Based on the provided content, it's challenging to derive specific industry-relevant insights due to the lack of detailed information General Sentiment - The content expresses positive sentiment through interjections like "Oh" and "Yeah" [1] - The repetition of "Heat" suggests intensity or excitement [1]

Research Focus - The Scripps Research Institute focuses on studying viral antigen interactions with the immune system to inform vaccine and therapeutic design [1] - A significant portion of their research is dedicated to basic science, including protein stabilization and structure analysis [2] Technological Advancement & Challenges - The advent of direct electron detectors and advanced cameras has expanded research capabilities but also significantly increased data generation [2] - Cryo-EM data processing was a time-consuming task, potentially taking months to obtain results after data collection [6] Data Platform Solution & Impact - A primary consideration for the data platform solution is high uptime and availability, ensuring accessibility from various locations [3][4] - The implemented data platform enables real-time data capture from microscopes around the clock, accelerating workflows [4][5] - The platform allows scientists to focus on research rather than infrastructure concerns such as data storage and space limitations [7] - Generative AI models for protein design reduce manual work, allowing scientists to start with more mature models [5][6] - Faster data processing and analysis lead to quicker discoveries [8]

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]

AI Infrastructure & Market Trends - Modern AI applications are expanding across various sectors like finance, energy, healthcare, and research [3] - The industry is evolving from initial LLM training to Retrieval Augmented Generation (RAG) pipelines and agentic AI [3] - Vulture is positioned as an alternative hyperscaler, offering cloud infrastructure with 50-90% cost savings compared to traditional providers [4] - A new 10-year cycle requires rethinking infrastructure to support global AI model deployment, necessitating AI-native architectures [4] Vulture & DDN Partnership - Vulture and DDN share a vision for radically rethinking the infrastructure landscape to support global AI deployment [4] - The partnership aims to build a data pipeline to bring data to GPU clusters for training, tuning, and deploying models [4] - Vulture provides the compute infrastructure pipeline, while DDN offers the data intelligence platform to move data [4] Scalability & Flexibility - Enterprises need composable infrastructure for cost-efficient AI model delivery at scale, including automated provisioning of GPUs, models, networking, and storage [2] - Elasticity is crucial to scale GPU and storage resources up and down based on demand, avoiding over-provisioning [3] - Vulture's worldwide serverless inference infrastructure scales GPU resources to meet peak demand in different regions, optimizing costs [3] Performance & Customer Experience - Improving customer experience requires lightning-fast and relevant responses, making time to first token and tokens per second critical metrics [4] - Consistency in response times is essential, even with thousands of concurrent users [4] - The fastest response for a customer is the ultimate measure of customer satisfaction [4] Data Intelligence Platform - DDN's Exascaler offers high throughput for training, with up to 16x faster data loading and checkpointing compared to other parallel file systems [5] - DDN's Infinia provides low latency for tokenization, vector search, and RAG lookups, with up to 30% lower latency [5] - The DDN data intelligence platform helps speed up data response times, enabling saturated GPUs to respond quickly [6]

AI Development & Trends - The industry is focusing on achieving Artificial General Intelligence (AGI), aiming for AI that matches or surpasses human intelligence [1][2] - Reasoning is a key component in achieving AGI, with research institutions and enterprises focusing on reasoning models [2] - Reinforcement Learning (RL) is crucial for generalization capability in AI models, enabling consistent performance across varying data distributions [3][4] - AI is being integrated across various industries, including manufacturing, healthcare, education, and entertainment, impacting both automation and strategic decision-making [10] - Widespread adoption of AI is anticipated, driving insights, real-time analysis, and AI-powered solutions across industries [11] Company Solutions & Infrastructure - The company offers solutions for AI experimentation (Jupyter Notebooks, containerization), scalable training (distributed training jobs on GPUs), and deployment (virtual machines, containers) [6][7] - The company has data centers globally, including in the US, and is based in Singapore [7] - The company is utilizing DDN solutions to prevent data from becoming a bottleneck in AI training [8] - The company aims to make AI more efficient and cost-effective, allowing businesses to focus on innovation [12] - The company aims to transform high-performance computing by making AI computing accessible beyond big tech, focusing on developing AI in Singapore [14]

AI Development & Trends - The modern AI movement significantly accelerated with the advent of ChatGPT two and a half years ago [1] - Open models like LLaMA and Mistral are democratizing AI, making it accessible for broader deployment on-premise or in the cloud [1] - The industry is evolving from Retrieval Augmented Generation (RAG) models to agentic AI, focusing on making AI more actionable and integrated with enterprise data [1] - DeepSeek, an open model, has emerged as a significant advancement in AI, demonstrating reasoning capabilities comparable to proprietary models [1] Enterprise AI Adoption & Impact - AI is increasingly integrated into applications used by a billion knowledge workers, enhancing productivity [1] - 50% of organizations are expected to leverage AI agents to derive better value by 2025 [1] - AI is facilitating quicker content creation, exemplified by the ease of producing videos for platforms like YouTube [1] - NVIDIA focuses on providing a platform for partners to build AI-powered solutions, rather than developing end-user applications [1] Data & Infrastructure - Enterprise data is growing massively, with 11 zettabytes created, highlighting the need for AI to leverage this unstructured data [2] - NVIDIA emphasizes high-performance ingestion tools and data efficiency improvements (35% data improvement) to ensure accurate and reliable AI outputs [2] - NVIDIA Enterprise provides enterprise-ready AI solutions with constant CVEs, tech support, and ABI stability, ensuring solutions built today will continue to work [2] Customer Solutions & Partnerships - NVIDIA partners with companies like DDN to deliver AI solutions to customers, focusing on solving customer problems and driving revenue [1][2] - NVIDIA's Nemo solutions and custom models have enabled partners like Justt.ai to achieve rapid growth and customer adoption [2] - SAP is leveraging NVIDIA's AI to improve its ABAP programming language, helping customers clean up code and solve 80% of coding problems [2]

Customer Success & Technology - DDN emphasizes customer success through its HPC experience, focusing on scale, end-to-end configurations, and tuning to ensure successful deployments [2] - DDN's partnership with Nvidia is 30 years old, with Nvidia using DDN for testing, including 4,000 Blackwell GPUs in their lab [2] - DDN highlights the importance of two pillars: Exascaler for large-scale GPU deployments and Infinia as a data intelligence platform [9][10] - DDN's solutions are designed for simple scalability, allowing customers to easily increase capacity by adding more DDN units [11] AI Market & Deployment - AI is permeating across various sectors, including cloud, industries, and daily life, driving significant changes in the next 10 years [3] - DDN is experiencing high demand and is actively hiring to meet customer needs and maintain delivery value [3] - DDN estimates supporting over 1 million GPUs before summer [3] Key Verticals & Partnerships - DDN serves various markets, including NCP/AI providers, healthcare (San Jud), financial services (HFT, fraud detection), energy (oil and gas), automotive (4GM, Tesla), defense, and public sector (NASA) [3] - DDN collaborates with technology partners like Super Micro, Nvidia, and others to deliver solutions at massive scale [6] - DDN has AI cloud program partners, including GCP and Scaleway, offering DDN solutions [6] Product Performance & Innovation - DDN's systems are running at 100% flawlessly [4] - Xia is running a quarter of a thousand GPUs based on 150 terabytes of Exascaler and close to 600 petabytes of Infinia for production [7] - DDN values customers who push the limits of their products, driving innovation and improvements [8]

Thank you. Um I'm Jen from SK Terracon from South Korea and uh today I'd like to give a talk about the storage virtualization and optimization for GPU as a service that what we are trying to do recently. And uh first of all I think you are not quite familiar what is SQL.So I would like to give a brief like introduction to the SK talon. JSK is a novel metal player in South Korea and we made the most of the market penetrations in South Korea and recently we are trying to transform from the NNO to the AI compa ...

I'm here to talk about the partnership with DDN and I don't think I need to convince you how much of an opportunity it is you know for us um in the marketplace otherwise you would not be here uh but um you know we measure that in trillions right we measure in trillion is it 5 trillion 10 trillion 1 trillion it's okay we can take a chunk of it and and suddenly for many customers partners and maybe consultants in the room. Uh we've all started to uh to deliver solutions, make money of it. And I kind of joke w ...

Product Overview - Infinia is a software-defined, next-generation data intelligence platform designed to support both structured and unstructured data, integrating diverse data sources into a unified platform [1] - Infinia aims to reduce complexity in AI deployments and can be deployed on various hardware configurations, including cloud, data centers, reference architectures, and OEM solutions [1] - Infinia operates on the premise of SLAs for capacity, performance, and resilience, abstracting away traditional storage management complexities like volumes and IOPS capping [1] Key Features and Capabilities - Infinia focuses on simplifying data ingestion with low-latency access, emphasizing data tagging for categorization and retrieval in complex environments with billions of objects [1] - Infinia addresses limitations in existing storage solutions by offering unlimited metadata scalability, allowing for thousands of tags per object to refine data definition [1] - Infinia includes capabilities for filtering data at the edge, enabling efficient transfer of relevant data to the core data center for analytics [2] - Infinia offers a software development kit (SDK) to enable customers and service providers to accelerate their applications by using a high-performance interface into Infinia [2] Performance and Efficiency - Infinia achieved a 22x speedup in an indexing and ingest workload on AWS by integrating Nvidia GPUs and utilizing Infinia's S3 interface, resulting in a 70% reduction in GPU resource consumption [1][2] - The company demonstrated a factor of 600 speedup in certain workloads related to identifying data for training AI models [1] Architecture and Integration - Infinia implements data services on top of a key-value store, providing different views onto the data through protocols like S3, file interfaces, and SQL interfaces [2] - Infinia includes a massively distributed SQL server, allowing native access to data and metadata stored in the key-value store through SQL queries [2]