Core Viewpoint - Wells Fargo maintains an Underweight rating on Qualcomm (QCOM) amid its expansion into AI technologies, highlighting competitive pressures in the semiconductor market [4]. Group 1: Company Developments - Qualcomm has officially launched its AI200 and AI250 accelerator cards and racks, specifically designed for AI inference, indicating a strategic move into datacenter solutions [2]. - The company has secured a deal with Saudi Arabia's Humain to deploy 200MW of Qualcomm AI systems starting in 2026, which could generate approximately $2 billion in revenue [3]. Group 2: Competitive Landscape - Competition in the AI semiconductor space is intensifying, with major players like Intel, AMD, and Nvidia targeting similar markets with their respective products [3]. - Wells Fargo analyst Aaron Rakers noted that Qualcomm's focus on rack-scale systems was unexpected, suggesting a shift in its strategic direction [2]. Group 3: Financial Outlook - Despite competitive challenges, Qualcomm is viewed as an attractive option for income investors due to its consistent dividend payouts, having raised dividends for 21 consecutive years [4]. - Wells Fargo has set a price target of $140 for Qualcomm, reflecting a cautious outlook on the stock's performance [4].

Group 1: Nvidia's New Product and Market Impact - Nvidia has confirmed the use of M9 materials in its next-generation product Rubin, with the CPX and midplane PCBs utilizing M9 CCL, amid a shortage of Q cloth [1] - The Rubin CPX is designed for ultra-long context AI inference tasks, featuring a decoupled inference architecture and significant hardware changes, including a wireless cable architecture [1] - The market potential for CPX, midplane, and orthogonal backplane is nearly 100 billion [1] - By 2027, Nvidia's AI PCB market is projected to reach $6.96 billion, a 142% increase from 2026 [1] Group 2: PCB Industry Growth Driven by AI - The global PCB market is expected to grow from $62 billion in 2020 to $75 billion by 2024, with a compound annual growth rate of 4.9% [2] - The value of PCBs in AI servers is significantly higher than in traditional servers, leading to a substantial increase in demand for high-performance PCBs [3] - Low dielectric constant and low loss factor materials are deemed most suitable for high-performance applications, with Q cloth outperforming second-generation cloth [3] Group 3: Recommendations for M9 and Related PCB Manufacturers - Strong recommendations have been made for M9 upstream and related PCB manufacturers, with companies like 建滔积层板 reporting a revenue increase of 11% year-on-year [4] - 建滔集团 is expanding its production capabilities for AI-related products, with a projected investment of approximately 800 to 1,000 million RMB for a new production line [4] - The demand for copper-clad laminates and printed circuit boards is expected to rise significantly due to the rapid development of AI technologies [4]

Group 1: Nvidia's New Product and Market Impact - Nvidia is set to launch its new product series, Rubin, in the second half of next year, utilizing M9 materials for its CPX and midplane PCBs due to a shortage of quartz fabric [1] - The Rubin CPX is designed specifically for long-context AI inference tasks, featuring a decoupled inference architecture and significant hardware innovations, which are expected to expand its market size [1] - According to CICC, the AI PCB market for Nvidia is projected to reach $6.96 billion by 2027, representing a 142% increase from 2026 [1] Group 2: PCB Industry Growth Driven by AI - The global PCB market is expected to grow from $62 billion in 2020 to $75 billion by 2024, with a compound annual growth rate (CAGR) of 4.9% [2] - The demand for high-performance PCBs is anticipated to rise significantly due to the higher value of AI server PCBs compared to traditional servers, with low dielectric constant materials being preferred [3] Group 3: Recommendations for M9 and Related PCB Manufacturers - Strong recommendations have been made for M9 upstream and related PCB manufacturers, with companies like Kintor achieving a revenue of HKD 9.588 billion in the first half of the year, a year-on-year increase of 11% [4] - Kintor Group is actively developing high-frequency and high-speed products for AI server GPU motherboards and plans to establish an AI PCB production line in Guangdong with an investment of approximately RMB 800 million to 1 billion [4]

Summary of NVIDIA VR200 NVL144 CPX Rack Design Changes and Beneficiaries Company and Industry - **Company**: NVIDIA (NVDA US) - **Industry**: Semiconductor and PCB (Printed Circuit Board) Design Key Points and Arguments Introduction of VR200 NVL144 CPX Rack - NVIDIA has launched the VR200 NVL144 CPX rack, generating significant interest among investors [1] - The new rack features PCB design changes aimed at optimizing performance and cost [1] Processing Stages of Large Language Models - The processing of large language models is divided into prefill and decode stages, each with distinct load characteristics [2] - The prefill stage is compute-bound, while the decode stage is memory-bound, leading to performance degradation when run concurrently [2] Design Changes in the VR200 NVL144 CPX Rack - The rack maintains the traditional design of 18 compute trays and 9 switch trays but incorporates changes to the Oberon architecture [5] - Each compute tray includes 2 Vera CPUs, 4 Rubin GPUs, 8 Rubin CPX GPUs, and 8 CX9 NICs [5] - The Rubin CPX uses GDDR7 memory instead of HBM, focusing on optimizing prefill scenarios [2][7] PCB Value Increase - The PCB value in the VR200 NVL144 CPX rack has nearly tripled compared to the GB200/300 generation, with an estimated total PCB value of over $3000 per compute tray [19] - The Bianca board in the VR200 has seen a 30% increase in value compared to previous generations [18] Cooling Solutions - The power of Rubin GPGPU has increased from 1800W to 2300–2500W, necessitating the adoption of Micro-Channel Lid (MCL) for improved cooling efficiency [23][24] - MCL is supplied exclusively by Taiwan's Jentech, making it a key beneficiary of the power increase [25] Memory Module Changes - NVIDIA is switching from LPDDR5X memory to SOCAMM, which offers higher flexibility and maintainability [26][29] - This change benefits suppliers of memory module peripheral chips, such as Rambus, as SOCAMM modules require additional components [29] Supply and Demand for HVLP4 Copper Foil - Demand for HVLP4 copper foil is expected to exceed supply, with NVIDIA's VR200 racks and AWS Tranium 3 racks fully adopting HVLP4 [31][33] - By 2027, total demand for HVLP4 is projected to be 15,000 tons, while supply is estimated at 13,000 tons, resulting in a 10% supply shortage [33] MEC as a Hidden Beneficiary - MEC, a Japanese company specializing in surface treatment chemicals, is positioned to benefit from the increased demand for HVLP4 copper foil due to its CZ series products [35][36] - MEC's revenue from the adoption of its products in AI server PCBs could increase significantly, contributing to 50% of its total revenue by 2027 [48] Other Important Points - The PCB design changes include a midplane to replace overpass cables, enhancing connectivity within the compute tray [12][15] - The NVSwitch tray boards have also seen a value increase, with a new design that includes more layers and higher-grade materials [20] - The transition to SOCAMM memory modules is NVIDIA's second attempt, raising concerns about potential warpage issues [29] This summary encapsulates the critical developments and implications of NVIDIA's new VR200 NVL144 CPX rack, highlighting the potential beneficiaries and market dynamics within the semiconductor and PCB industries.

Summary of Key Points from the Conference Call Industry Overview - The focus is on the semiconductor industry, specifically the CoWoS (Chip on Wafer on Substrate) technology utilized by companies like Nvidia and Broadcom [2][3][4]. Core Insights and Arguments 1. **Demand Forecasts for CoWoS**: - Nvidia's CoWoS demand estimates have been raised by 5% for 2025 and 26% for 2026, driven by higher production units of the Blackwell architecture, which is expected to increase by 30% quarter-over-quarter in Q3 2025 [2]. - Total GPU production units for Nvidia at TSMC are projected to be 6.9 million in 2025 and 7.4 million in 2026, up from previous estimates of 6.5 million and 6.7 million respectively [2]. 2. **Rubin Production at TSMC**: - Nvidia's Rubin production at TSMC is on track, with an increase in production units estimated from 1.3 million to 2.3 million in 2026 [3]. - Trial production of Rubin chips is expected to conclude soon, with sample shipments to supply chain partners anticipated this quarter [3]. 3. **New Product Launch - Rubin CPX**: - The introduction of the Rubin CPX SKU, which features a single GPU die and utilizes GDDR7, is expected to drive additional CoWoS demand [4]. - CoWoS demand for Nvidia is forecasted to rise from 444,000 units in 2025 to 678,000 units in 2026 due to the CPX and Rubin ramp-up [4]. 4. **Stock Recommendations**: - TSMC is recommended as a leading foundry for Cloud/Edge AI, benefiting from its advanced packaging capabilities [5]. - ASE is also highlighted as a key beneficiary in the advanced packaging and testing sector [5]. Additional Important Insights 1. **Valuation Comparisons**: - A detailed valuation comparison table shows TSMC with a market cap of $1,225,437 million and a target price of NT$1,570, while ASE has a market cap of $25,042 million with a target price of NT$189 [6]. 2. **Revenue Projections**: - CoWoS/2.5D packaging revenue is projected to grow significantly, reaching $13,423 million by 2026, with Nvidia's revenue expected to hit $7,970 million in the same year [9]. 3. **Market Dynamics**: - The report emphasizes the competitive landscape and the rapid technological changes in the semiconductor industry, which pose both opportunities and risks for companies involved [18][19][20]. 4. **Investment Risks**: - The report outlines various risks associated with tech investing, including volatility in financial results and challenges in valuation due to the dynamic nature of the market [18][19][20]. 5. **Analyst Certifications**: - Analysts involved in the report have certified that their views reflect their personal opinions and were prepared independently [23]. This summary encapsulates the critical insights and projections regarding the semiconductor industry, particularly focusing on Nvidia and Broadcom's CoWoS technology and the implications for TSMC and ASE.

Investment Rating - The report assigns a "Buy" rating to TSMC and ASE Industrial, indicating a positive outlook for these companies in the semiconductor industry [5][32]. Core Insights - The demand outlook for CoWoS (Chip on Wafer on Substrate) is strengthened due to increased production estimates for Nvidia and Broadcom, with Nvidia's CoWoS demand expected to rise significantly in 2026 [2][4]. - Nvidia's Rubin production at TSMC is on track, with an increase in production units estimated from 1.3 million to 2.3 million in 2026, indicating robust growth potential [3]. - The introduction of Nvidia's new Rubin SKU, CPX, is anticipated to drive further CoWoS demand, with projections showing an increase from 444k units in 2025 to 678k units in 2026 [4]. Summary by Sections Demand Forecasts - Nvidia's CoWoS demand estimates for 2025 and 2026 have been raised by 5% and 26% respectively, driven by higher production units and new product launches [2]. - Broadcom's CoWoS demand for AI accelerators in 2026 has also been revised upwards, reflecting stronger demand from major clients like Google and OpenAI [2]. Production Insights - TSMC's CoWoS capacity is projected to increase from 100kwpm to 110kwpm by the end of 2026, supporting the anticipated growth in demand from Nvidia and Broadcom [2]. - The report highlights that Nvidia's total GPU production units at TSMC are expected to reach 6.9 million and 7.4 million in 2025 and 2026 respectively, up from previous estimates [2]. Stock Recommendations - TSMC is favored as a leading Cloud/Edge AI foundry due to its advanced packaging capabilities, while ASE is expected to benefit from the growth in advanced packaging and testing [5]. - The valuation comparison indicates strong growth potential for both TSMC and ASE, with TSMC's market cap at approximately $1,225 billion and ASE's at $25 billion [5].

Summary of Key Points from the Conference Call Industry and Company Overview - **Industry**: AI Supply Chain, specifically focusing on semiconductor manufacturing and AI ASICs - **Key Companies**: TSMC (Taiwan Semiconductor Manufacturing Company), NVIDIA, Meta, Google, AWS, Alchip, MediaTek, GUC (Global Unichip Corp) Core Insights and Arguments 1. **CoWoS Capacity and Demand**: - TSMC's 2026 CoWoS capacity plan is currently under-supplying based on anticipated demand from key customers, particularly NVIDIA [2][10] - NVIDIA's RTX Pro 6000 forecast remains strong, indicating robust inference demand in China [1][2] - TSMC's current offering of 590k CoWoS-L wafers in 2026 is projected to be 20% below NVIDIA's demand [2] 2. **NVIDIA's AI ASIC Developments**: - The Rubin CPX GPU is expected to adopt TSMC's CoWoS-S packaging, with a performance of 30 PFLOPS and 128GB of GDDR7 memory [3][39] - NVIDIA's AI capacity costs are estimated at $50-60 billion per GW, with $35-40 billion allocated to NVIDIA, suggesting potential revenues of $350-400 billion starting in 2H26 [2][11] 3. **AI ASIC Volume Forecast**: - AI ASIC volumes could reach approximately 5.7 million units in 2026 and 8 million units in 2027, with Google and AWS being the primary players [47] - MediaTek's chances of winning the Meta MTIA project are diminishing due to strong competition from Broadcom and Marvell [57][58] 4. **Market Dynamics and Competitor Analysis**: - GUC is expected to benefit from increased demand for Google Axion CPUs, potentially contributing around $250 million to its revenue in 2026 [59] - Alchip is projected to generate approximately $1.8 billion in revenue from Trainium3 in 2026, despite a back-loaded shipment schedule [56] 5. **Capacity Expansion and Future Projections**: - TSMC's clean room space is ready for expansion to 110-120k if demand is confirmed, with a revised CoWoS capacity assumption of 100kwpm by 2026 [2][12] - The overall demand for CoWoS is expected to grow significantly, with NVIDIA's demand forecasted to increase by 61% in 2026 [20] Additional Important Insights - **NVIDIA's Strategic Positioning**: - NVIDIA's Rubin architecture is designed to optimize inference performance, potentially delivering a 30x to 50x return on investment [39] - The demand for NVIDIA's Blackwell GPU, including RTX Pro 6000, is strong among Chinese customers, with expected sales of 1.5 million to 2 million units in 2H25 [14] - **Competitive Landscape**: - SMIC is aggressively expanding its 7nm node capacity to meet domestic GPU and AI ASIC demand in China [14] - The competition in the AI ASIC market is intensifying, with various players vying for market share and technological advancements [57][58] This summary encapsulates the critical insights and projections regarding the AI supply chain, particularly focusing on TSMC and NVIDIA's roles within the semiconductor industry.

Core Insights - The article highlights a significant shift in the AI industry, focusing on the emerging competition in AI inference chips, which is expected to grow rapidly, with the global AI inference market projected to reach $150 billion by 2028, growing at a compound annual growth rate (CAGR) of over 40% [3][4]. Group 1: Huawei's Ascend 950PR - Huawei announced its Ascend 950 series, including the Ascend 950PR and 950DT chips, designed for AI inference, with a focus on cost optimization through the use of low-cost HBM (High Bandwidth Memory) [3][4]. - The Ascend 950PR targets the inference prefill stage and recommendation services, significantly reducing investment costs, as memory costs account for over 40% of total expenses in AI inference [4]. - Huawei plans to double the computing power approximately every year, aiming to meet the growing demand for AI computing power [3]. Group 2: NVIDIA's Rubin CPX - NVIDIA launched the Rubin CPX, a GPU designed for large-scale context processing, marking its transition from a training leader to an inference expert [5][8]. - The Rubin CPX boasts a computing power of 8 Exaflops, with a 7.5 times improvement over its predecessor, and features 100TB of fast memory and 1.7PB/s bandwidth [5][8]. - This chip supports low-precision data formats, enhancing training efficiency and inference throughput, and is expected to solidify NVIDIA's dominance in the AI ecosystem [9]. Group 3: Google's Ironwood TPU - Google introduced the Ironwood TPU, which has seen a geometric increase in inference request volume, with a 50-fold growth in token usage from April 2024 to April 2025 [10][13]. - The Ironwood TPU features a single-chip peak performance of 4.614 Exaflops and a memory bandwidth of 7.4 TB/s, significantly enhancing efficiency and scalability [17][20]. - Google aims to reduce inference latency by up to 96% and increase throughput by 40% through its software stack optimizations [24]. Group 4: Groq's Rise - Groq, an AI startup specializing in inference chips, recently raised $750 million, increasing its valuation from $2.8 billion to $6.9 billion within a year [25][26]. - The company plans to deploy over 108,000 LPU (Language Processing Units) by Q1 2025 to meet demand, highlighting the growing interest in AI inference solutions [26][27]. - Groq's chips utilize a novel "tensor flow" architecture, offering ten times lower latency compared to leading GPU competitors, making them suitable for real-time AI inference [27]. Group 5: Industry Implications - The competition in AI inference chips is intensifying, with a focus not only on raw computing power but also on cost, energy efficiency, software ecosystems, and application scenarios [28]. - As AI transitions from experimental phases to everyday applications, the ability to provide efficient, economical, and flexible inference solutions will be crucial for companies to succeed in the AI era [28].

Core Insights - President Trump announced a plan to make the U.S. a leader in AI and machine learning by removing restrictions on companies developing future technologies [2] - Major chip manufacturers like Nvidia, Intel, and AMD are actively developing new processors to meet increasing AI performance demands, indicating a promising market for AI chips [2] - The AI chip market is expected to grow significantly, but market maturity and consolidation may limit opportunities for new entrants [2] AI Processor Market Growth - Omdia predicts that the AI data center chip market will continue to grow rapidly, with an annual growth rate of over 250% from 2022 to 2024, but slowing to about 67% from 2024 to 2025 [4] - AI infrastructure spending is expected to peak in 2026, driven primarily by AI, before gradually decreasing by 2030 [4] - Precedence Research forecasts the AI chip market will grow from $94.31 billion in 2025 to $931.26 billion by 2034, with a compound annual growth rate of 28% [5] GPU and ASIC Dominance - GPUs remain dominant in the AI chip market due to their parallel processing capabilities, essential for training and inference tasks in data centers [5] - ASICs are expected to drive future growth in the AI chip market due to their efficiency in specific AI functions, particularly in inference-heavy environments [6] Major Developments by Chip Manufacturers - AMD launched the Instinct MI350 series GPUs, offering significant performance improvements and cost-effectiveness for AI solutions [9] - Intel introduced new Xeon 6 series CPUs designed to enhance the performance of GPU-driven AI systems [10] - Nvidia released the Rubin CPX GPU, designed for high-speed processing of large amounts of data, and integrated it into the new Vera Rubin NVL144 CPX platform [11] Cloud and Edge Computing Trends - By 2024, cloud AI processing is expected to dominate the market with a 52% share, driven by investments from major cloud providers [13] - Edge AI processing is rapidly growing due to the demand for low-latency, device-side intelligence in applications like autonomous vehicles and smart city infrastructure [12] Industry Consolidation and Collaboration - Jon Peddle Research predicts that by 2030, the AI processor market will consolidate to about 25 key players, with IoT and edge computing suppliers likely to survive due to their larger market potential [14] - OpenAI and Nvidia have formed a strategic partnership to deploy NVIDIA systems for training next-generation AI models, with Nvidia planning to invest up to $100 billion [14][15] Technical Challenges in AI Processing - The performance demands of AI processors are creating challenges for existing memory configurations, leading to the adoption of new memory designs to meet data and speed requirements [16] - Liquid cooling solutions are being explored to manage the heat generated by high-performance processors, although they add complexity and cost [17]

Core Insights - NVIDIA Corporation is recognized as one of the best stocks to buy, particularly due to its dominance in the AI and data center markets, with significant revenue growth reported [2] - The company achieved second-quarter fiscal 2026 revenue of $46.7 billion, reflecting a 56% year-over-year increase, primarily driven by demand for its Blackwell Data Center products [2] - A strategic partnership with OpenAI involves an investment of up to $100 billion, aimed at supplying advanced data center chips for next-generation AI systems, enhancing NVIDIA's role in the AI ecosystem [3][4] Financial Performance - NVIDIA's second-quarter fiscal 2026 revenue reached $46.7 billion, marking a 56% increase compared to the previous year [2] - The Blackwell Data Center products experienced a 17% sequential growth, indicating strong market demand [2] Strategic Partnerships - The partnership with OpenAI includes a commitment to invest up to $100 billion, which is expected to significantly expand NVIDIA's long-term market potential [3] - NVIDIA also announced a $5 billion investment in Intel stock to co-develop custom AI infrastructure, targeting a $50 billion market opportunity [3] Product Innovations - The introduction of the Rubin CPX GPU is designed for massive-context inference tasks, enabling advanced capabilities such as million-token coding and generative video [4] - This innovation aligns with NVIDIA's vision of transforming data centers into fully integrated "AI factories" [4]