体价值将达 5000 亿美元。"市场研究机构 Yole 的这一预测，正指向一个快速膨胀的赛道。而 IDC 半导体研究副总裁 Mario Morales 的判断更直接："未来五年，数据中心将成为半导体全行业增长最快的领域。" 数据中心这条赛道，好像没人不看好。 在服务器 CPU 领域，x86、ARM、RISC-V 正在进行一场静默的架构革命。不同架构的市场里，各自酝酿着不寻常的变局。 x86架构，权力正在重构 服务器CPU作为数据中心的核心组件，其重要性和复杂性正日益凸显。 过去十余年里，x86架构在服务器CPU中占据主导地位，其中英特尔凭借深厚的技术积累与完善的生态布局，多年来几乎独霸这片市场，形成难以撼动的 行业格局。 不过，这样的稳固态势正在悄然松动。 服务器CPU市场份额 | 时间 | 英特尔 | AMD | | --- | --- | --- | | 2021年01 | 91.1% | 8.9% | | 2021年02 | 90.5% | 9.5% | | 2021年Q3 | 89.8% | 10.2% | | 2021年Q4 | 89.3 ...

智通财经APP获悉，近日，ARM(ARM.US)召开了2026财年第一季度的财报电话会。公司提到，云计算 领域，超7万家企业在Arm Neoverse数据中心芯片运行人工智能工作负载，同比增长40%，自2021年激 增14倍。去年Arm在超大规模数据中心的市场份额约18%，今年预计接近 50%。 目前公司正在开发的许多小芯片(chiplets)大多基于 Arm 的知识产权(IP)，而且已通过 Arm Total Design 生态系统为小芯片的开发提供支持。基于此，公司目前正在评估超越现有平台、向更多子系统、小芯片 领域拓展，或者推出完整解决方案的可行性。在公司内部对设计、实现并制造诸如小芯片(Chiplet)等产 品所需的全部专业知识和技术有深入了解，或者能够直接运用这些知识和技术。 中国市场方面，Arm中国市场与全球同步，在智能手机、自动驾驶、数据中心等领域的发展势头与全球 一致。Q1中国业务增长稳健，收入占比21%，较上一季度(15%)和去年同期(14%)均有提升，与全球增 长态势一致。 Q&A 问答 Q：请问 Arm 在 ASIC 及完整终端解决方案方面的战略有何可分享之处? A：目前暂无具体信息宣布 ...

Core Viewpoint - The data center processor market is undergoing significant shifts, with AMD rapidly gaining market share in the x86 segment and Arm-based CPUs expected to grow substantially, driven by major players like NVIDIA and large-scale enterprises [3][4][6]. Group 1: AMD's Market Position - AMD is quickly capturing Intel's market share in the x86 data center processor space, with its share rising to 40% from nearly zero in 2018 [6]. - AMD's data center revenue for Q1 2025 is projected to be $3.7 billion, with server CPUs accounting for approximately $2.5 to $3 billion of that [8]. - The total shipment of server CPUs for AMD and Intel in Q3 2024 is estimated at 5.5 million units, indicating a significant annual volume [8]. Group 2: Growth of Arm-based CPUs - Arm-based CPUs are expected to capture about 15% of the data center market by the end of 2024, with projections suggesting this could rise to nearly 50% by the end of 2025 [8][12]. - Major cloud providers like Amazon, Google, and Microsoft are developing their own Arm-based CPUs, which are reported to offer significant performance and energy efficiency improvements over x86 [11][14]. - The shift towards Arm is driven by the need for optimized performance for specific workloads, particularly in large-scale computing environments [10]. Group 3: Future Projections - By 2030, the total number of data center CPUs is expected to reach approximately 48 million, with Arm's share potentially rising to 19 million [22]. - The growth of AI workloads is projected to increase by 3.5 times, while non-AI workloads will grow by 1.7 times, further influencing the demand for Arm CPUs [21]. - The trend indicates that large-scale enterprises will increasingly transition workloads from x86 to Arm, particularly as the cost-effectiveness and performance of Arm CPUs improve [22].

Group 1 - Evercore has reaffirmed "outperform" ratings for Arm Holdings and AMD based on recent market share data indicating growth in their CPU market shares [1] - Arm's market share in the server segment increased by 4% year-over-year to 10%, while AMD's share rose by 2.3% to 24% [1] - Intel's server market share declined by 6.3% year-over-year to 65%, with a quarter-over-quarter decrease of 2.1% [1] Group 2 - Looking ahead, Arm is expected to continue expanding its presence in the server market, with management projecting that up to 50% of new server chips deployed in large-scale data centers this year will use Arm architecture [2] - Companies like Nvidia, Amazon, and Google are accelerating the adoption of Arm architecture in their products, which will benefit Arm directly [2] - AMD's average selling price (ASP) has shown resilience, declining only 2% year-over-year, compared to Intel's 11% drop during the same period [2]

Core Insights - The article discusses the significant acquisition of Annapurna Labs by Amazon for approximately $350 million, which has become crucial to Amazon's success, particularly in its AWS division and AI strategy [1][2][4] - Amazon's investment in AI infrastructure is projected to exceed $100 billion this year, highlighting its commitment to developing custom chips to reduce reliance on Nvidia [2][4] - The article emphasizes the strategic foresight of Amazon's leadership in recognizing the importance of chip design for cloud computing and AI [2][5][9] Group 1: Acquisition and Impact - Annapurna Labs was acquired by Amazon in a pivotal moment for AWS, contributing significantly to its profitability and revenue, which surpassed $100 billion last year [2][4] - The acquisition has allowed Amazon to develop custom chips like Trainium and Graviton, enhancing its AI capabilities and operational efficiency [10] Group 2: Strategic Vision and Development - Amazon's leadership, particularly Andy Jassy, recognized the need for in-house chip design to better control supply chains and offer competitive pricing to AWS customers [5][9] - The article highlights the role of James Hamilton, an Amazon executive, in advocating for chip design, which was initially met with skepticism [8][9] Group 3: Market Context and Competition - The current AI boom has led to a multi-trillion dollar "arms race" among cloud computing giants, with Amazon leading in capital expenditure for AI infrastructure [2][4] - The article notes that other tech companies, including Microsoft and Google, are also investing heavily in custom hardware to support their AI initiatives [2]

Core Insights - Amazon reported strong performance in AWS for FY25Q1, with operating profit reaching $11.5 billion and a profit margin nearing 40% [1] - The company plans to increase capacity in the second half of the year to meet growing demand, particularly in AI services [1][3] - AWS backlog orders stood at $189 billion, reflecting a year-over-year growth of approximately 20% [1][16] AWS Performance - AWS's operating profit margin is attributed to robust growth and continuous investment in innovation and technology [1][10] - The company is focusing on maximizing power usage efficiency in existing data centers, which helps reduce costs and recycles power for new workloads [1][10] - The annualized revenue from AI business has reached several billion dollars, growing at a triple-digit percentage year-over-year [3] Capital Expenditure - Cash capital expenditure for the first quarter was $24.3 billion, primarily to support the growing demand for technology infrastructure related to AI services [1] - Investments are increasingly directed towards custom chips like Trainium and supporting technology infrastructure for both North American and international operations [1] Retail Business and Tariffs - Company executives expressed uncertainty regarding future tariff levels and their timing, but noted no current signs of demand weakening [2][4] - Some categories have seen increased purchasing, possibly indicating consumer stockpiling ahead of potential tariff impacts [2] Strategic Focus - The company emphasizes maintaining low prices and a wide selection of products as a core strategy, especially in light of potential tariff increases [4][5] - The diversity of sellers on the platform provides opportunities for some to capture market share without passing on tariff costs to consumers [5] Supply Chain and AI Demand - Supply chain issues are expected to improve over the coming months, which will help alleviate capacity constraints [3] - The company is actively introducing more GPU instances to support the growing demand for AI workloads [3] Revenue Guidance and Cost Considerations - The company anticipates fluctuations in revenue due to various factors, including sales cycles and competition [7][9] - Historical trends indicate that stock-based compensation typically increases in the second quarter, impacting overall cost structure [6] Migration to Cloud - There is a renewed focus among enterprises on migrating workloads to the cloud, driven by the rise of AI and the need for innovation [17] - The average remaining duration of backlog orders is 4.1 years, indicating a long-term commitment to cloud migration [16]

《环球报》获悉，谷歌正在扩大其在以色列的芯片开发业务，目前正招聘数十名员工开发一种新型 通信芯片——网络接口卡（NIC），这是用于 AI 处理的核心处理器和图形处理器之间通信的基本组 件。此类芯片目前由博通、英特尔和 Nvidia 生产。 价格昂贵的商品 直到最近，谷歌开发网卡还被认为是一项简单的任务，但随着人工智能处理操作的激增，该组件对 于大型数据中心图形处理器之间的通信至关重要。它的价格一路飙升，"NIC"已成为一种有价值的商 品，Nvidia 以特别高的价格出售，作为由以色列公司 Mellanox 开发的一套通信芯片的一部分，该 公司五年前被 Nvidia 收购。Nvidia 正试图将其 NIC 组件与其他通信组件一起以看似打折的套餐形 式销售，但这些价格仍然让科技巨头难以盈利地部署人工智能技术。 如果您希望可以时常见面，欢迎标星收藏哦~ 来源：内容 编译自globes ，谢谢。 参考链接 四年前，谷歌成立了自己的芯片开发部门，以开发硬件组件，减少对英特尔、Nvidia和博通等外部 供应商的依赖。 在 OpenAI 等领先公司发布开发能够处理下一代语言模型处理任务的芯片的规范后，科技巨头们纷 纷竞相开 ...