Core Viewpoint - The article emphasizes that discussions about an AI bubble should be set aside as the focus should be on growth, particularly highlighted by Nvidia's strong financial performance in Q3 [2][4]. Financial Performance - Nvidia reported Q3 revenue of $57 billion, a year-over-year increase of 62%, with net profit of $32 billion, up 65% compared to the previous year, surpassing Wall Street expectations [2]. - The data center business was the primary driver of growth, generating a record $51.2 billion in revenue, which is a 25% increase from the previous quarter and a 66% increase year-over-year [2]. Business Segments - The remaining revenue of $5.8 billion came from the gaming segment, which contributed $4.2 billion, followed by professional visualization and automotive sectors [2]. - Nvidia's CFO noted that the data center business is propelled by computing acceleration, powerful AI models, and autonomous applications [2]. Product Demand - The Blackwell Ultra GPU, launched in March, has shown particularly strong performance and has become a key product for the company, with sales described as "off the charts" [3]. - The demand for training and inference computing power is accelerating, indicating a robust expansion of the AI ecosystem across various industries and countries [3]. Geopolitical Challenges - The company faced challenges in the Chinese market due to geopolitical issues, which resulted in disappointing sales figures for the H20 data center GPUs, with 50 million units shipped [4]. - Despite the inability to deliver competitive data center computing products to China, Nvidia remains committed to communication with both the U.S. and Chinese governments [4]. Future Outlook - Nvidia anticipates Q4 revenue to reach $65 billion, which has positively impacted the stock price, increasing by over 4% in after-hours trading [4]. - The CEO expressed confidence in the growth trajectory, dismissing concerns about an AI bubble and highlighting the ongoing expansion of AI applications [4].

Core Insights - Nvidia's revenue and upcoming sales exceeded Wall Street expectations, alleviating investor concerns about massive spending in the AI sector [2] - The company's quarterly revenue surged 62% to $57 billion, driven by increased demand for AI data center chips [2][4] - Nvidia's net profit reached $32 billion, a 65% year-over-year increase, surpassing analyst forecasts [5] Revenue Breakdown - AI data center sales grew 66% to $51.2 billion, significantly exceeding the expected $49.09 billion [2][4] - The gaming segment contributed $4.2 billion, while professional visualization and automotive sectors added $6.8 billion [2] - Nvidia anticipates sales of approximately $65 billion for the upcoming quarter, higher than the analyst estimate of $61.66 billion [4] Product Performance - The growth was primarily driven by initial sales of the GB300 chip, with network business contributing $8.2 billion in data center sales [4] - The Blackwell Ultra GPU, launched in March, has become the company's leading product, showcasing strong demand [4] - Nvidia's CEO highlighted that the sales of the Blackwell system exceeded expectations, with cloud GPUs sold out [5][7] Market Dynamics - Nvidia's performance is seen as a bellwether for the AI boom, influencing market sentiment [5] - Concerns about AI stock valuations have led to fluctuations in the S&P 500 index, but Nvidia's results were highly anticipated [7] - The company is expected to receive additional orders beyond the previously announced $500 billion in AI chip orders [8] Geopolitical Challenges - Nvidia expressed disappointment over regulatory restrictions hindering chip exports to China, emphasizing the need for support from developers in both the US and China [8] - The company remains committed to maintaining communication with both governments to enhance competitiveness [8] Industry Trends - Major tech companies like Meta, Alphabet, and Microsoft are heavily investing in AI, confirming the trend of significant capital allocation across various sectors [9] - Nvidia's chips are critical for AI data centers, and the company has established partnerships with key players in the AI field [9]

Core Viewpoint - Nvidia's third-quarter earnings report indicates strong growth driven by its data center business, with significant revenue and profit increases compared to the previous year [1][2][6]. Financial Performance - Nvidia reported a revenue of $57 billion for the fiscal third quarter, a 62% increase year-over-year [1]. - The company's net income on a GAAP basis was $32 billion, reflecting a 65% year-over-year increase [1]. - Both revenue and profit exceeded Wall Street expectations [1]. Data Center Business - The data center business generated a record revenue of $51.2 billion, up 25% from the previous quarter and 66% from a year ago [2]. - The demand for Nvidia's GPUs is broad, spanning various markets including cloud service providers, sovereign entities, and supercomputing centers, with a total sale of 5 million GPUs [3]. Product Demand - The Blackwell Ultra GPU, launched in March, has become a leading product for the company, with strong ongoing demand for previous versions of the Blackwell architecture [4]. - Sales of Blackwell GPU chips are described as "off the charts," with cloud GPUs reportedly sold out [6]. Future Outlook - Nvidia forecasts a revenue of $65 billion for the fourth quarter, contributing to a more than 4% increase in share price during after-hours trading [6]. - The company emphasizes a continuous growth trajectory, dismissing concerns about a market bubble and highlighting the accelerating demand for AI technologies [7].

So let's go inside out on Nvidia. Joining us now is Dave Altavilla, principal analyst at Hot Tech Vision and Analysis. Very good morning to you.Thanks so much for joining us on this very important day for Nvidia. What are your expectations here. >> Ah good morning and thanks for having me.Uh you know I think a lot of folks are waiting on pins and needles for this earnings call today uh this afternoon and for good reason. Uh I expect Nvidia is going to put up another beat and a raise firmly. So I might add u ...

Core Insights - NEBIUS has deployed its first AI cloud infrastructure in London, utilizing NVIDIA's latest Blackwell Ultra GPU and Quantum-X800 InfiniBand technology, marking a significant step in its global AI cloud strategy [1] - This deployment aligns with the UK government's AI Opportunities Action Plan, aimed at enhancing the AI industry's competitiveness by providing large-scale AI training and inference capabilities to research institutions, government departments, and enterprises [1] - Following the announcement, NEBIUS's stock rose over 3%, while NVIDIA's stock saw a slight increase of 0.4% [1] Company Developments - NEBIUS's CEO Arkady Volozh stated that this deployment represents a new milestone for the company and signifies a more mature stage for the UK's AI ecosystem, enabling local institutions to train, deploy, and scale AI models and applications more quickly, securely, and sustainably [1] - The deployment comes shortly after the launch of NEBIUS's "Token Factory" inference platform, which supports open-source and customized AI inference tasks, providing enterprises and developers with more flexible computing power and AI toolchains [1] Industry Context - Industry experts note that as competition in AI large models intensifies, the supply of high-performance computing power globally becomes crucial, positioning NEBIUS's move as a strategic effort to capture the AI infrastructure market in the UK and Europe [2] - This initiative further solidifies NVIDIA's dominant position in the global AI chip supply chain [2]

Core Viewpoint - Eli Lilly collaborates with NVIDIA to build a powerful supercomputer and AI factory aimed at accelerating drug development in the pharmaceutical industry, expected to launch in January next year [2][4]. Group 1: AI in Drug Development - The pharmaceutical industry's efforts to utilize AI for accelerating drug approvals are still in the early stages, with no AI-designed drugs yet on the market, but an increase in AI-discovered drugs entering clinical trials [4]. - Eli Lilly's Chief AI Officer, Thomas Fuchs, describes the supercomputer as a novel scientific instrument, akin to a giant microscope for biologists [5]. - The supercomputer will enable scientists to train AI models through millions of experiments, significantly expanding the scope and complexity of drug discovery [6]. Group 2: Precision Medicine - The new AI tools are not solely focused on drug discovery but represent a significant opportunity to discover new molecules that humans may not identify [7]. - Eli Lilly emphasizes that new scientific AI agents can support researchers and advanced medical imaging can help in observing disease progression and developing biomarkers for precision treatment [9][10]. - NVIDIA's healthcare VP, Kimberly Powell, states that achieving the promise of precision medicine requires AI infrastructure, which is being built, with Eli Lilly serving as a prime example [11]. Group 3: Open Platform for Data Sharing - Multiple AI models will be available on the Lilly TuneLab platform, launched by Eli Lilly in September last year, which allows biotech companies to access drug discovery models trained on proprietary research data valued at $1 billion [13]. - The platform aims to broaden industry access to drug discovery tools, with Powell noting the significance of assisting startups that might otherwise take years to reach similar stages [14]. - In exchange for access to the platform, biotech companies are expected to contribute some of their research and data to help train the AI models [15].

Core Viewpoint - Eli Lilly collaborates with NVIDIA to build a powerful supercomputer and AI factory aimed at accelerating drug development, expected to launch in January next year [1][3] Group 1: Supercomputer and AI Factory - The supercomputer will consist of over 1,000 NVIDIA Blackwell Ultra GPUs connected through a unified high-speed network [3] - The system is designed to power an AI factory specifically for large-scale development, training, and deployment of AI models in drug discovery [3] - Eli Lilly's Chief Information and Digital Officer, Diogo Rau, indicated that significant returns from these new tools may not be realized until 2030 [3][6] Group 2: AI in Drug Discovery - Currently, no drugs designed using AI have been approved, but there is an increase in the number of AI-discovered drugs entering clinical trials [5] - Eli Lilly's Chief AI Officer, Thomas Fuchs, described the supercomputer as a novel scientific instrument that will allow scientists to train AI models through millions of experiments [6] - Rau emphasized that while drug discovery is a major focus, the new tools will also support other research areas [7] Group 3: Precision Medicine - Eli Lilly plans to use the supercomputer to shorten drug development cycles and enhance treatment efficacy [8] - Precision medicine aims to customize disease prevention and treatment based on individual genetic, environmental, and lifestyle differences [9] - NVIDIA's healthcare VP, Kimberly Powell, stated that AI infrastructure is essential for realizing the promise of precision medicine [10] Group 4: Data Sharing and Collaboration - Multiple AI models will be available on the Lilly TuneLab platform, which was launched last September, allowing biotech companies access to Eli Lilly's drug discovery models valued at $1 billion [12] - The platform aims to broaden industry access to drug discovery tools, with biotech companies contributing their research and data to help train AI models [13]

Core Insights - Microsoft has launched its first massive AI system, referred to as an AI "factory," which will be deployed across its Azure data centers to support OpenAI workloads [1] - Each AI system consists of over 4,600 Nvidia GB300s rack computers equipped with the Blackwell Ultra GPU chip, with plans to deploy "hundreds of thousands" of these GPUs globally [2] - The announcement follows OpenAI's significant data center deals with Nvidia and AMD, with OpenAI reportedly securing $1 trillion in commitments for its data center expansion by 2025 [3] - Microsoft operates more than 300 data centers in 34 countries, positioning itself to meet the demands of advanced AI applications [4] - Further details on Microsoft's AI workload capabilities are expected to be shared at the upcoming TechCrunch Disrupt event [5]

Core Insights - Nvidia and OpenAI have formed a strategic partnership to build the world's largest AI computing infrastructure network, which will include at least 10 gigawatts (GW) of AI-specific data centers and millions of Nvidia GPUs [1][2] - Nvidia is set to invest up to $100 billion in this project, with the first phase of the system expected to launch in the second half of 2026 using Nvidia's next-generation Vera Rubin supercomputing platform [1] - This collaboration addresses OpenAI's significant cost challenge related to computing power, as electricity costs currently account for 35% of the total expenses for training GPT-5 level models, while building their own data centers could reduce long-term operational costs by 70% [1] Group 1 - The initial Vera Rubin system will be operational in the third quarter of 2026 at a data center in Dallas, Texas, featuring 500,000 Blackwell Ultra GPUs, equating to the computing power of the top 50 supercomputers globally [2] - Nvidia and OpenAI plan to finalize details regarding equity distribution, technology sharing, and data security within the next six weeks, and will establish a joint governance committee to oversee project progress [2]

Summary of Lightmatter Passage Conference Call Industry and Company Overview - **Industry**: AI and Photonic Computing - **Company**: Lightmatter, known for its Passage M1000 "superchip" platform utilizing photonic technology to enhance AI training capabilities [1][3][13] Core Points and Arguments 1. **Exponential Growth of AI Models**: The scale of AI models has increased dramatically, with models now reaching hundreds of billions or even trillions of parameters, necessitating thousands of GPUs for training [3][4] 2. **Challenges in AI Training**: The industry faces significant challenges in scaling AI training, particularly due to the slowdown of Moore's Law and the limitations of traditional electrical interconnects, which create bottlenecks in data communication and synchronization [7][10][11] 3. **Lightmatter's Solution**: The Passage M1000 platform addresses the interconnect bottleneck by employing a 3D photonic stacking architecture, integrating up to 34 chiplets on a single photonic interposer, achieving a total die area of 4,000 mm² [13][14] 4. **Unprecedented Bandwidth**: The Passage platform delivers a total bidirectional bandwidth of 114 Tbps and 1,024 high-speed SerDes lanes, allowing each chiplet to access multi-terabit-per-second I/O bandwidth, effectively overcoming traditional I/O limitations [17][21] 5. **Comparison with Competitors**: Lightmatter's approach contrasts with other industry players like NVIDIA and Cerebras, who focus on maximizing single-chip performance or building ultra-large chips. Lightmatter emphasizes optical interconnects to achieve high bandwidth communication across chiplets [30][42][44][52] Additional Important Insights 1. **Nature Paper Validation**: A study published in *Nature* demonstrated the feasibility of photonic processors for executing advanced AI models, achieving near-electronic precision, which complements Lightmatter's focus on interconnect solutions [22][23][82] 2. **Future of AI Acceleration**: The combination of Lightmatter's optical interconnects and the advancements in photonic computing suggests a paradigm shift towards hybrid electronic-photonic architectures, breaking through performance ceilings in AI acceleration [82][83] 3. **Scalability and Efficiency**: Lightmatter's Passage aims to simplify AI deployments and improve efficiency by collapsing datacenter-level communication into a single "superchip," potentially offering better cost efficiency and flexibility compared to traditional methods [42][52][78] Conclusion - The emergence of Lightmatter's Passage platform represents a significant advancement in addressing the challenges of modern AI training, providing a breakthrough pathway through innovative photonic interconnect technology [84]