Nvidia may pleasantly surprise analysts and investors in the next few years.Nvidia (NVDA 0.65%) has delivered phenomenal growth powered by artificial intelligence (AI) over several years. I contend that the company could reach annual revenue as high as $1 trillion by the end of this decade (fiscal year 2031 ending January 2031).Here are some factors that support the claim. Growth catalystsAnalysts expect Nvidia's fiscal 2026 revenue to be around $213 billion. To hit annual revenue of $1 trillion in fiscal 2 ...

如果您希望可以时常见面，欢迎标星收藏哦~ 根据Wccftech 报导，传英伟达极可能成为台积电A16 制程（1.6 纳米）的唯一客户，并已将此 技术锁定于英伟达新一代GPU「Feynman」。 供 应 链 消 息 显 示 ， 英 伟 达 的 Rubin 与 Rubin Ultra 系 列 将 率 先 采 用 3 纳 米 ， 而 再 下 一 代 的 Feynman 则计画直接跨入A16。为配合此时程，台积电高雄P3 厂正加速建置，预计在2027 年 替英伟达启动量产。近期台积电扩充3 纳米产能，也被业界解读为因应英伟达大量拉货、并提 前为A16 布局。 A16 采用纳米片电晶体架构，并搭配SPR 背面供电技术，可释放更多正面布局空间、提升逻辑 密度并降低压降，其背面接面（Backside Contact） 亦能维持传统版图弹性，是业界首创的背 面供电整合方案。相较N2P，A16 在相同电压下速度提升8–10%，相同速度下降低15–20% 功 耗，芯片密度提升至1.10 倍，特别适合AI 与HPC 等高运算密度芯片。 除了台积电外，其他晶圆大厂也正加速布局背面供电技术。三星已在今年的晶圆代工论坛宣 布，将于202 ...

Core Insights - Major investments in AI infrastructure by global tech giants are driven by the urgent need for energy infrastructure upgrades and the new challenges posed by high-density computing on power supply [1] - The rapid development of renewable energy is outpacing the construction of grid infrastructure, necessitating accelerated grid development to keep up with power generation [1] - The rise of AI is providing new growth momentum for the power equipment industry, with data centers (AIDC) being central to AI infrastructure and their stable operation relying on energy supply [1] Group 1 - The traditional 415V AC systems are becoming inadequate due to increasing rack power density, leading to a potential shift towards 800V DC distribution systems [2] - The concentration of AC-DC conversion equipment is expected to rise, with the use of Solid State Transformers (SST) simplifying systems and aiding in carbon reduction for data centers [2] - The white paper from NVIDIA highlights significant fluctuations in rack power due to GPU power increases, presenting challenges for power supply and grid stability [2] Group 2 - Solutions proposed for managing power fluctuations include software optimization, energy storage systems, and limiting GPU performance, which opens up application spaces for supercapacitors and energy storage [2] - The development of new power systems, such as virtual power plants, is suggested to enhance system stability by matching electricity consumption with generation [2] - Companies with strong systemic solution capabilities are expected to gain a competitive advantage in this evolving landscape [2]

Investment Rating - The report initiates coverage of Jentech with a Buy rating and sets a target price of TWD3,186. It also reiterates Buy ratings for AVC and Auras with target prices of TWD1,700 and TWD1,160 respectively [15][16][44]. Core Insights - The rapid development of AI performance upgrades is expected to revolutionize the liquid cooling industry, particularly with the introduction of microchannel lid (MCL) technology and potential new thermal interface materials (TIM) from late 2026 to 2028 [3][6]. - The thermal design power (TDP) of mainstream AI chips is projected to increase to approximately 2,000W by mid-2026, with expectations that chips will exceed 3,000W by 2027 [6][19]. - MCL is anticipated to be the most practical solution for cooling chips with TDPs over 3,000W, as it integrates a heat spreader with a cold plate to minimize thermal resistance [7][27]. - Current thermal component makers are expected to experience significant growth opportunities in the next two to three years, driven by the increasing adoption of liquid cooling solutions across various AI systems [14][39]. Summary by Sections Liquid Cooling Technology - Liquid cooling technology is evolving rapidly, with strong total addressable market (TAM) growth expected to benefit both existing and new players [6][19]. - The transition from air cooling to liquid cooling is becoming mainstream, particularly for AI GPUs, with full liquid cooling solutions anticipated to dominate by 2025 [6][19]. Microchannel Lid (MCL) Technology - MCL is viewed as a critical advancement for next-gen AI server architecture, offering compatibility with existing single-phase liquid cooling systems and a lower Z-height for higher-density designs [7][27]. - The adoption of MCL may face challenges, including design and manufacturing complexities, but its potential for earlier adoption compared to two-phase cooling solutions is noted [8][28]. Thermal Interface Materials (TIM) - The report discusses the potential shift to indium metal TIMs for high-performance chips, particularly as TDP levels rise and current graphite film TIMs face limitations [10][38]. - Optimized lids with highly thermal-conductive TIMs are expected to remain favored solutions for upcoming AI chips, with ongoing research into new materials like silicon carbide (SiC) [9][37]. Company Coverage - Jentech is positioned as a leading beneficiary of MCL technology due to its strong relationships with foundries and experience in heat spreader manufacturing [16][42]. - AVC and Auras are also highlighted for their potential growth as liquid cooling solutions become more prevalent in AI systems, with both companies maintaining Buy ratings [15][44].

Investment Rating - The report maintains a "Positive" investment rating for the electronic industry, indicating an expected return that is stronger than the market benchmark by over 5% [5]. Core Insights - The AI server market continues to grow, driven by demand for AI computing power and hardware upgrades [7]. - System assembly is emerging as a new driver for performance enhancement in AI servers, as traditional manufacturing processes may not keep pace with the rapid development of AI computing needs [8]. - Advanced packaging techniques are becoming crucial for improving chip performance, especially as traditional process upgrades slow down [8]. - Industry leaders are expected to benefit from the rising technical barriers and improved competitive environment in the system assembly sector [8]. Summary by Sections AI Server Market Dynamics - The demand for AI computing facilities is driving growth in the AI server market, with significant upgrades in hardware [7]. - The number of GPUs in AI servers is increasing dramatically, with projections for future upgrades to 144 GPUs per cabinet by 2027 [8]. Performance Enhancement Drivers - The report highlights that system assembly is becoming a key factor in enhancing AI server performance, as the number of GPUs per server increases [8]. - The complexity of system assembly is rising, which may limit production capacity for some companies [8]. Recommended Investment Targets - The report recommends several companies related to AI server system assembly, including: - Industrial Fulian (601138, Buy) - Haiguang Information (688041, Buy) - Lenovo Group (00992, Buy) - Huaqin Technology (603296, Buy) [8]. - Industrial Fulian is noted for significant improvements in product testing and production efficiency, with strong order growth expected [8]. - Haiguang Information is positioned to leverage vertical integration capabilities following its merger with Zhongke Shuguang [8]. - Lenovo Group is anticipated to launch various servers based on Nvidia's Blackwell Ultra starting in the second half of 2025 [8]. - Huaqin Technology is recognized as a core ODM supplier for AI servers, benefiting from increased capital expenditures by cloud service providers [8].

Group 1 - Major semiconductor companies, including TSMC, Samsung, Micron, and SanDisk, have announced price increases for their products, reflecting the strong demand driven by the AI wave and data center construction [1][3] - TSMC plans to raise prices for its 3nm and 2nm process nodes, with the 2nm process expected to see a price increase of at least 50% compared to the 3nm process, significantly exceeding market expectations [2][3] - The price hikes in memory chips include a 30% increase for DRAM products and a 5% to 10% increase for NAND flash products from Samsung, driven by supply constraints and surging demand from cloud enterprises [3] Group 2 - TSMC's strong pricing strategy highlights its dominant position in the supply chain, with major clients like Apple and Nvidia relying on its advanced process technologies for their next-generation chips [3] - The stock prices of major memory chip manufacturers, including Samsung and SK Hynix, have risen in response to the price increases, indicating a positive market reaction [3] - The overall performance of the US stock market remains mixed, with major tech stocks experiencing declines, while semiconductor stocks show strength due to the price increase announcements [3]

Summary of Key Points from Conference Call Records Industry Overview - The focus is on the North American cloud computing industry, particularly major players like Google, Meta, Microsoft, and Amazon, and their capital expenditure (CapEx) related to AI and cloud services [1][2][4][5]. Core Insights and Arguments - **Capital Expenditure Growth**: North American cloud providers are expected to exceed $366 billion in total capital expenditure in 2025, reflecting a year-on-year increase of over 47%, driven primarily by Google, Meta, Microsoft, and Amazon [1][2]. - **Google's Investment**: Google raised its 2025 CapEx guidance from $75 billion to $85 billion, a 62% increase year-on-year, with further growth anticipated in 2026 [2][4]. - **Meta's Strategic Goals**: Meta aims for "super intelligence" and has established a dedicated lab for this purpose, indicating a potential CapEx nearing $100 billion by 2026, driven by five key business opportunities [1][7]. - **Microsoft and Amazon's Commitment**: Microsoft plans to maintain over $30 billion in CapEx for the next fiscal quarter, while Amazon expects to sustain its investment levels in the second half of 2025 [2][4]. - **AI Industry Resilience**: Despite concerns over the delayed release of OpenAI's GPT-5, the AI industry continues to innovate, with significant advancements from companies like Anthropic and Xai [1][10]. Additional Important Content - **PCB Market Volatility**: The PCB sector has experienced significant fluctuations due to discussions around COVF/SOP technology paths and increased CapEx expectations from cloud providers [1][14]. - **ASIC Supply Chain Outlook**: The ASIC supply chain is expected to see significant demand elasticity by 2026, with emerging companies like New Feng Peng Ding and Dongshan Jingwang poised to enter the market [3][16]. - **Technological Innovations in PCB**: Innovations such as cobalt processes are being explored to simplify PCB structures, although challenges like heat dissipation and chip warping remain [3][17]. - **Market Trends and Future Projections**: The AI industry's growth is projected to continue, with hardware demand expected to rise significantly by 2026, despite short-term market fluctuations [11][15]. - **Investment Opportunities**: There is a recommendation to monitor potential market pullbacks to capitalize on investment opportunities, particularly in the PCB sector and traditional NB chain stocks [12][15][24]. Conclusion - The North American cloud computing industry is poised for substantial growth in capital expenditures, particularly in AI-related investments. Major players are demonstrating strong confidence in the future of AI, with ongoing innovations and strategic investments shaping the landscape. The PCB and ASIC markets are also highlighted as areas of potential growth and investment opportunity.

Core Viewpoint - Morgan Stanley believes that the transition from CoWoS to CoWoP faces significant technical challenges, and the reliance on ABF substrates is unlikely to change in the short term [1][2][8] Group 1: Technical Challenges - The CoWoP technology requires PCB line/space (L/S) to be reduced to below 10/10 microns, which is significantly more challenging than the current standards of ABF substrates [5][6] - The current high-density interconnect (HDI) PCB has an L/S of 40/50 microns, and even the PCB used in iPhone motherboards only reaches 20/35 microns, making the transition to CoWoP technically difficult [5][6] Group 2: Supply Chain Risks - Transitioning from CoWoS to CoWoP could introduce significant yield risks and necessitate a reconfiguration of the supply chain, which is not commercially logical given the timeline for mass production [8] - TSMC's CoWoS yield rate is nearly 100%, making a switch to a new technology unnecessarily risky [8] Group 3: Potential Advantages of CoWoP - Despite the short-term challenges, CoWoP technology has potential advantages, including shorter signal paths, improved thermal performance suitable for >1000W GPUs, better power integrity, and addressing organic substrate capacity bottlenecks [10] - The goals of adopting CoWoP include solving substrate warping issues, increasing NVLink coverage on PCBs without additional substrates, achieving higher thermal efficiency without packaging lids, and eliminating bottlenecks in certain packaging materials [10]

Core Viewpoint - Navitas Semiconductor's GaN and SiC technologies have been selected to support NVIDIA's next-generation 800 V HVDC data center power infrastructure, which is designed to enhance power delivery for AI workloads and support 1 MW IT racks and beyond [1][15]. Group 1: Technology Collaboration - The collaboration between Navitas and NVIDIA focuses on the 800 V HVDC architecture, which aims to provide high-efficiency and scalable power delivery for AI workloads, improving reliability and reducing infrastructure complexity [2][4]. - Navitas' GaNFast™ and GeneSiC™ technologies will enable the powering of NVIDIA's GPUs, such as the Rubin Ultra, directly from the 800 V HVDC system [1][6]. Group 2: Advantages of 800 V HVDC - The existing data center architecture, which uses traditional 54 V in-rack power distribution, is limited to a few hundred kilowatts and faces physical limitations as power demand increases [3]. - The 800 V HVDC system allows for a reduction in copper wire thickness by up to 45%, significantly decreasing the amount of copper needed to power a 1 MW rack, which is crucial for meeting the gigawatt power demands of modern AI data centers [5][6]. - This architecture eliminates the need for additional AC-DC converters, directly powering IT racks and enhancing overall system efficiency [6][13]. Group 3: Performance and Efficiency - NVIDIA's 800 V HVDC architecture is expected to improve end-to-end power efficiency by up to 5%, reduce maintenance costs by 70% due to fewer power supply unit (PSU) failures, and lower cooling costs by connecting HVDC directly to IT and compute racks [13]. - Navitas has introduced several high-efficiency power supply units (PSUs), including a 12 kW PSU that meets 98% efficiency, showcasing the company's commitment to innovation in power delivery for AI data centers [12]. Group 4: Company Background - Navitas Semiconductor, founded in 2014, specializes in next-generation power semiconductors, focusing on GaN and SiC technologies for various markets, including AI data centers and electric vehicles [17]. - The company holds over 300 patents and is recognized for its commitment to sustainability, being the first semiconductor company to achieve CarbonNeutral® certification [17].

Core Viewpoint - Huawei's CloudMatrix 384 super node claims to rival Nvidia's NVL72, but there are discrepancies in the hardware descriptions and capabilities between CloudMatrix and the UB-Mesh paper, suggesting they may represent different hardware forms [1][2][8]. Group 1: CloudMatrix vs. UB-Mesh - CloudMatrix is described as a commercial 384 NPU scale-up super node, while UB-Mesh outlines a plan for an 8000 NPU scale-up super node [8]. - The UB-Mesh paper indicates a different architecture for the next generation of NPUs, potentially enhancing capabilities beyond the current 910C model [10][11]. - There are significant differences in the number of NPUs per rack, with CloudMatrix having 32 NPUs per rack compared to UB-Mesh's 64 NPUs per rack [1]. Group 2: Technical Analysis - CloudMatrix's total power consumption is estimated at 500KW, significantly higher than NVL72's 145KW, raising questions about its energy efficiency [2]. - The analysis of optical fiber requirements for CloudMatrix suggests that Huawei's vertical integration may mitigate costs and power consumption concerns associated with fiber optics [3][4]. - The UB-Mesh paper proposes a multi-rack structure using electrical connections within racks and optical connections between racks, which could optimize deployment and reduce complexity [9]. Group 3: Market Implications - The competitive landscape may shift if Huawei successfully develops a robust AI hardware ecosystem, potentially challenging Nvidia's dominance in the market [11]. - The ongoing development of AI infrastructure in China could lead to a new competitive environment, especially with the emergence of products like DeepSeek [11][12]. - The perception of optical modules and their cost-effectiveness may evolve, similar to the trajectory of laser radar technology in the automotive industry [6].