Industry Focus - Pharmaceutical Development - The pharmaceutical industry faces high costs (billions of dollars) and lengthy timelines (years) for drug development, including FDA approval [1] - Traditional drug development involves sequential or parallel exploration of multiple avenues, which can be inefficient [1] Technological Solution - Digital Twins and Omniverse - The company proposes using digital twins in the Omniverse to simulate drug development processes [1] - The Omniverse is described as a "phenomenal thing" that can revolutionize how things are done [1] Potential Benefits - Combining attributes from different approaches (e.g., "pass number one" and "pass number four") in the digital environment can maximize the likelihood of success [1] - This approach can compress the time to market for new drugs and maximize their benefits [1]

AI Infrastructure and Architecture - Infinia was conceived due to the need for a different architecture for AI, one that scales efficiently for training, has low latency, is distributed on-premise and multi-cloud, and minimizes data movement [1] - The industry is shifting towards Data Intelligence, reframing storage of raw data into informational form, which is a new opportunity for DDN to provide data intelligence for enterprises running AI [1] - Metadata and tagging are essential for multimodal AI, enabling the movement of metadata and making the economics viable due to the compression ratio [1] AI Application and Adoption - Enterprises need to adopt AI at an accelerated pace, requiring the application layer to be supercharged and the infrastructure to be efficient [1] - The industry is moving from high-performance computing to Enterprise, and then to digital twins of Enterprise, enabled by technologies like Omniverse [2] - AI is enabling companies to create digital twins, allowing them to run thousands of experiments simultaneously and optimize outcomes, applicable to enterprises, governments, and individuals [2] AI Model and Ecosystem - Post-training, which involves problem-solving and reasoning, is a crucial and compute-intensive part of intelligence, following pre-training [3] - The release of open-source reasoning models like DeepSeek's R1 is accelerating AI adoption by highlighting opportunities for more efficient models [3] - The CUDA ecosystem is enabling the application of AI in specific industries like Life Sciences, Financial Services, and autonomous driving [3] Strategic Partnership and Future Vision - The partnership between Nvidia and DDN is expanding from supercomputing to Enterprise and Omniverse, with Infinia playing a key role [4] - Companies should both use public cloud AI and build their own specialized AI, curating AI agents from various sources to solve large problems [3] - Differentiation for organizations comes from specialized application of AI, enabled by technologies like Nvidia's Nims and DDN's Infinia [4]

Core Insights - The importance of inference computing power is increasing, with significant growth expected in related sectors such as optical modules and chips, driven by advancements in hardware and software from companies like NVIDIA [1][4] Group 1: Inference Computing Power Growth - Inference computing power is projected to continue growing, with NVIDIA's CEO stating that the demand for computing power will be 100 times greater than in the past to support advancements like AGI and embodied intelligent robots [1][2] - The number of tokens processed by models has increased to over 100 trillion, with inference models requiring 20 times more tokens and 150 times more computational power than before [2] - NVIDIA's Blackwell architecture shows a performance improvement of 68 times over the previous Hopper architecture, leading to an 87% reduction in costs [2] Group 2: Hardware and Software Developments - NVIDIA introduced the upgraded Blackwell Ultra architecture, emphasizing its potential to generate 50 times more revenue for data centers, with a clear development roadmap extending to 2026 and beyond [3] - The launch of the open AI engine stack, Nvidia Dynamo, aims to simplify inference deployment and scaling, potentially creating a new paradigm for efficiency in hardware and software [3] - The introduction of Nvidia Llama Nemotron is expected to serve as a foundational model for inference, facilitating exploration in related areas and forming an ecosystem [3] Group 3: Investment Recommendations - The current landscape indicates that the demand for computing power is not declining but is instead being stimulated by the growth of inference applications, suggesting substantial investment opportunities in the sector [4] - Recommended investment targets include telecom operators such as China Mobile, China Unicom, and China Telecom, as well as companies in optical modules and chips like Zhongji Xuchuang, Xinyi Guosheng, and others [4]