Ac t i o n | 20 Nov 2025 02:46:50 ET │ 19 pages NVIDIA Corp (NVDA.O) Estimate Changes — We revise FY27/FY28 sales by 19%/26% reflecting what we now expect to be a re-accelerated DC sales backed by better-than expected Blackwell and Rubin demand visibility. Though pushing down our FY27E/FY28E non-GAAP GM estimates to 75%/75.3% from prior 76%/76.1% respectively, our FY27/28 EPS estimates go up by 12%/19% to $8.10 and $10.08, respectively. We raise our TP to $270 on consistent 30x P/E times revised CY27 EPS po ...

Core Insights - NVIDIA has introduced the DGX Spark, a compact AI supercomputer designed for personal use, which is significantly smaller and more affordable than traditional data center models [2][5][21] - The DGX Spark is positioned as a solution to the rising costs of cloud computing for AI applications, allowing users to run models locally without incurring high cloud fees [22][23] Product Specifications - The DGX Spark features NVIDIA's Blackwell architecture, 128GB unified system memory, and delivers 1 PFLOP of AI performance, while consuming only 240 W of power [1] - In contrast, the previous DGX-1 model utilized the Pascal architecture, had 128GB of GPU memory, and required 3,200 W of power, highlighting the advancements in efficiency and performance [1] Market Context - The introduction of DGX Spark reflects a shift in the AI landscape from cloud-based solutions to local computing, driven by increasing cloud costs and the need for real-time processing capabilities [22][24] - Companies are increasingly looking to establish local GPU nodes to reduce costs and enhance compliance, marking a return to desktop computing as a viable option for AI workloads [24][26] Testing and Performance - Initial tests by LMSYS indicate that DGX Spark performs well with mid-sized models (8B-20B parameters), outperforming similarly priced standalone GPU platforms [10][21] - The device can operate as a local AI node, providing API services similar to cloud-based solutions, thus enabling a complete local AI development environment [11][16][21] Industry Implications - The launch of DGX Spark signifies a potential revolution in how AI capabilities are deployed, allowing developers to maintain control over their computing resources and model deployment [22][26] - As AI applications evolve to require real-time interaction, the need for local processing power is becoming increasingly critical, positioning products like DGX Spark favorably in the market [25][26]

Core Viewpoint - The article discusses the emerging trend of bringing AI capabilities from the cloud back to personal desktops, exemplified by NVIDIA's launch of the DGX Spark personal AI supercomputer, which is designed to provide powerful AI processing capabilities in a compact form factor [2][34]. Group 1: Product Overview - NVIDIA's DGX Spark is now available for purchase starting at $3,999, representing a significant reduction in price and size compared to previous models like the DGX-1, which was priced at $129,000 [3][4]. - The DGX Spark features a new GPU architecture (NVIDIA Blackwell) and offers 1 PFLOP (FP4) AI performance, while consuming only 240 W of power and weighing 1.2 kg [4][33]. - The device is designed to function as a personal AI supercomputer, allowing developers to run AI models locally without relying on cloud infrastructure [4][33]. Group 2: Performance and Testing - Initial tests by LMSYS indicate that DGX Spark performs well with mid-sized models (8B-20B), outperforming similarly priced standalone GPU platforms, especially in batch processing scenarios [13][32]. - For larger models (70B+), DGX Spark is capable of running them but is deemed suitable for testing rather than production use [14]. - The testing process demonstrated that DGX Spark can operate as a local AI node, providing API services similar to cloud-based solutions, thus enabling a complete local AI development environment [18][22][29]. Group 3: Market Context and Trends - The article highlights a shift in the AI landscape from cloud reliance to local processing, driven by rising costs associated with cloud computing, particularly in inference tasks [36][37]. - Companies are increasingly moving AI inference to local devices to reduce costs and improve performance, as evidenced by significant reductions in monthly infrastructure expenses for some organizations [38][39]. - The trend reflects a broader movement towards "near computing," where local devices handle real-time AI tasks, while cloud services focus on training and data aggregation [43].

Core Insights - Nvidia has launched the world's smallest AI supercomputer, the Nvidia DGX Spark, which is designed to provide enterprise-level supercomputing performance in a compact desktop form factor, potentially driving significant new revenue growth for the company [1][2] - The introduction of DGX Spark indicates that the AI computing industry, led by Nvidia, TSMC, Broadcom, and Micron, is still in a "super bull market," making it a favored investment sector for global capital [1][10] - Nvidia's stock has surged by 40% this year, currently trading around $188, with a market capitalization of approximately $4.6 trillion, maintaining its position as the highest-valued company globally [1][10] Product Overview - The Nvidia DGX Spark supercomputer features the latest GB10 Grace Blackwell superchip, ConnectX-7 high-performance networking capabilities, and Nvidia's proprietary AI software stack, priced at $3,999 [2][6] - It is aimed at small and medium-sized enterprises and AI developers, allowing them to access AI supercomputing capabilities without the need for expensive cloud services or dedicated AI server racks [2][6] - The DGX Spark can support up to 128GB of memory, enabling the execution of large-scale AI models, and can be interconnected with another unit to handle models with up to 405 billion parameters [6][7] Historical Context - The DGX Spark is reminiscent of the earlier DGX-1, which was pivotal in the development of AI supercomputing, with the first unit delivered to Elon Musk, co-founder of OpenAI [3][4] - Nvidia's CEO Jensen Huang emphasized the importance of making AI supercomputing accessible to developers, similar to the impact of the DGX-1 on AI research [4][9] Market Position and Future Outlook - Nvidia is expected to continue its leadership in the AI computing race, with the DGX Spark serving as a new growth driver and amplifier for its AI ecosystem [9][10] - The company has secured significant deals, including a $100 billion investment in OpenAI and a $6.3 billion order with CoreWeave for AI computing power [9][10] - Analysts predict that Nvidia's stock price could reach $300, reflecting confidence in its ability to capitalize on the ongoing AI infrastructure investment wave, which is projected to reach $2 trillion to $3 trillion [10][11]

Summary of Conference Call Notes Company and Industry Involved - The conference call primarily discusses developments in the **AI hardware sector**, particularly focusing on **NVIDIA** and its product offerings related to AI computing and data centers. Core Points and Arguments 1. **Blackwell Series Products**: The HGX series 8-card servers have been in production since last year, with deliveries starting in February. The GB200 cabinet is fully produced, and an upgrade to GB300 is expected in Q3 of this year [1][2] 2. **AI Factory Core Computing Unit**: The GB300 is positioned as a core computing unit for AI factories, supporting large-scale inference and training tasks. There have been significant upgrades compared to the GB200, although detailed specifics were not reiterated in this call [2] 3. **Production Challenges**: Q1 production rates were lower than expected due to assembly issues at ODM factories, leading to a downward revision of the annual cabinet shipment forecast [2][3] 4. **NVLink Fusion Technology**: This new technology allows customers to purchase only an NVLink Switch chip or NVLink Fusion IP, simplifying the procurement process for ASIC chips [3] 5. **DGX Spark and DGX Station**: The DGX Spark is aimed at personal supercomputer users, featuring NVIDIA's GB10 chip and supporting local model training. The DGX Station is a desktop-level AI supercomputer capable of running large models efficiently [4] 6. **AI Supercomputer in Taiwan**: NVIDIA plans to collaborate with TSMC and Foxconn to establish the first AI supercomputer in Taiwan, which is expected to be a cornerstone of the local AI ecosystem [5] 7. **RTX Pro Servers**: The RTX Pro servers, announced by ASUS, are designed to accelerate the transition of IT data centers to AI factories, boasting performance improvements over previous flagship systems [6] 8. **Software Ecosystem Expansion**: NVIDIA is also expanding its software ecosystem, launching various professional acceleration libraries aimed at standardizing AI acceleration capabilities across industries [7] 9. **Taiwan's Semiconductor Role**: Taiwan's advanced semiconductor manufacturing capabilities are crucial for NVIDIA's hardware deployment, fostering a deep collaboration in design, manufacturing, and application [8] 10. **Market Outlook**: The overseas computing power sector is gradually recovering, with companies in this space expected to release strong earnings this year. The computing PC sector is noted to be at a relatively low valuation [8] Other Important but Overlooked Content - The conference highlighted the ambition of NVIDIA to standardize and modularize AI acceleration capabilities across various industries, indicating a strategic direction towards broader applications of AI technology [7] - The establishment of a new NVIDIA office in Taiwan, named NVIDIA Constellation, signifies a commitment to local research and development, particularly in AI and semiconductor design [7][8]

Investment Rating - The industry investment rating is "Recommended," indicating an expected increase in the industry index by more than 5% over the next 3-6 months compared to the benchmark index [37]. Core Insights - NVIDIA reported FY26Q1 revenue of $44.1 billion, a year-over-year increase of 69% and a quarter-over-quarter increase of 12%, significantly exceeding market expectations of $43.3 billion and company guidance of $43.0±2 billion. This growth was primarily driven by the data center business, which generated $39.1 billion in revenue, up 73% year-over-year and 10% quarter-over-quarter [3][7]. - The Blackwell architecture contributed approximately 70% of the data center computing revenue, marking the fastest ramp-up in GPU production in the company's history [4]. - The company expects FY26Q2 revenue to be $45.0 billion, with a potential loss of $8.0 billion in revenue due to recent export control restrictions affecting the H20 product line [5][8]. Summary by Sections 1. Performance Overview - FY26Q1 revenue reached $44.1 billion, with data center revenue at $39.1 billion, reflecting a 73% year-over-year growth. The GAAP and non-GAAP gross margins were 60.5% and 61.0%, respectively. Excluding a $4.5 billion expense, the non-GAAP gross margin would have been 71.3% [3][7]. - The diluted earnings per share were $0.76 (GAAP) and $0.81 (non-GAAP), with a potential adjusted non-GAAP EPS of $0.96 when excluding the aforementioned expense [3][7]. 2. Business Segment Performance - **Data Center**: Revenue reached a record high of $39.1 billion, with computing revenue at $34.2 billion (up 76% YoY) and networking revenue at $4.957 billion (up 56% YoY) [4]. - **Gaming**: Revenue was $3.763 billion, showing a 42% year-over-year increase, driven by strong adoption of Blackwell architecture GPUs [4]. - **Professional Visualization**: Revenue was $509 million, with a 19% year-over-year increase, although it remained flat quarter-over-quarter due to tariff-related uncertainties [4]. - **Automotive and Robotics**: Revenue was $567 million, reflecting a 72% year-over-year increase, driven by strong demand for autonomous driving and electric vehicles [4]. 3. Future Guidance - The company anticipates FY26Q2 revenue of $45.0 billion, accounting for an estimated $8.0 billion loss in H20 revenue due to export restrictions. Expected gross margins are projected at 71.8% (GAAP) and 72.0% (non-GAAP) [5][8].

Core Viewpoint - NVIDIA's CEO Jensen Huang expressed concern over the H20 export restrictions impacting the company's access to the Chinese AI market, which is valued at $50 billion, while highlighting the robust demand for AI processing capabilities driven by the Blackwell chip production [1][8][45]. Group 1: Financial Performance and Market Impact - NVIDIA's Q1 revenue reached $44 billion, a 69% year-over-year increase, despite the challenges posed by export restrictions [25]. - The company anticipates a loss of $8 billion in H20 revenue due to new export limitations, significantly affecting future business prospects in the Chinese market [8][43]. - The data center revenue grew by 73% year-over-year, driven by the rapid ramp-up of the Blackwell product line [5][27]. Group 2: AI Demand and Technological Advancements - There is an explosive growth in AI inference demand, with token generation increasing by 500% year-over-year, particularly in complex AI workloads [12][29]. - The Blackwell architecture is designed to support this demand, offering a throughput that is 40 times higher than the previous Hopper architecture [12][10]. - The average deployment rate for major hyperscale customers is nearly 1,000 NVL72 racks per week, indicating strong market adoption [10][28]. Group 3: Strategic Insights on AI Market - Huang emphasized that winning the Chinese AI market is crucial for global leadership, as it houses half of the world's AI researchers [3][45]. - The company is exploring options to create attractive solutions for the Chinese market in light of the export restrictions [8][46]. - The rise of open-source AI models like DeepSeek and Qwen is seen as a strategic advantage for the U.S. in maintaining its leadership in AI technology [13][46]. Group 4: Future Outlook and Growth Engines - NVIDIA is optimistic about future growth, citing multiple key growth engines including surging inference demand, sovereign AI initiatives, and enterprise AI [19][49]. - The company plans to achieve $45 billion in revenue for Q2, with expected gross margins of 71.8% [20][43]. - The establishment of AI factories globally is seen as a foundational step in building the necessary infrastructure for AI deployment across industries [15][62].

Financial Performance - Nvidia reported Q1 FY2026 revenue of $44.062 billion, a 69% year-over-year increase [1] - Net profit reached $18.775 billion, up 26% year-over-year [1] - Earnings per share (EPS) was $0.76, reflecting a 27% increase year-over-year [1] - Data center business, driven by AI chips and related products, saw a 73% revenue increase, accounting for 88% of total revenue [1] Impact of Export Controls - U.S. export controls on chips significantly impacted Nvidia's performance, leading to a $4.5 billion inventory write-down and an estimated loss of $2.5 billion in potential sales [1] - An additional loss of approximately $8 billion is expected in Q2 due to these restrictions [1] - The Chinese market, valued at around $50 billion, is now largely inaccessible to U.S. companies due to the H20 chip export ban [3][12] AI Market Dynamics - China is a major player in the global AI market, with half of the world's AI researchers located there [3] - The inability to deploy Hopper architecture products in China limits Nvidia's market share and growth potential in this region [3][12] - The competition in AI is not just about chips but also about who leads the entire technology stack [4] Domestic Manufacturing Initiatives - Nvidia supports the vision of bringing advanced manufacturing back to the U.S., with significant investments in local chip production facilities [7] - Partnerships with companies like TSMC and Foxconn are underway to establish AI supercomputer manufacturing plants in the U.S. [7] AI Infrastructure and Development - The company emphasizes the importance of AI as a foundational infrastructure that will transform various industries [12][14] - New enterprise AI products are being launched to support local developers and businesses, indicating a shift towards internal AI deployment [14] - The future of AI infrastructure is expected to include AI factories within manufacturing plants, enhancing operational efficiency [14]

专题：英伟达财报：英伟达Q1业绩整体超预期 Q2料H20收入减少80亿 北京时间5月29日凌晨，英伟达公布2026财年第一财季财报：营收为440.62亿美元，同比增长69%；调 整后净利润为198.94亿美元，同比增长31%（注：英伟达财年与自然年不同步，2025年1月底至2026年1 月底为2026财年）。 详见： 英伟达第一财季营收440.62亿美元 同比增长69% 财报发布后，英伟达创始人、总裁兼首席执行官黄仁勋和执行副总裁兼首席财务官科莱特·克雷斯等高 管出席随后召开的财报电话会议，解读财报要点并回答分析师提问。 以下是分析是问答环节主要内容： 摩根大通分析师Joseph Moore：我们注意到，在过去至少一年时间里，管理层都讨论逻辑推导模型在 推理方面的需求扩展。也正如你们所谈到的，已经看到这些努力所取得的一些成果，从你们的客户那里 也听到了相关反馈。能否展开谈一下，公司目前能够多大程度上满足这些需求？推理业务对英伟达而 言，目前规模有多大？另外，未来的逻辑推导和推理是否需要完全基于NBL72机架规模的解决方案？ 黄仁勋：事实上，思考这个问题的最佳方式可能是，人工智能包含多个方面。当然，我们知道人工 ...

Financial Data and Key Metrics Changes - NVIDIA reported revenue of $44 billion, a 69% year-over-year increase, exceeding expectations despite a challenging operating environment [6] - Data center revenue reached $39 billion, growing 73% year-on-year [6] - GAAP gross margins were 60.561%, while non-GAAP gross margins would have been 71.3% excluding a $4.5 billion charge related to inventory write-downs [31][33] Business Line Data and Key Metrics Changes - Data center revenue was significantly impacted by new export controls, with $4.6 billion recognized prior to the controls and a $4.5 billion charge for inventory write-downs [7][31] - Gaming revenue reached a record $3.8 billion, increasing 48% sequentially and 42% year-on-year, driven by strong adoption of Blackwell architecture [22][23] - Pro Visualization revenue was flat sequentially at $5.9 billion but up 19% year-on-year [26] - Automotive revenue was $567 million, down 1% sequentially but up 72% year-on-year, driven by self-driving technology and demand for new energy vehicles [28] Market Data and Key Metrics Changes - China data center revenue was slightly below expectations due to export licensing controls, with a meaningful decrease anticipated in Q2 [21][22] - Singapore represented nearly 20% of Q1 build revenue, primarily for US-based customers [22] Company Strategy and Development Direction - NVIDIA is focusing on AI infrastructure, with plans to build AI factories globally, emphasizing the importance of AI in various industries [14][62] - The company is committed to a multi-year product roadmap extending through 2028, with a focus on enhancing AI capabilities and infrastructure [11][12] - NVIDIA is exploring limited options to supply data center products compliant with new export control rules, acknowledging the competitive landscape in AI [9][38] Management's Comments on Operating Environment and Future Outlook - Management expressed concerns about losing access to the China AI accelerator market, which could have a material adverse impact on business [9] - The company anticipates continued ramp-up of Blackwell products, partially offset by declines in China revenue, with total revenue expected to be around $45 billion in Q2 [32][33] - Management highlighted the exponential growth in reasoning AI and its implications for future demand and infrastructure needs [62][84] Other Important Information - NVIDIA returned a record $14.3 billion to shareholders through share repurchases and dividends [32] - The company is actively investing in onshore manufacturing and partnerships to strengthen its supply chain [45][46] Q&A Session Summary Question: How much of the inference demand is NVIDIA able to serve? - Jensen Huang stated that NVIDIA aims to serve all inference demand and is on track to do so, highlighting the capabilities of the Grace Blackwell NVLink 72 for reasoning AI [53][54] Question: What is the impact of China on revenue guidance? - Colette Kress clarified that the company recognized $4.6 billion in H20 revenue in Q1 and expects a significant decline in China data center revenue in Q2 due to export controls [60][61] Question: What are the drivers of growth for the AI infrastructure? - Jensen Huang identified four positive surprises driving growth: increased demand for reasoning AI, the rescinding of the AI diffusion rule, the rise of enterprise AI, and the emergence of industrial AI [82][84] Question: Are there more large GPU cluster investments expected? - Jensen Huang confirmed that there are more orders than previously discussed and that many AI factories are being planned globally, indicating a strong future demand for AI infrastructure [70][72] Question: What is the outlook for the networking business? - Jensen Huang highlighted the success of Spectrum X and its adoption among major CSPs, emphasizing the importance of low latency and high performance in AI networking solutions [100]