结论与建议： 盈利预测：公司从五年前开始布局Chiplet 技术及其在生成式人工智能和智慧驾驶上的应用，并持续开 拓增量市场和具有发展潜力的新兴市场，拓展行业头部客户，未来有望实现业绩高速增长。我们预计公 司2025-2027 年营收30.6 亿元、54.1 亿元和84.7 亿元，YOY 分别增长32%、77%和57%，实现净利润 0.26 亿、2.7 亿元和6.4 亿元，EPS 分别为0.05元、0.51 元和1.21 元，目前股价对应2027 年PS 8 倍，考 虑到公司业绩潜力较大，维持买进建议。 公司公告，4Q25（10 月至12 月25 日）新签订单25 亿元，较4Q25 全期增长130%，环比3Q25 全期增长 56%。公司及行业高速增长趋势得到进一步验证。展望未来，互联网厂商持续增加AI 算力投入，打造 更为强大的计算体系，将加速互联网厂商在专用处理芯片ASIC 领域的布局，公司有望从行业变革中持 续受益。目前公司股价对应2027 年PS（市销率）8 倍，公司股价近期因股东减持及解禁股流通而调整 较多，构成长期买入机会，维持买入建议。 风险提示：AI 领域下游需求不及预期 大基金减持，长期影响有 ...

Core Insights - Intel's AI strategy is focusing on two main areas: ASIC and edge AI, aiming to regain competitiveness in the AI sector where it lags behind Nvidia and AMD [2][4] - The establishment of the Central Engineering Group (CEG) is intended to consolidate engineering talent and enhance the company's capabilities in ASIC and design services [4][5] Group 1: AI Strategy and Market Position - Intel has acknowledged its shortcomings in AI strategy, with former CEO Pat Gelsinger admitting that the company's approach has not been satisfactory [2] - The company is developing a power-optimized GPU for inference as part of its edge AI strategy, with products like Meteor Lake and Lunar Lake aimed at enhancing mobile SoC performance [2][3] - Intel's new ASIC department, led by Srini Iyengar, aims to provide customized chips for specific workloads, competing with established solutions like Google's TPU and Amazon's Trainium [3][4] Group 2: ASIC Business Development - The ASIC business is expected to play a crucial role in Intel's operations, with plans to offer a "one-stop" solution for customers seeking custom AI chips [4][6] - Intel's CEG will lead the development of ASIC and design services, expanding the application of its core x86 IP and leveraging its design advantages [5][6] - The company aims to differentiate itself by providing internal foundry services, which is a unique offering compared to competitors like Broadcom and Marvell [6][7] Group 3: Future Prospects and Challenges - If executed effectively, the custom chip business could become a significant revenue stream for Intel, positioning it as a system foundry responsible for every supply chain segment [7] - The competitive landscape in the AI market is intense, with companies like Broadcom continuously evolving, posing challenges for Intel to capitalize on this opportunity [7]

Core Viewpoint - Intel's AI strategy is shifting focus towards ASIC and edge AI, aiming to regain competitiveness in the AI sector where it lags behind Nvidia and AMD [2][6]. Group 1: AI Strategy and Market Position - Intel has historically dominated general computing and server-level computing but has struggled in the AI domain, as acknowledged by former CEO Pat Gelsinger [2]. - The company is working on a power-optimized GPU for inference as part of its edge AI strategy, with products like Meteor Lake, Lunar Lake, and the upcoming Panther Lake series [2]. - Intel plans to expand its edge product line with Crescent Island, a processor focused on inference, featuring LPDDR5X memory [2]. Group 2: ASIC Business Development - Intel has established a dedicated ASIC department under Srini Iyengar, aiming to provide customized chips for specific workloads, similar to Google's TPU and Amazon's Trainium [3][5]. - The ASIC business is expected to play a crucial role in Intel's operations, potentially opening new revenue streams and addressing past missteps in the AI market [5][6]. - The company aims to offer a "one-stop" solution for customers seeking custom AI chips, leveraging its chip technology expertise and internal foundry services [6]. Group 3: Competitive Landscape and Future Prospects - Intel's ASIC business is positioned to compete with companies like Broadcom and Marvell, focusing on custom network ASIC chips for network-intensive workloads [4][6]. - The centralized engineering group (CEG) is expected to reduce costs by integrating design services with manufacturing and packaging [6]. - If executed effectively, the custom chip business could become a significant revenue source for Intel, enhancing its position in the AI supply chain [7].

据《每日经济新闻》记者观察，从全球供应链调整到国内算力需求剧增，再到大模型商业化加速，AI 芯片正站在科技产业的战略风口。 12月8日，围绕百度此时选择评估分拆上市计划的原因及未来的商业化路径，记者向百度方面发去采访 提纲，但截至发稿尚未获对方回复。 IPO进程步入关键阶段 当下，市场对AI(人工智能)底层算力标的的追逐持续升温，百度集团-SW(HK09888，股价121.400港 元，市值3339.000亿港元)一纸公告再次搅动资本市场的神经。 12月7日晚间，百度集团SW(以下简称百度)发布公告称，公司正在评估旗下非全资附属公司昆仑芯(北 京)科技有限公司(以下简称昆仑芯)的拟议分拆及独立上市计划。倘进行拟议分拆及上市，将须经相关监 管审批程序，而公司并不保证拟议分拆及上市将会进行。 据此前市场消息，百度计划最早于2026年一季度向港交所递交昆仑芯的上市申请，目标是在2027年初完 成IPO(首次公开募股)。 在12月7日发布的公告中，百度表示，正在评估旗下非全资附属公司昆仑芯的拟议分拆及独立上市计 划。 记者注意到，当前昆仑芯在百度AI全栈技术体系中处于底层位置，为飞桨深度学习框架、文心大模型 及搜索等 ...

Nvidia graphics processing units like these latest Blackwell [music] GPUs are inside server racks all over the world. Nvidia has catapulted [music] from gaming giant to the very core of generative AI, training the models, running the workloads, and sending Nvidia's valuation soaring. [music] With 6 million Blackwell GPUs shipped over the last year, >> this [music] connects all 72 GPUs, allowing to act as a single GPU to power the most advanced AI workloads.[music] GPUs are the generalpurpose workhorse stars ...

Core Viewpoint - The AI chip market is experiencing significant shifts, with Google accelerating the commercialization of its self-developed AI chip, TPU, which may disrupt the dominance of NVIDIA's GPUs in the computing power market [1][3]. Group 1: Google's TPU Development - Google has been developing TPU since 2013, primarily for internal AI workloads and Google Cloud services, but is now pushing for external commercialization, with potential contracts worth billions [3]. - Meta is considering deploying Google's TPU in its data centers starting in 2027, with the possibility of renting TPU capacity through Google Cloud as early as next year [3]. - Google's TPU strategy aligns with its long-term "soft-hard integration" approach, aiming to reduce energy consumption and control costs amid rising training costs for large models [3]. Group 2: NVIDIA's Market Position - NVIDIA currently holds over 90% of the AI chip market share and emphasizes its "one generation ahead" and "all-scenario advantages" in response to competition from Google's TPU [3][4]. - Despite the potential entry of TPU into large-scale data centers, NVIDIA maintains that GPUs will not be replaced in the short term, as both TPU and NVIDIA GPUs are experiencing growing demand [1][4]. Group 3: Industry Trends - The industry is moving towards a heterogeneous deployment of ASICs and GPUs, rather than a single architecture dominating the market [2][5]. - Major tech companies, including AWS and Microsoft, are also developing their own AI chips, indicating a broader trend of companies seeking to control their computing power [5][6]. - The collaboration between Anthropic and both NVIDIA and Google highlights a shift towards a diversified supply chain for AI computing power, as companies are reluctant to rely solely on one chip architecture [6]. Group 4: Market Reactions - Following news of Google's TPU commercialization, NVIDIA's stock experienced significant fluctuations, reflecting market reassessment of GPU's future share and profitability in AI infrastructure [7]. - The AI infrastructure industry is transitioning from hardware competition to system-level competition, influenced by changes in software frameworks, model systems, and energy efficiency [7].

Core Insights - Major international companies are investing heavily in AI self-developed chip markets, creating new business opportunities. MediaTek is leveraging its years of R&D strength to enter the ASIC design service market, targeting high-end orders and expanding into the AI sector within cloud data centers [1][2]. Group 1: Market Potential and Growth - MediaTek has revised its total addressable market (TAM) for data center ASICs from $40 billion to $50 billion, driven by increased capital expenditures from cloud service providers [2]. - The company aims to capture a market share of approximately 10% to 15% within the next two years, with expectations of stable growth even if its market share remains constant [2]. - The first ASIC project is expected to contribute several billion dollars in revenue starting in 2027, with a second project anticipated to begin generating revenue in 2028 [2][6]. Group 2: Technological Advancements - MediaTek is actively investing in high-speed interconnects and silicon photonics, focusing on chip-to-chip and chip-to-rack connectivity, while also advancing 2nm process technology and 3.5D packaging [3]. - The company emphasizes its long-term technological foundation and R&D investments as key advantages in the ASIC field, enhancing its capabilities in data center technology and communication with local customers [2][6]. Group 3: Competitive Landscape - The AI ASIC market is projected to grow from $12 billion in 2024 to $30 billion by 2027, with a compound annual growth rate (CAGR) of 34% [5]. - Major tech giants, including Google, Tesla, Amazon, Microsoft, and Meta, are all investing in ASIC chip development, indicating a competitive and rapidly evolving market [5]. - MediaTek's collaboration with Google to develop the next-generation TPU, expected to be produced by 2026, highlights the strategic partnerships forming within the industry [6].

Group 1 - The core viewpoint highlights the strong performance of the STAR Market 100 Index, with significant gains in semiconductor and AI-related stocks, driven by increased capital expenditures from major cloud service providers [1][2] - The STAR 100 ETF has shown a 1.98% increase, indicating positive market sentiment and potential for continued growth in the tech sector [1] - Major cloud service providers are expected to collectively exceed $420 billion in capital expenditures by 2025, reflecting a robust investment trend in AI and cloud technologies [1] Group 2 - Google is building a self-sufficient ecosystem from chip development (TPU v7p) to application deployment (Gemini 3.0), positioning itself to regain market leadership in AI [2] - The deployment of TPU chips has significantly reduced inference costs, contributing to a stable recovery in Google's search market share, which has risen to over 90% [2] - ASICs are projected to gain market share over GPUs, with TPU v7 requiring more optical modules compared to NVIDIA's offerings, suggesting a shift in capital expenditure dynamics [2] Group 3 - The STAR 100 Index comprises 100 medium-sized, liquid stocks selected from the STAR Market, reflecting the overall performance of different market capitalization companies [3] - As of October 31, 2025, the top ten weighted stocks in the STAR 100 Index account for 25.77% of the index, indicating concentrated investment in key players [3]

Core Insights - The primary value of Google's TPU lies not only in its speed but also in its profit margins, allowing the company to bypass the "Nvidia tax" and significantly reduce computing costs [1][17][18] - Google's TPU v7 is positioned as a formidable competitor in the AI chip market, showcasing substantial advancements in performance and efficiency compared to Nvidia's offerings [5][14][20] Background and Development - The inception of TPU was driven by a critical need for enhanced computational capacity to support Google's services, leading to the decision to develop a custom ASIC chip tailored for TensorFlow [6][7][8] - The rapid development cycle of TPU, from concept to deployment in just 15 months, highlights Google's commitment to innovation in AI technology [8] Architectural Advantages - TPU's architecture is designed for efficiency, utilizing a "Systolic Array" that minimizes data movement and overcomes the "von Neumann bottleneck," resulting in superior energy efficiency compared to traditional GPUs [10][11][12] - The TPU v7 demonstrates a significant leap in performance metrics, achieving a BF16 computing power of 4,614 TFLOPS, a tenfold increase from its predecessor [15] Competitive Landscape - The TPU v7's specifications, including a single-chip HBM capacity of 192GB and a memory bandwidth of 7,370 GB/s, position it competitively against Nvidia's Blackwell series [16] - Google's strategic control over TPU design allows it to escape the high costs associated with Nvidia's GPUs, restoring higher profit margins for cloud services [17][18] Market Implications - As AI workloads shift from training to inference, the importance of Nvidia's CUDA may diminish, potentially benefiting Google's TPU ecosystem [19] - Analysts suggest that Google's dominance in large-scale computing and the performance of TPU v7 could redefine the competitive dynamics in the AI chip market, positioning Google as a key player capable of controlling its own destiny [20]

Core Insights - Major international companies are investing heavily in AI self-developed chips, creating new business opportunities. MediaTek is leveraging its years of R&D strength to enter the ASIC design service market, targeting high-end orders and expanding into the AI sector within cloud data centers [1][2]. Group 1: Market Potential and Growth - MediaTek has revised its total addressable market (TAM) for data center ASICs from $40 billion to $50 billion, driven by increased capital expenditures from cloud service providers [2][3]. - The company aims to capture a market share of approximately 10% to 15% within the next two years, with expectations of steady growth even if its market share remains stable [2][3]. Group 2: Project Developments - MediaTek's first ASIC project is expected to contribute several billion dollars in revenue starting in 2027, with a second project anticipated to begin generating revenue in 2028 [2][3]. - The company is actively engaging with a second large-scale data center operator to discuss new ASIC projects, indicating strong confidence in future business growth [1][2]. Group 3: Technological Advancements - MediaTek is investing in key areas such as high-speed interconnects and silicon photonics, alongside advancing 2nm process technology and 3.5D packaging to build a comprehensive high-performance computing platform [3]. - The company emphasizes its long-term technological foundation and R&D investments as key advantages in the ASIC field, enhancing its capabilities in design and supply chain management [2][3]. Group 4: Competitive Landscape - The AI ASIC market is projected to grow significantly, with estimates suggesting it will increase from $12 billion in 2024 to $30 billion by 2027, reflecting a compound annual growth rate of 34% [5]. - Major tech companies, including Google, Tesla, and Amazon, are heavily investing in ASIC chip development, indicating a competitive and rapidly evolving market landscape [5][6].