Fourth Quarter and Fiscal 2025 Earnings November 5, 2025 Snapdragon and Qualcomm branded products are products of Qualcomm Technologies, Inc. and/or its subsidiaries. References in this presentation to "Qualcomm" may mean Qualcomm Incorporated, Qualcomm Technologies, Inc., and/or other subsidiaries or business units within the Qualcomm corporate structure, as applicable. In addition to historical information, this document and the conference call that it accompanies contain forward-looking statements that a ...

Core Viewpoint - Qualcomm's entry into the AI inference chip market is seen as a strategic move to compete with Nvidia, which has a dominant position in the sector, particularly in the cloud inference market [2][3][4]. Qualcomm's AI Inference Solution - Qualcomm announced its AI inference optimization solution for data centers, which includes the Qualcomm AI200 and AI250 cloud AI chips, along with corresponding accelerator cards and racks [2]. - The launch has positively impacted Qualcomm's stock, with a peak increase of 22% during trading, closing with an 11% rise, adding nearly $20 billion to its market capitalization [2]. Market Dynamics and Competition - Analysts suggest that Qualcomm's experience in edge chips could lead to new business growth in AI inference chips, as the market seeks to avoid Nvidia's monopoly [3]. - The global AI inference chip market is projected to grow from approximately $14.21 billion in 2024 to $69.01 billion by 2031, with a compound annual growth rate (CAGR) of 25.7% from 2025 to 2031 [5]. Technical Advantages and Challenges - Qualcomm emphasizes a low Total Cost of Ownership (TCO) but needs to prove its competitive edge in energy efficiency and memory processing capabilities in real-world scenarios [4]. - Nvidia's rapid iteration speed and technological advancements, such as the Rubin CPX platform, provide significant advantages in terms of token processing and cost efficiency [4]. Collaboration and Customization - Qualcomm has partnered with Saudi AI company HUMAIN to deploy its AI200 and AI250 solutions, with a planned scale of 200 megawatts starting in 2026 [5]. - The collaboration aims to develop cutting-edge AI data centers and hybrid AI inference services, focusing on customized solutions to meet specific client needs [5]. Hardware Specifications - Qualcomm's AI200 supports 768 GB LPDDR memory, while the AI250 is expected to adopt an innovative near-memory computing architecture, enhancing memory bandwidth and reducing power consumption [7][8]. - The comparison of specifications shows that Qualcomm's chips have a significant memory capacity advantage, which is crucial for private deployments [7][8]. Software Ecosystem Development - Qualcomm is also enhancing its software ecosystem to support its AI inference products, optimizing for leading machine learning frameworks and inference engines [9]. - The integration of Qualcomm's network chips is expected to create products with performance advantages in the competitive landscape dominated by Nvidia [9].

Core Viewpoint - Qualcomm is making a significant move into the data center market by launching next-generation AI inference optimization solutions, including the Qualcomm AI200 and AI250 chips, which are expected to be commercially available in 2026 and 2027 respectively [1][3][5]. Group 1: Product Launch and Features - Qualcomm has introduced the Qualcomm AI200, a dedicated rack-level AI inference solution designed for large language models (LLM) and other AI workloads, offering low total cost of ownership (TCO) and optimized performance [5]. - The Qualcomm AI250 solution will utilize near-memory computing architecture, achieving over 10 times effective memory bandwidth and lower power consumption, enhancing the efficiency and performance of AI inference workloads [5][8]. - Both solutions employ direct liquid cooling for improved thermal efficiency and support PCIe for vertical expansion and Ethernet for horizontal expansion, with a total rack power consumption of 160 kilowatts [8]. Group 2: Market Strategy and Historical Context - This is not Qualcomm's first attempt to penetrate the data center market; a previous effort in 2017 with an Arm-based data center CPU product did not succeed [3][16]. - Qualcomm has strengthened its hardware and software capabilities through acquisitions and partnerships, positioning itself differently compared to its previous attempts [3][17]. - The company is currently in the early stages of market development, engaging with potential customers and has announced a partnership with HUMAIN to deploy advanced AI infrastructure in Saudi Arabia [9][11]. Group 3: Financial Implications and Market Position - Qualcomm's QCT (chip business) revenue is heavily reliant on mobile hardware, which accounted for 70.37% of its revenue, while the data center business has yet to show significant financial impact [14]. - The AI inference market is expected to grow more than the AI training market, with numerous players, including cloud service providers and emerging AI chip companies, competing for market share [17][19]. - Qualcomm's strategy includes leveraging its historical expertise in CPU and NPU fields to capitalize on the shift from commercial x86 CPUs to custom Arm-compatible CPUs, creating new growth opportunities [8][19].

Core Viewpoint - Qualcomm is making a significant move into the data center market with the launch of its next-generation AI inference optimization solutions, including the Qualcomm AI200 and AI250 chips, which are expected to be commercially available in 2026 and 2027 respectively [1][3][4]. Group 1: Product Launch and Market Strategy - Qualcomm announced the introduction of AI200 and AI250, targeting AI inference workloads with a focus on low total cost of ownership (TCO) and optimized performance [4][8]. - The AI200 solution is designed for large language models (LLM) and multimodal models (LMM), while the AI250 will utilize near-memory computing architecture to achieve over 10 times effective memory bandwidth [4][8]. - Both solutions will feature direct liquid cooling for improved thermal efficiency and will support PCIe and Ethernet for scalability [7][8]. Group 2: Historical Context and Competitive Landscape - This is not Qualcomm's first attempt to enter the data center market; a previous effort in 2017 with the Centriq 2400 processor did not succeed due to a lack of market acceptance [3][18]. - Qualcomm has strengthened its capabilities through acquisitions and partnerships, including the acquisition of Nuvia for $14 billion, which focuses on data center CPUs [19]. - The company is also pursuing the acquisition of Alphawave IP Group, which will enhance its high-speed connectivity solutions for data centers [19]. Group 3: Market Opportunities and Challenges - Qualcomm's expansion into the data center market is seen as a new growth opportunity, especially as cloud service providers are building dedicated inference clusters [8][9]. - The AI inference market is expected to grow faster than the AI training market, with many players, including custom ASICs from cloud service providers, competing for market share [20]. - Qualcomm's differentiation strategy includes using LPDDR memory instead of the more common HBM, aligning with its goal of lower TCO [8][20]. Group 4: Initial Partnerships and Future Prospects - Qualcomm has announced its first customer for the new data center products, HUMAIN, a national AI company in Saudi Arabia, which plans to deploy 200 megawatts of Qualcomm's solutions starting in 2026 [10][9]. - The success of Qualcomm's data center strategy will depend on the performance validation of its products in real-world applications and the establishment of a robust software ecosystem [20].

Core Insights - Qualcomm is entering the data center AI chip market with its AI200 and AI250 chips, aiming to challenge Nvidia's dominance in this space [1][2] - Qualcomm's stock surged nearly 21% on the announcement, reflecting positive market sentiment and increasing its market capitalization by approximately $28 billion [2] - The company is diversifying its business due to structural growth challenges in its core mobile chip segment, particularly from Apple's move towards in-house chip development [4][5] Qualcomm's AI Chip Strategy - Qualcomm's AI200 and AI250 chips are designed for low power consumption, high cost-performance, and modular deployment, with the AI200 expected to launch in 2026 and the AI250 in 2027 [1][11] - The AI200 features 768GB LPDDR memory, significantly more than Nvidia's offerings, and supports flexible deployment options [10][11] - The AI250 introduces a "near-storage computing" architecture, promising over ten times memory bandwidth improvement and reduced power consumption [11] Market Context - Nvidia's revenue from data center AI chips has surged, with an increase of over $100 billion from 2022 to 2024, highlighting the rapid growth in this market [8][10] - The global AI chip market is projected to see a shift towards ASIC chips, which are expected to capture a larger market share by 2027, providing an opportunity for Qualcomm [9][10] - The demand for AI chips is expected to grow significantly, with projections of 10 million units in 2025 and 17 million in 2027, indicating a robust market for specialized chips [9] Competitive Landscape - Qualcomm faces strong competition from Nvidia, which has established a powerful ecosystem through its CUDA platform, making it challenging for new entrants to gain traction [11] - Other tech giants like Google, Amazon, and Microsoft are also developing their own AI chips, which could impact Qualcomm's ability to attract large clients [11] - Despite these challenges, Qualcomm's experience in low-power chip design positions it well to capitalize on the growing demand for efficient AI solutions in data centers [10]

Core Viewpoint - Qualcomm is entering the AI market with new AI-focused accelerators, aiming to diversify beyond its traditional smartphone chip business, which has been impacted by the loss of major customers like Huawei [1][3]. Group 1: Product Launch and Market Timing - Qualcomm launched two AI accelerators, the AI200 and AI250, which are designed for high performance per dollar and watt, potentially driving adoption in the data center market [4]. - The timing of Qualcomm's entry into AI chips aligns with increasing investments in the sector, as cloud giants seek to build systems for generative AI models and chatbots [4]. - The new chips are expected to be commercially available in 2026 and 2027, targeting AI-driven demand [4]. Group 2: Competitive Landscape - The AI data center market is currently dominated by Nvidia, with AMD also gaining market share with its next-generation AI accelerators [5]. - The high cost of switching AI platforms poses a challenge for new entrants like Qualcomm, but its rack-based solutions with direct liquid cooling features offer a competitive edge in energy efficiency and cost-effective performance [5]. Group 3: Customer Engagement and Revenue Expectations - Qualcomm is in discussions with major potential customers, including a leading hyperscaler, indicating early traction in a challenging market [6]. - If these discussions lead to design wins, Qualcomm anticipates meaningful revenue contributions from its AI data center business starting around fiscal 2028 [6]. Group 4: Strategic Acquisitions and Partnerships - Qualcomm's ambitions in the AI data center space are bolstered by its pending acquisition of Alphawave IP Group, which specializes in high-speed connectivity and compute technologies [7]. - This acquisition, expected to close in early 2026, will enhance Qualcomm's data center design capabilities and complement its core processor technologies [7]. - The combination of this acquisition and in-house development of NPU-based accelerators strengthens Qualcomm's vertical integration strategy [7].

Core Insights - Qualcomm has launched next-generation AI inference optimization solutions for data centers, featuring the Qualcomm AI200 and AI250 chips, which provide rack-level performance and memory capacity for high-performance generative AI inference [1][2] Group 1: Product Features - The Qualcomm AI200 is designed specifically for rack-level AI inference, supporting large language models (LLM) and multimodal models (LMM) with a total memory capacity of 768GB LPDDR, offering low total cost of ownership and optimized performance [1] - The Qualcomm AI250 introduces an innovative near-memory computing architecture that enhances effective memory bandwidth by over 10 times while significantly reducing power consumption, thus improving efficiency and performance for AI workloads [1][2] Group 2: System Capabilities - Both solutions support direct liquid cooling for enhanced thermal efficiency, PCIe vertical expansion, Ethernet horizontal expansion, and confidential computing to ensure the security of AI workloads, with a total system power consumption of 160 kilowatts [2] - Qualcomm emphasizes that these AI infrastructure solutions allow customers to deploy generative AI with industry-leading total cost of ownership while meeting modern data center demands for flexibility and security [2] Group 3: Software Ecosystem - Qualcomm offers a comprehensive AI software stack that spans from application to system software layers, optimized for AI inference, supporting mainstream machine learning frameworks and inference engines [2] - Developers can utilize Qualcomm's Efficient Transformers Library and Qualcomm AI Inference Suite for seamless model integration and one-click deployment of Hugging Face models, providing ready-to-use AI applications and operational services [2] Group 4: Future Plans - The Qualcomm AI200 and AI250 are expected to be commercially available in 2026 and 2027, respectively, with the company committed to advancing its data center product technology roadmap annually, focusing on AI inference performance, efficiency, and total cost of ownership advantages [3]

Core Insights - Qualcomm has launched a new series of artificial intelligence chips to compete with Nvidia, which currently holds approximately 90% of the AI chip market [2][4] - The first chip in Qualcomm's AI series, the AI200, is expected to be commercially available in 2026, followed by the AI250 in 2027 [2] - Qualcomm's stock experienced a 20% increase following the announcement of its entry into the data center market [2] Company Strategy - Qualcomm plans to offer purpose-built AI server racks containing multiple AI chips for data centers, as well as standalone AI chips for enterprises to integrate into existing servers [3] - The company aims to position itself as an energy-efficient alternative in the AI chip market, focusing on long-term cost savings [4] Market Demand - There is a growing demand for AI inference chips due to increased adoption and new use cases, with major companies like Amazon, Google, and Microsoft developing their own AI chips [5] - An estimated $7 trillion will be spent on data centers through 2030, indicating significant investment opportunities in this sector [5] Competitive Landscape - Qualcomm joins other semiconductor companies like Intel and AMD in the AI chip market, seeking to diversify beyond its traditional smartphone business [6] - OpenAI has recently signed a $10 billion deal with Broadcom for custom AI chips, highlighting the competitive nature of the AI chip industry [7] Partnerships and Collaborations - Qualcomm has secured its first customer for the new AI chip series, Saudi Arabia's Humain, which plans to deploy the chips in its data centers starting in 2026 [7] - Humain is also launching a $10 billion venture fund to support AI initiatives, indicating a strong interest in AI infrastructure development [8]

Core Insights - Qualcomm has launched next-generation AI inference optimization solutions for data centers, including acceleration cards and rack systems based on Qualcomm AI200 and AI250 chips, focusing on rack-level performance and memory capacity optimization to support generative AI inference across various industries [1][3]. Group 1: Qualcomm AI200 and AI250 Solutions - The Qualcomm AI200 solution is designed for rack-level AI inference, targeting large language models (LLM), multimodal models (LMM), and other AI workloads, with advantages in low total cost of ownership and performance optimization. Each acceleration card supports 768GB LPDDR memory, meeting high memory capacity needs while controlling costs [3][4]. - The Qualcomm AI250 solution introduces a near-memory computing architecture that achieves over 10 times effective memory bandwidth improvement while significantly reducing power consumption, enhancing efficiency and performance for AI inference workloads. It also features decoupled AI inference capabilities for efficient hardware resource utilization [3][4]. Group 2: Common Features and Software Support - Both Qualcomm AI200 and AI250 rack solutions share several common technical designs, including support for direct liquid cooling to enhance thermal efficiency, compatibility with PCIe vertical expansion and Ethernet horizontal expansion to meet various deployment needs, and built-in confidential computing features to ensure the security of AI workloads. The total power consumption for the entire rack is controlled at 160 kilowatts, aligning with data center energy management standards [3][4]. - Qualcomm provides a large-scale AI software stack that covers the entire link from application layer to system software layer, optimized for AI inference scenarios. This software stack supports mainstream machine learning frameworks, inference engines, generative AI frameworks, and decoupled services for LLM/LMM inference optimization [4][5]. Group 3: Future Plans - The Qualcomm AI200 is expected to be commercially available by 2026, while the AI250 is planned for market launch in 2027. Qualcomm aims to iteratively advance its data center product technology roadmap annually, focusing on optimizing AI inference performance, energy efficiency, and total cost of ownership to better meet the evolving demands of generative AI [5].

Qualcomm - Qualcomm has entered the artificial intelligence accelerator market, aiming to compete with Nvidia and AMD [3] - The company launched the AI200 and AI250 chip-based accelerator cards, with shares rallying over 10% following the announcement [4] - The AI200, set for release in 2026, offers higher memory capacity at a lower cost, while the AI250, available in 2027, features innovative memory architecture that provides 10 times higher effective memory bandwidth and reduced power consumption [4][5] - Qualcomm's products are designed to enable customers to deploy generative AI with unprecedented total cost of ownership, and another AI accelerator is expected to be unveiled in 2028 [5] Organon - Organon’s CEO Kevin Ali resigned amid a scandal involving manipulation of sales results, leading to a significant drop in the company's stock [6] American Water Works and Essential Utilities - American Water Works and Essential Utilities are merging in an all-stock deal to form a water and wastewater utility with a combined enterprise value of approximately $63 billion [7] Keurig Dr Pepper - Keurig Dr Pepper reported sales growth across all segments in Q3 and detailed its strategy and leadership changes related to the acquisition of JDE Peet's [8] Beyond Meat - Beyond Meat reported preliminary Q3 revenue of $70 million, slightly above estimates but down 13% year-on-year, leading to a negative outlook from analysts [9][10] Argentina's Financial Markets - Argentina's financial markets surged following President Javier Milei's party winning significant midterm legislative elections, with the S&P MERVAL Index rising 20% and the Global X MSCI Argentina ETF increasing by 18% [10][11]