Arm (NasdaqGS:ARM) Q3 2026 Earnings call February 04, 2026 05:00 PM ET Company ParticipantsAndrew Gardiner - Head of European Technology Equity ResearchJason Child - EVP and CFOJeff Kvaal - Head of Investor RelationsRene Haas - CEOSimon Leopold - Managing DirectorConference Call ParticipantsCharles Shi - Senior AnalystHarlan Sur - Executive Director and Equity Research AnalystJoe Quattrocchi - Director and Senior Equity Research AnalystJohn DiFucci - Senior Managing Director and Equity Research AnalystKrish ...

Nvidia Corp. (NASDAQ:NVDA) stock rose Tuesday after OpenAI Chief Executive Sam Altman publicly reaffirmed his company’s commitment to the U.S. chipmaker. Altman Reaffirms Support for Nvidia In a post on X, Altman said Nvidia makes “the best AI chips in the world” and added that OpenAI intends to remain a major customer “for a very long time.” He also brushed aside the growing wave of speculation, calling it "insanity." “We love working with NVIDIA and they make the best AI chips in the world. We hope to b ...

Core Viewpoint - NVIDIA Corporation (NASDAQ:NVDA) is positioned as a leading blue chip stock following a $20 billion licensing agreement with Groq, enhancing its capabilities in AI inference technology [1][3]. Group 1: Licensing Agreement - NVIDIA has entered a $20 billion nonexclusive licensing agreement with Groq, allowing it to utilize Groq's inference Language Processing Units (LPUs) [1]. - The agreement includes the integration of Groq's personnel into NVIDIA, which may strengthen its technological capabilities [1]. Group 2: Market Position and Strategy - The licensing of Groq's LPU IP is aimed at enhancing NVIDIA's leadership in inference technology, which is critical for real-time AI applications [2]. - Analysts at Bernstein suggest that this investment is strategic for NVIDIA to solidify its market position as inference technology scales, potentially limiting competitors' access to Groq's technology [3]. Group 3: Product Focus - NVIDIA designs and sells specialized processors that are essential not only for gaming but also for AI, data centers, professional visualization, and the automotive industry [4].

Core Viewpoint - Qualcomm is showcasing a strong and competitive PC product portfolio at CES 2026, which is positively impacting its stock performance [1] Group 1: Product Launches and Partnerships - Qualcomm highlighted multiple PC launches in collaboration with brand partners such as Lenovo, Asus, and HP, utilizing X2 Elite SIP chipsets [2] - The current product lineup is said to deliver substantial performance leadership, covering over 95% of PC price points as the rollout progresses [2] Group 2: Performance and AI Capabilities - Qualcomm demonstrated performance benchmarking of its Snapdragon X2 Elite and X Elite chips, showing superior performance compared to key competitors, especially in low-power and unplugged scenarios [3] - The role of the Neural Processing Unit (NPU) was emphasized, which offloads workloads from the CPU, enhancing application performance and user experience [4] Group 3: Enterprise Applications and Long-Term Opportunities - Qualcomm is making progress in enterprise applications, including a fleet management solution for remote device management, supported by an embedded modem [5] - Devices incorporating this solution are expected to launch in the second half of the year, creating additional content opportunities for Qualcomm [6] Group 4: Data Center Strategy and Customer Engagement - Qualcomm's data center strategy focuses on inference workloads, with the company viewing Nvidia's acquisition of Groq as validation of distinct market requirements [6] - There is a significant increase in customer engagement around physical AI opportunities, with activity rising across various robotics applications [7] Stock Performance - Qualcomm shares were reported to be up 1.43% at $182.77 at the time of publication [7]

Core Insights - Nvidia's acquisition of Grok is characterized as an "aqua hire," focusing on technology licensing and talent acquisition rather than a traditional purchase, which reflects a strategic shift in big tech acquisitions [1][2] - The deal allows Nvidia to circumvent anti-competitive pressures that have affected previous acquisitions, such as the failed ARM deal [2][3] - Grok's founder, Jonathan Ross, brings significant expertise in chip design, particularly in neural networks and inference technology, which is crucial for Nvidia to enhance its competitive position against companies like Alphabet [2][3] Company Strategy - Nvidia aims to change the narrative around its capabilities in inference technology, an area where it has been perceived as lagging behind competitors [3][4] - The acquisition is expected to bolster Nvidia's software offerings alongside its hardware, enhancing its overall value proposition in AI processes [4][6] - Nvidia's current revenue model heavily relies on GPU sales, accounting for nearly 90% of its revenue, but the integration of Grok's technology could diversify its offerings [4][6] Market Position - Nvidia has approximately $300 billion in sales already booked for the next two years, indicating strong future revenue prospects [5][6] - The company is positioned as a key player in the AI landscape, with its software and hardware ecosystem making it a preferred choice for large-scale data center projects [7][8] - Nvidia's current trading at a 0.7 PEG ratio suggests it is undervalued relative to its growth rate, presenting an attractive investment opportunity [8] Future Outlook - The competitive landscape is expected to evolve, with both established players like Nvidia and new entrants in the AI application layer likely to thrive [10][11] - The ongoing development of AI technologies and applications will create new investment opportunities, particularly as the market resets and reassesses growth potential [11]

Core Insights - Nvidia has purchased $5 billion in Intel shares, marking a significant tech partnership aimed at enhancing collaboration in product development [1][2] - The acquisition of 214.7 million shares is seen as a crucial support for Intel, which has faced financial challenges due to past missteps and costly expansions [2] - The partnership will focus on developing customized data center and personal computing products to improve applications and workloads across various markets [3] Company Developments - The Federal Trade Commission (FTC) has cleared the way for Nvidia's investment in Intel, which is viewed as a strong endorsement of Intel by Nvidia [3] - Nvidia has also acquired talent and technology from Groq, a company specializing in custom-built inference chips, to enhance its capabilities in accelerated computing [3] - The collaboration with Groq aims to expand access to high-performance, low-cost inference technology, which is critical for AI applications [3][4] Industry Trends - Nvidia introduced the Nemotron 3 family of open models designed for efficient and specialized AI applications across industries [4] - Open-source AI models allow developers and enterprises to customize and integrate technology without restrictive licensing, contrasting with proprietary models [5][6] - The focus on inference as a dominant operational challenge highlights the growing importance of AI systems that can manage large volumes of requests efficiently [4][6]

AI Trade & Market Dynamics - The AI trade is expected to shift from broad enthusiasm to a more selective environment in 2026 [3] - A robust IPO market is anticipated, featuring private AI companies and SAS companies that didn't IPO in 2021 [4] - M&A activity is expected to be significant as enterprise companies seek to integrate AI into their architectures [4][5] Enterprise Adoption & Sector Impact - AI is transitioning from a consumer novelty to an integral part of business processes and workflows [8] - Enterprise adoption of AI will drive increased demand for inference, potentially requiring 10-50 trips back to LLMs for complex workflows [9] - The inference is becoming the heartbeat of global business, creating enormous demand for data centers [10] LLM & Data Center Considerations - The LLM market is expected to be highly competitive, with open-source models from Chinese companies, Meta, and Nvidia [11] - Leaders in the LLM market are likely to move up the stack, similar to Microsoft with Windows and Oracle with databases [11] - The data center trade is not a concern due to the expected imbalance between high demand and limited supply, despite capital and resource constraints [10][11]

Core Viewpoint - The competition between Broadcom and AMD to challenge Nvidia's dominance in the AI infrastructure market is intensifying, with both companies showing strong stock performance in 2025, particularly AMD with a year-to-date increase of over 70% compared to Broadcom's approximately 45% gain [1]. Summary by Company AMD - AMD is the second-largest player in the GPU market, focusing on the inference segment where cost-per-inference is crucial, and it has a competitive edge against Nvidia's CUDA software [3]. - Microsoft is developing a toolkit to convert CUDA code to AMD's ROCm, enhancing the use of AMD GPUs for inference, and AMD has partnered with OpenAI to deploy 6 gigawatts of GPUs, starting with 1 gigawatt next year, with OpenAI also acquiring a stake in AMD [4]. - In addition to GPUs, AMD is a leading provider of CPUs for computers and data centers, a rapidly growing market where it is gaining market share [5]. Broadcom - Broadcom approaches the AI chip market by designing custom AI ASICs, which are preprogrammed chips optimized for specific tasks, offering better performance and energy efficiency compared to traditional GPUs [6]. - The company has collaborated with Alphabet to develop Tensor Processing Units (TPUs), which have attracted other major data center operators as customers, with potential revenue from three key customers projected to exceed $60 billion by fiscal year 2027, and a $21 billion order from Anthropic for TPUs [7]. - Both AMD and Broadcom are trading at similar valuations, indicating a competitive landscape [8].

Key Concepts - KV caching accelerates inference by pre-computing the prompt's KV cache before token generation [1] - This pre-computation explains the longer time-to-first-token (TTFT) observed in models like ChatGPT [1] Performance Bottleneck - Time-to-first-token (TTFT) is a significant performance metric in inference [1] - Improving TTFT is an area for further research and development [1]

Educational Resources - Stanford's CS336 provides a video guide to Karpathy's nanochat, covering essential topics for Frontier AI Labs preparation [1] Key AI Concepts - The curriculum includes Tokenization, Resource Accounting, Pretraining, Finetuning (SFT/RLHF), Key Architectures, GPUs, Kernels, Tritons, Parallelism, Scaling Laws, Inference, Evaluation, and Alignment [1]