on 09-Jan-2026 U.S. Semiconductors Broadcom Inc Rating Outperform Price Target AVGO 475.00 USD 9 January 2026 Stacy A. Rasgon, Ph.D. +1 213 559 5917 stacy.rasgon@bernsteinsg.com Alrick Shaw +1 917 344 8454 alrick.shaw@bernsteinsg.com Arpad von Nemes +1 917 344 8461 arpad.vonnemes@bernsteinsg.com Broadcom (AVGO): Vegas baby...Takeaways from a CES investor meeting with the Semiconductor Solutions Group President On Thursday at the Consumer Electronics Show in Las Vegas we hosted a small investor meeting with ...

产品聚焦AI推理场景，用于自身Gemini模型 智通财经APP获悉，中国银河证券发布研报称，未来AI芯片的市场竞争将更加激烈，谷歌(GOOGL.US) 有望凭借TPU v7系列产品提升自身AI芯片市占率。该行认为，随着明年谷歌TPU v7的上市，国内液冷/ 电源/PCB领域有望带来新的发展机遇，同时随着AI芯片竞争格局不断深化，国产算力芯片在国产替代 趋势长期上行。 中国银河证券主要观点如下： 谷歌即将上市TPU v7，技术指标比肩英伟达B200 谷歌即将正式上市第七代TPU芯片"Ironwood"，标志着AI算力技术的重大突破。该芯片单芯片峰值算力 达到4614 TFLOPs(FP8精度)，配备192GB HBM3e内存，内存带宽高达7.4TB/s，功耗约1000W。与前代 产品相比，Ironwood的算力提升了4.7倍，能效比达到每瓦29.3 TFLOPs，是前代产品的两倍。服务器散 热方面，采用100%液冷架构，采用大冷板设计，覆盖4 颗TPU及VRM;集群规模上最大支持144 个机架 互联，即9216 个TPU芯片集群。整体技术指标比肩英伟达B200芯片。 风险提示 下游需求不及预期的风险，同业竞争格 ...

公司管理层在电话会上提到，未来18个月内可交付的AI积压订单规模约为730亿美元，这一表述在一定 程度上暗示2027年上半年收入可能面临回落风险。不过，Moore认为，公司在此期间仍有望获得新订单 支撑。但在未来18个月AI收入已超过900亿美元的背景下，2027年上半年环比增长空间可能相对有限， 公司对2027年增长前景的表态也较上一次财报电话会议略显保守。 杰富瑞分析师Blayne Curtis同样在财报后上调了博通的目标价，并认为公司的AI故事仍在持续扩展。他 指出，Anthropic在2026财年第四季度又追加了约110亿美元订单，外部TPU相关业务继续推进。此外， 公司在本季度签下第五位未具名客户，启动了一项多年期的定制XPU项目，潜在客户可能并非 OpenAI，也可能与苹果有关。博通披露的未来六个季度AI订单积压规模约为730亿美元。 Curtis同时提到，部分投资者担心上述数据未能充分体现2026财年的上行空间，但他认为，下半年仍有 望继续获得新订单。他指出，博通在沟通策略上也出现转变：此前在短期表现强劲时更强调远期展望， 而如今随着AI业务加速放量，管理层的指引更聚焦于即将到来的季度。 富国银 ...

Core Insights - The article presents a weekly roundup of the top 50 keywords in the AI sector, highlighting significant developments and trends in the industry [2]. Group 1: Computing Power - TPU v7 is a key focus from Google, indicating advancements in their tensor processing units [3]. - Huawei's Flex.ai container technology is noted for its potential impact on computing capabilities [3]. Group 2: Models - DeepSeek's DeepSeek-Math-V2 and Anthropic's Claude Opus 4.5 are among the notable AI models introduced [3]. - Other significant models include Tencent's HunyuanOCR and OpenAI's Shallotpeat, showcasing a diverse range of applications [3]. Group 3: Applications - Anthropic's dual-agent architecture and OpenAI's integration of voice modes are highlighted as innovative applications in AI [3]. - Tencent's 3D creation engine and Alibaba's Z-Image are also mentioned, reflecting the growing application of AI in creative fields [3]. Group 4: Technology and Perspectives - Google is advancing with technologies like Quick Share and basketball robots developed by Hong Kong University of Science and Technology [4]. - Perspectives from institutions like Tsinghua University and Ilya Sutskever emphasize the role of AI in education and research acceleration [4]. Group 5: Events - The Genesis Project in the U.S. and discussions around job displacement due to AI are significant events shaping the current landscape [4].

Core Insights - Google is launching an aggressive TPU@Premises initiative to sell its computing power directly to major companies like Meta, aiming to capture 10% of Nvidia's revenue [1][14] - The TPU v7 has achieved performance parity with Nvidia's flagship B200, indicating a significant advancement in Google's hardware capabilities [1][6] Summary by Sections Google's Strategy - Google is shifting from being a "cloud landlord" to a "arms dealer" by allowing customers to deploy TPU chips in their own data centers, breaking Nvidia's monopoly in the high-end AI chip market [2][3] Meta's Involvement - Meta is reportedly in talks with Google to invest billions of dollars to integrate Google's TPU chips into its data centers by 2027, which could reshape the industry landscape [3][5] Technological Advancements - The latest Google model, Gemini 3, trained entirely on TPU clusters, is closing the gap with OpenAI, challenging the long-held belief that only Nvidia's GPUs can handle cutting-edge model training [5][10] - The Ironwood TPU v7 and Nvidia's B200 are nearly equal in key performance metrics, with TPU v7 slightly leading in FP8 computing power at approximately 4.6 PFLOPS compared to B200's 4.5 PFLOPS [7][10] Competitive Landscape - Google's TPU v7 features a high inter-chip connectivity bandwidth of 9.6 Tb/s, enhancing scalability for large model training, which is a critical advantage for clients like Meta [8][10] - Google is leveraging the PyTorch framework to lower the barrier for developers transitioning from Nvidia's CUDA ecosystem, aiming to capture market share from Nvidia [11][13] Nvidia's Response - Nvidia is aware of the competitive threat posed by Google's TPU v7 and has been making significant investments in startups like OpenAI and Anthropic to secure long-term commitments to its GPUs [14][16] - Nvidia's CEO has acknowledged Google's advancements, indicating a recognition of the competitive landscape shifting [14]

Core Viewpoint - The demand for next-generation chips from AI giants like Nvidia is pushing TSMC's N3 advanced process capacity to its limits, leading to a significant supply shortage that is expected to enhance TSMC's profit margins, potentially pushing gross margins above 60% by 2026 [1][3][9] Group 1: Capacity Constraints - TSMC's N3 advanced process capacity is nearing its maximum, with Morgan Stanley predicting a significant capacity shortfall even with efforts to optimize existing lines [1][3] - Nvidia's CEO Jensen Huang has personally requested increased chip supply from TSMC, highlighting the urgency of the situation [3] - Despite Nvidia's request to expand N3 capacity to 160,000 wafers per month, TSMC's actual capacity may only reach 140,000 to 145,000 wafers per month by the end of 2026, indicating a persistent supply-demand imbalance [3][4] Group 2: Production Strategies - TSMC is not planning to build new N3 fabs but will prioritize existing facilities for next-generation processes, with capacity increases mainly coming from line conversions at the Tainan Fab 18 [4][6] - The conversion of N4 lines to N3 may face challenges if Nvidia is allowed to ship GPUs to the Chinese market, potentially slowing down the conversion process [5] - TSMC is also utilizing cross-factory collaboration to maximize output, leveraging idle capacity from its Fab 14 to handle some backend processes for N3 [6] Group 3: Customer Demand - Major tech companies are scrambling to secure production capacity, with a diverse lineup of clients including Nvidia, Broadcom, Amazon, Meta, Apple, Qualcomm, and MediaTek [7] - The demand from cryptocurrency miners is expected to remain largely unmet in 2026 due to the pre-booking of capacity by major clients [7] Group 4: Profitability Outlook - The scarcity of capacity is translating directly into TSMC's profitability, with clients willing to pay premiums of 50% to 100% for expedited orders [8][9] - Morgan Stanley predicts that if the trend of urgent orders continues, TSMC's gross margin could reach the low to mid-60% range in the first half of 2026, exceeding current market expectations [9]

Core Viewpoint - Nvidia's CEO Jensen Huang is personally requesting increased chip supply from TSMC, indicating a critical demand for the next generation of AI chips, particularly the Rubin series, amidst a supply shortage in advanced chip manufacturing [1][2]. Group 1: Supply and Demand Dynamics - TSMC's current capacity for N3 chips is projected to reach only 140,000 to 145,000 wafers per month by the end of 2026, despite Nvidia's request for an expansion to 160,000 wafers per month [1][2]. - The supply-demand imbalance suggests that companies relying on advanced processes may face growth bottlenecks, while TSMC, having pricing power, is likely to see a significant increase in profit margins [1][6]. Group 2: Production Strategies - TSMC is not planning to build new N3 fabs but will prioritize existing facilities for next-generation nodes like N2 and A16, focusing on encouraging clients to migrate to leading nodes [2][4]. - The main increase in N3 capacity will come from converting production lines at the Tainan Fab 18, with an expected reduction in N4 utilization rates [2][4]. Group 3: Customer Demand - The demand for N3 process chips is expected to be extremely tight, with major tech companies like Nvidia, Broadcom, Amazon, Meta, and Microsoft all vying for capacity [5][6]. - Due to pre-booked capacity by primary clients, demand from cryptocurrency miners is likely to remain unmet in 2026 [5]. Group 4: Financial Implications for TSMC - The scarcity of capacity is translating into improved profitability for TSMC, with clients executing "hot-runs" and "super hot-runs" at prices 50% to 100% higher for expedited delivery [6]. - TSMC's gross margin is projected to reach the low to mid-60% range in the first half of 2026, exceeding current market expectations, supported by a planned price increase of 6% to 10% for advanced processes starting in Q1 2026 [6].

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The report focuses on the commercialization progress of AI applications, highlighting significant advancements in various sectors, including large models, AI video, AI programming, and enterprise-level AI software [4][28] - The report emphasizes the rapid growth in token consumption for AI applications, indicating accelerated commercialization and the emergence of new revenue streams [4][15] - Key companies in the AI space are experiencing substantial valuation increases, with several achieving over $1 billion in annual recurring revenue (ARR) [16][21] Summary by Sections 1. AI Application Overview: Acceleration of Commercialization - AI applications are witnessing a significant increase in token consumption, reflecting faster commercialization progress [4] - Major models like OpenAI have achieved an ARR of $12 billion, while AI video tools are approaching the $100 million ARR milestone [4][15] 2. Internet Giants: Recommendation System Upgrades + Chatbot - Companies like Google, OpenAI, and Meta are enhancing their recommendation systems and developing independent AI applications [4][26] - The integration of AI chatbots into traditional applications is becoming a core area for computational consumption [14] 3. AI Programming: One of the Hottest Application Directions - AI programming tools are gaining traction, with companies like Anysphere achieving an ARR of $500 million [17] - The commercialization of AI programming is accelerating, with several startups reaching significant revenue milestones [17][18] 4. Enterprise-Level AI: Still Awaiting Large-Scale Implementation - The report notes that while enterprise AI has a large potential market, its commercialization has been slower compared to other sectors [4][25] - Companies are expected to see significant acceleration in AI implementation by 2026 [17] 5. AI Creative Tools: Initial Commercialization of AI Video - AI video tools are beginning to show revenue potential, with companies like Synthesia reaching an ARR of $100 million [15][21] - The report highlights the impact of AI on content creation in education and gaming [4][28] 6. Domestic AI Application Progress - By mid-2025, China's public cloud service market for large models is projected to reach 537 trillion tokens, indicating robust growth in AI applications domestically [4] 7. Key Company Valuation Table - The report provides a detailed valuation table for key companies in the AI sector, showcasing significant increases in their market valuations and ARR figures [16][22]

Core Insights - The article discusses the advancements in AI GPU and ASIC technologies, highlighting the performance improvements and cost differences associated with training large models like Llama-3 [1][5][10]. Group 1: Chip Development and Performance - NVIDIA is leading the development of AI GPUs with multiple upcoming models, including the H100, B200, and GB200, which show increasing memory capacity and performance [2]. - AMD and Intel are also developing competitive AI GPUs and ASICs, with notable models like MI300X and Gaudi 3, respectively [2]. - The performance of AI chips is improving, with higher configurations and better power efficiency being observed across different generations [2][7]. Group 2: Cost Analysis of Training Models - The total cost for training the Llama-3 400B model varies significantly between GPU and ASIC, with GPUs being the most expensive option [5][7]. - The hardware cost for training with NVIDIA GPUs is notably high, while ASICs like TPU v7 have lower costs due to advancements in technology and reduced power consumption [7][10]. - The article provides a detailed breakdown of costs, including hardware investment, power consumption, and total cost of ownership (TCO) for different chip types [12]. Group 3: Power Consumption and Efficiency - AI ASICs demonstrate a significant advantage in inference costs, being approximately ten times cheaper than high-end GPUs like the GB200 [10][11]. - The power consumption metrics indicate that while GPUs have high thermal design power (TDP), ASICs are more efficient, leading to lower operational costs [12]. - The performance per watt for various chips shows that ASICs generally outperform GPUs in terms of energy efficiency [12]. Group 4: Market Trends and Future Outlook - The article notes the increasing availability of new models like B300 in the market, indicating a growing demand for advanced AI chips [13]. - Continuous updates on industry information and investment data are being shared in dedicated platforms, reflecting the dynamic nature of the AI chip market [15].

Summary of Conference Call Notes Industry Overview - The conference call focuses on the ASIC (Application-Specific Integrated Circuit) market dynamics, particularly involving major players like AWS, Google, Microsoft, and META, with projections extending into 2026 and beyond [1][2][5]. Key Company Insights AWS - AWS has resolved issues with Trainium 3 and continues to secure orders from downstream suppliers. The development of Trainium 4 has commenced, with expectations for a contract signing soon [2][5]. - The specifications for AWS's TPU chips are significantly higher than competitors, with TPU v6p and TPU v7p expected to have ASPs of US$8,000 and higher, respectively [2]. Google - Google is progressing steadily with its TPU series, with TPU v6p featuring advanced specifications including multiple compute and I/O dies. The company is anticipated to become a top customer for GUC due to its rapid ramp-up in CPU development [2][10]. - The revenue from Google's 3nm server CPU is expected to contribute to GUC's revenue sooner than previously anticipated, moving from Q4 2025 to Q3 2025 [10]. Microsoft - Microsoft is working on its Maia v2 ASIC, with a target of ramping 500,000 chips in 2026. However, the project has faced delays, pushing the tape-out timeline from Q1 2025 to Q2 2025 [3][4]. - The allocation of chips has shifted, with expectations of 40-60k chips for MSFT/GUC and 400k chips for Marvell in 2026 [3]. META - META is transitioning from MTIA v2 to MTIA v3, with expectations of ramping 100-200k chips for MTIA v2 and 200-300k chips for MTIA v3 in 2026 [2]. Non-CSPs - Companies like Apple, OpenAI, and xAI are entering the ASIC server market, with many expected to tape out in 2H25 and ramp in 2H26. These companies are likely to collaborate with Broadcom for high-end ASIC specifications [7][8][9]. Financial Projections - GUC's FY25 revenue is expected to exceed previous forecasts, driven by contributions from Google and crypto projects. However, concerns remain about FY26 growth without crypto revenue, with a projected 50% YoY growth in MP revenue [10][11]. - The revenue contribution from various ASIC projects in 2026 includes significant figures such as US$16,756 million from TPU v6p and US$2,616 million from Trainium 3 [18]. Additional Insights - The competitive landscape for ASIC design services is intensifying, with Broadcom and MediaTek entering the fray alongside existing players like Marvell and GUC [4][15]. - The potential impact of geopolitical factors on HBM2E clients was discussed, highlighting the resilience of Faraday in the face of possible restrictions [14]. Conclusion - The ASIC market is poised for significant growth, driven by advancements in technology and increasing demand from both CSPs and non-CSPs. Key players are adapting their strategies to navigate challenges and capitalize on emerging opportunities in the sector [1][5][7].