自去年AI专用加速芯片（ASIC，应用专用集成电路）竞赛打响以来，这场争夺"AI定制芯片霸 主"的比赛，很快演变成了双雄争霸的格局。 不过，现在分析师对Marvell的看法开始明显转向积极。近期，一家新的重要客户传出了利好信 号——这家客户似乎终于要加大投入，专注于定制AI芯片的开发。如果一切顺利，这将成为 Marvell 2026年业绩展望的重大利好。 因此，分析师现在将Marvell的评级上调至 "Strong Buy"。 博通（Broadcom, AVGO） 一直是行业的绝对领导者，掌握着AI工作负载ASIC市场的最大份 额；美满科技（Marvell Technology, MRVL） 在去年也取得了不错的表现。 但随着越来越多芯片制造商的加入，这块高利润的"定制AI加速芯片市场"竞争骤然加剧，直接打 乱了Marvell原本想拿下20%市场份额的目标。此外，由于Marvell的一家重要客户出现问题，该 客户将部分ASIC订单转给了其他厂商，也让公司业绩承压。 今年以来，市场对Marvell的态度一直偏中性。原因很简单： 公司未能充分把握住AI定制加速芯 片的巨大市场机会。 相较于竞争对手博通那种"压倒性 ...

Core Viewpoint - The article emphasizes the necessity for cloud vendors to continuously invest in computing power, models, chips, and ecosystems to build a "super AI cloud" [2][25]. Group 1: AI Cloud Competition - AI cloud has become a new entry ticket in the cloud computing arena, crucial for vendors to escape price wars and rebuild competitive advantages [2]. - The competition for "AI Cloud No. 1" is intensifying among domestic cloud vendors, with the focus on market leadership becoming a core industry concern [2]. - Globally, only four major players remain in the AI cloud space: AWS, Microsoft, Google, and Alibaba Cloud [2][11]. Group 2: Evaluation Criteria for AI Cloud Leaders - The evaluation of who is the "AI Cloud No. 1" depends on various standards, with models being a key factor for some [5][6]. - The article outlines four critical questions to assess the capabilities of AI cloud vendors: 1. Annual infrastructure investment of at least 100 billion [6]. 2. Possession of million-level large-scale computing clusters and cloud scheduling capabilities [8]. 3. Availability of top-tier large model capabilities that perform across various scenarios [9]. 4. Strategic layout of AI chip computing power [10]. Group 3: Capital Expenditure Insights - Major cloud vendors like Google, Microsoft, and AWS have significantly increased their capital expenditures to meet the explosive growth in AI infrastructure demand, with Google raising its annual target to $85 billion [6][7]. - Alibaba's capital expenditure for 2024 is projected at 76.7 billion RMB, significantly lower than its competitors, indicating a disparity in financial strength [10]. Group 4: Development Models - Two primary development models are identified: "Cloud + Ecosystem" (AWS and Microsoft) and "Full Stack Self-Research" (Google and Alibaba) [12][19]. - The "Cloud + Ecosystem" model allows vendors to leverage external models, reducing R&D costs and risks while increasing platform attractiveness [14][15]. - The "Full Stack Self-Research" model involves significant upfront investment but can create a strong competitive moat and higher long-term value [19][20]. Group 5: Alibaba Cloud's Position - Alibaba Cloud is positioned as a representative of the "Full Stack Self-Research" model in the Eastern context, competing closely with Google Cloud [25]. - The company plans to invest over 380 billion RMB in cloud and AI hardware infrastructure over the next three years, demonstrating a commitment to enhancing its capabilities [24]. - Alibaba Cloud's strategy includes embracing open-source models, creating a large AI model community, and addressing hardware constraints through software ecosystem development [24][25].

Core Viewpoint - NVIDIA's CEO Jensen Huang asserts that NVIDIA's growth will continue to outpace that of Application-Specific Integrated Circuits (ASICs), citing a high failure rate among ASIC projects and emphasizing NVIDIA's technological advancements and cost optimization [2][3]. Group 1: NVIDIA's Market Position - Huang believes that while many companies are developing ASICs, about 90% will fail, similar to the high failure rate of startups [2]. - NVIDIA is not overly concerned about the competition from ASICs, as they recognize that without NVIDIA, the computing field cannot thrive [3]. - Huang emphasizes that the development of ASICs is not the main challenge; rather, the deployment requires significant investment and expertise, which NVIDIA possesses [4]. Group 2: NVLink Fusion Announcement - NVIDIA introduced NVLink Fusion, a technology aimed at integrating third-party CPUs and accelerators with NVIDIA's ecosystem, allowing for semi-custom designs [5][7]. - NVLink Fusion enables non-NVIDIA CPUs to connect to NVIDIA GPUs via a short-distance chip-to-chip connection, enhancing flexibility for system vendors [9][11]. - The technology is seen as a step towards allowing third-party chip manufacturers to integrate their designs with NVIDIA's high-performance NVLink network [15]. Group 3: Industry Collaboration - Companies like Alchip, AsteraLabs, Marvell, and MediaTek are confirmed to be developing accelerators that will support NVLink Fusion, indicating a growing ecosystem around NVIDIA's technology [15]. - Fujitsu and Qualcomm are also working on new CPUs that will pair with NVIDIA GPUs, aiming to enhance efficiency through NVLink Fusion [15]. - Cadence and Synopsys are participating as technical partners in the NVLink Fusion initiative, providing IP blocks and design services to companies looking to build compatible hardware [16].

Core Viewpoint - NVIDIA's CEO Jensen Huang asserts that NVIDIA's growth will continue to outpace that of Application-Specific Integrated Circuits (ASICs), citing a high failure rate among ASIC projects and emphasizing NVIDIA's rapid technological advancements and cost optimization [1][2][3]. Group 1: NVIDIA's Market Position - NVIDIA is not concerned about being marginalized in the AI market, recognizing its essential role in the computing field [2]. - Huang believes that most ASIC projects will be canceled if they do not outperform existing chips, indicating a competitive landscape where NVIDIA's technology remains superior [2][3]. Group 2: NVLink Technology - NVIDIA has introduced NVLink Fusion, a new technology aimed at integrating third-party CPUs and accelerators with NVIDIA's ecosystem, enhancing flexibility for system suppliers [5][7]. - NVLink has evolved since its introduction in 2016, significantly increasing bandwidth and enabling faster interconnects between GPUs [6][9]. Group 3: Future Developments - The NVLink Fusion initiative allows for semi-custom designs, enabling third-party chips to connect with NVIDIA GPUs, although it remains proprietary [10][14]. - Companies like Fujitsu and Qualcomm are developing CPUs that will support NVLink Fusion, aiming to improve efficiency and performance [16]. Group 4: Industry Collaboration - Cadence and Synopsys are participating as technical partners in the NVLink Fusion program, providing IP blocks and design services to companies looking to build compatible hardware [17].