Pharmaceutical Industry Challenges - Bringing a new drug to market is a complex process requiring significant data processing, primarily write-intensive simulations [1] - The pharmaceutical industry needs an AI infrastructure and data intelligence platform to run complex simulations at massive scale [2] DDN's Solution - DDN provides a fast and efficient data intelligence platform for complex simulations [2] - DDN has over a decade of experience in deploying and optimizing these types of constructs, powering hundreds of sites [2]

Alex, I want to speak about AI helping with global challenges, real challenges, things like food security, global health, climate change. Where do you think AI plays there and how long is it going to take for it to have real impact. I think we need to look at AI as an enabler to help us solve global challenges faster and hopefully better.Uh AI is not a replacement of humans to solve these challenges. It's a tool which will give us the ability to do so faster, more cost effectively and better hopefully. ...

[Music] together ddn and super micro deliver cunning Edge solution that Empower companies to tackle the most demanding HPC and AI workloads at any scale with amazing performance and efficiency now let's get the perspective of Charles yangang CEO of Super Micro on the benefits of our important partnership to provide customer amazing solution ddn and Sh have been working together for more than 20 years and together with your best class of data intelligent platform and our Bing b solution for Server optimizati ...

Infinia Platform Overview - Infinia is a software-defined, metadata-driven, containerized, cloud-native data intelligence platform designed for scalability, performance, and efficiency across core, cloud, and edge environments [1] - The platform supports critical data protocols like object and block, integrating with AI data acceleration libraries like TensorFlow and PyTorch [1] - Infinia enhances AI execution engines by serving data in its native form, reducing the need for data conversion and speeding up applications [1] Metadata and Multi-Tenancy Capabilities - Infinia allows for tagging massive amounts of metadata to objects, enabling faster data discovery and processing, with no limitations on metadata capability [1] - The platform has built-in multi-tenancy capabilities, providing SLAs for individual tenants and sub-tenants on capacity and performance, ensuring quality of service [1] Scalability and Cloud Native Design - Infinia is fully containerized, allowing for scale-out at web scale, starting from a few terabytes and scaling to exabytes [1] - The product is designed to be cloud-native and will soon be available in leading cloud provider marketplaces [2] AI Data Challenges and Solutions - Infinia addresses the complexity of managing large amounts of distributed multimodal data across core, cloud, and edge environments by creating a unified platform [1] - It tackles the demand for extremely low latency required to run AI applications, as well as the high costs associated with running AI [1] - The platform ensures data protection at any time and at any scale [1] Performance Metrics - Infinia can deliver time to first byte in less than a microsecond [2] - It can deliver 30 to 40 million objects per second in list object operations [2] - Infinia can deliver terabytes per second throughput at large scale [2] Efficiency and Sustainability - Infinia can achieve 10x data reduction, fitting over 100 petabytes of storage into a single rack [2] - It can reduce the overall data center footprint by a quarter compared to competitors, saving 10x power and cooling costs [2] Security Features - Infinia focuses on security authentication and access control, preventing unauthorized data access [2] - Data is always encrypted, and all actions within the system are audited [2] - The platform provides 99.9999% uptime enabled by reliability-focused features [2] Key Business Outcomes - Infinia aims to reduce complexity and achieve more accurate results on a unified platform for AI inference, data analytics, and model training [2] - It accelerates innovation by running AI apps faster, enabling businesses to beat the competition [2] - The platform enables rapid deployment across the cloud core and the edge to increase productivity, boost efficiency, and maximize ROI [2]

AI Impact & Applications - AI is transforming various industries, including automotive (making autonomous vehicles safer), digital twins & Omniverse (optimizing complex systems), and healthcare & life sciences (driving breakthroughs in drug discovery and personalized medicine) [1] - AI is improving healthcare by enabling early disease detection, precision treatments, and saving lives, exemplified by Scripps Research Center's work in leveraging AI for patient care [1] - Robotics industry is heavily augmented by AI, which is expected to change the way humans interact with machines [3] DDN's Role & Technology - DDN provides a data intelligence platform that accelerates AI initiatives across industries, in data centers and clouds, AI factories, Sovereign AI, digital twins, and Omniverse [1] - DDN's technology initially solved supercomputer data challenges, powering 60% of the world's fastest supercomputers [1] - DDN's AI data platform is deployed across more than half a million GPUs in data centers and clouds worldwide [2] - DDN accelerates AI applications by orders of magnitude, pulling data from diverse sources and boosting the performance of NVIDIA's GPUs, resulting in 10x faster AI pipelines [1] - DDN's new data intelligence platform, Infinia, enables more efficient compute by reducing infrastructure costs and speeds up data ingestion, leading to a 10x performance boost for developers [2] Partnership & Investment - Blackstone has invested $300 million in DDN, bringing its valuation to $5 billion, to accelerate data platforms for Enterprise AI and Cloud providers [2] - DDN has an ecosystem of over 200 global partners, including Super Micro, to deliver end-to-end solutions to customers [3] - DDN and Super Micro have been working together for more than 20 years, delivering solutions for HPC and AI workloads [3] Business Value & ROI - Integrating DDN with AI applications optimizes data movement and accelerates checkpoint and model load operations, freeing up GPU resources and boosting productivity [2] - Adding DDN to NVIDIA investments can increase ROI by a factor of 30 times, translating into meaningful revenue gains for customers [2] - DDN is trusted by 85% of the Fortune 500 companies today [2]

DDN Exoscaler产品特性与优势 - DDN Exoscaler是一个并行文件系统，专为大规模数据处理而设计，旨在加速GPU流量，提高GPU的生产力，适用于生命科学、金融等多种行业 [1] - 该技术通过消除IO等待时间，使GPU能够持续获取数据，从而加速模型训练、推理以及提高token生成速度 [1] - DDN的解决方案旨在以最小的物理空间、功耗和网络占用提供最大的性能 [1] - DDN提供多种闪存配置（TLC、QLC）和混合系统（HDD），以满足不同客户在成本、性能和容量方面的需求，并支持将这些不同介质类型挂载到同一挂载点 [1][2] - DDN Exoscaler的客户端具备智能性，能够感知数据位置，从而优化数据访问路径，提高效率 [2] - DDN提供数据缩减系统，通过客户端压缩机制，在不影响存储性能的前提下实现数据压缩，数据缩减率通常在2到4倍之间，对于文本和日志数据最高可达50倍 [2] - DDN提供在线升级功能，允许在系统运行过程中进行升级，这对需要保持服务持续性的客户至关重要 [1][2] - DDN提供EMF分析工具，用于全面测试网络，帮助客户快速发现和解决网络问题，确保系统稳定运行 [2] - DDN Exoscaler支持多种协议访问，包括S3、NFS、SMB以及原生并行文件系统，并兼容Prometheus和Grafana等开源监控工具 [2] - DDN的监控系统能够显示哪些用户、客户端或作业正在对文件系统造成压力，帮助云服务提供商确保公平的数据访问 [2] DDN AI400X3产品 - DDN推出AI400X3，专为Nvidia Blackwell架构设计，旨在满足GPU技术快速发展带来的数据存储和访问需求 [1] - AI400X3在2U空间内提供150 GB/s的网络吞吐量，并提供95 GB/s的checkpoint速度 [1][3]

Infinia Platform Overview - Infinia is a data intelligence platform, not just a traditional storage product, offering S3 object interface, CSI, Cinder, and file system interfaces [1] - The platform is designed for large-scale deployments, already tested at almost an exabyte in size across approximately 1,000 nodes [1][2] - Infinia is a pure software product that can be integrated into the cloud, with a system already running at GCP for testing [2] Key Features and Capabilities - Infinia supports extensive metadata tagging, allowing tens of thousands of metadata attributes per object for enhanced data discovery and enrichment [1][3] - The system is highly multi-tenant, enabling service providers to manage large-scale systems efficiently while providing SLAs for individual end-users [1][2] - Infinia offers quality of service (QoS) at the application level, allowing prioritization of performance for critical tasks [2] - The platform supports online upgrades, capacity expansion, and reduction without downtime [2] Data Intelligence and AI Workloads - Infinia can serve as a Lakehouse on-premise, providing object, block, and parallel file system access to the same data [2][3] - Remote bucket support allows Infinia to pull metadata from existing data sets, enabling querying and caching of data from external sources [2] - Native library support and SDK can provide up to 10x performance improvement for data ingestion with frameworks like Spark [3] Performance and Scalability - The system is designed for very large scale, with deployments reaching almost an exabyte of capacity [1][2] - Infinia can deliver millions of object operations per second with single-digit millisecond latency [4] - Client-side data reduction and erasure coding eliminate east-west traffic, improving overall performance [3] Resilience and Availability - The system demonstrated high resilience by maintaining operation with only a short IO delay (10-15 seconds) after an entire rack of 480 drives went offline [4]

DDN's Market Positioning & Strategy - DDN is recognized as a key player in high-performance computing, particularly by Nvidia, who has been using DDN exclusively for the past eight years [1] - DDN's technology is integral to Nvidia's testing and development, including platforms like Selene A100, Eos H100 (4,000 GPUs), and GB200 [1] - DDN emphasizes its ability to scale solutions from small implementations (2U) to massive deployments (100,000+ GPUs), validated at 100% [1] - DDN focuses on investing in R&D, engineering talent, and feature development for both Exascaler and Infinia, reinvesting customer dollars back into the company [2] Technological Advantages & Solutions - DDN's solutions offer better GPU efficiency through checkpointing, data loading, and data crunching, with significantly faster write performance compared to competitors [2] - DDN's architecture simplifies deployments with fewer network ports, enhancing stability and scalability, avoiding full mesh requirements seen in competing solutions [1] - DDN provides online upgrades and enhanced visibility into workload and potential issues at the cluster level, extending beyond storage to include network and GPU monitoring [2] - DDN's systems are fully balanced, ensuring that performance scales linearly with added units, aggregating performance and access as the system expands [2] Customer Success & Partnerships - Jump Trading, a high-frequency trading firm, deployed half an exabyte of DDN's platform after switching from competitive technologies [2] - DDN is partnering with Nvidia cloud providers (NCPs) to deliver AI in the cloud as a private cloud solution, offering control over data and latency [2] - Scaleway, an NCP, has found that DDN maintains consistent performance at scale, without issues related to metadata or object size limitations [2] Addressing Industry Trends - The industry is experiencing an accelerated pace of technology change, with new chips emerging every six months to a year, requiring faster time to data, resolution, and production [1] - The scale of deployments is increasing rapidly, with discussions now commonly involving 100,000 to 500,000 GPUs, requiring infrastructure that can handle this scale [1] - Customers demand rapid deployment, expecting systems to be up and running within 60 days or less, emphasizing the need for quick time to results [1]

Core Message - AI is essentially HPC (High Performance Computing), emphasizing the importance of data in both fields for extracting intelligence and value [1] - DDN (DataDirect Networks) provides the "rocket fuel" or data intelligence infrastructure that enables better, faster, and more accurate insights from massive datasets in real-time [1] - Data intelligence is critical for AI transformation, enablement, and acceleration, requiring the unification, curation, and analysis of distributed data from various sources [1] Challenges and Solutions - Current challenges hindering AI acceleration include GPU scarcity, limited data center space, and insufficient power; a data intelligence framework is needed to alleviate these issues [2] - DDN's solutions focus on delivering more capabilities from existing GPUs, shrinking data center footprint, and lowering power consumption [2] - DDN accelerates data ingestion, freeing up GPU cycles, and optimizing networks to reduce time to insight and enhance value [2] DDN's Technology and Positioning - DDN is the only data intelligence platform deployed internally at NVIDIA, and also supports massive deployments like XAI with over 100,000 GPUs [1] - DDN's new technology, Infinia, is a high-performance, multi-tenancy data intelligence platform that supports multiple protocols and minimizes data movement [2] - DDN's solutions maximize the value from infrastructures deployed at scale in data centers and the cloud, benefiting both HPC and AI applications [3] Market Impact and Growth - DDN powers more than half a million GPUs and has deployments at the exobyte level, demonstrating significant growth and scale [3] - DDN's ability to solve challenges at massive scale translates to bulletproof stability and cost-effectiveness across a broad range of installations [3] - DDN aims to accelerate scientific and business outcomes by handling data at the edge, in data centers, and in the cloud [3]

Core Functionality - Exoscaler data intelligence platform supports multi-tenancy by leveraging VLANs and secure data partitions [1][2] - Client access controls prevent unauthorized data access, enhancing security [2] - Capacity management controls via quotas allow flexible space allocation to tenants [2] Technical Implementation - Network configuration utilizes VLANs with paired IP addresses for intracluster networking and tenant connections [3] - Each tenant maps to two IPs for multiple connections to each VLAN, ensuring high availability [3] - Multi-tenancy is enabled via EMF settings and synced across the cluster [4] - Clients without registered IPs on the appropriate VLAN lose system access due to VLAN isolation [5] Quota Management - Hard quotas enforce strict limits, preventing tenants from exceeding allocated capacity, ensuring total capacity of all tenants never exceed the cluster's capacity [7][9] - Soft quotas allow tenants to use shared capacity by overallocating quotas, potentially leading to less waste but requiring trust [7][10] - Hybrid approach combines soft and hard quotas, providing leeway while preventing excessive consumption of free space [11][12] Data Handling - The system supports on-the-fly quota adjustments while serving data to clients [9] - Demonstrated the creation of a 10 TB (Terabyte) test file to illustrate quota enforcement [8]