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Jensen Huang & Alex Bouzari: Supercharging the Application Layer for the AI Economy
DDN· 2025-08-05 17:02
Industry Transformation & Vision - The industry is undergoing a profound and global transformation, impacting work, leisure, health, security, and safety [1] - This transformation represents a pivot of the global economy [1] Enterprise Adoption & ROI - Enterprises need to adopt new technologies at an accelerated pace for the ROI to be effective [2] - Efficient infrastructure in data centers or clouds is crucial for ROI [2] Application Layer Acceleration - Supercharging the application layer is a significant enabler and accelerator [2]
What does the future of AI infrastructure look like?
DDN· 2025-08-04 21:19
AI Infrastructure Focus - DDN is focused on helping organizations across all industries benefit from AI [2] - DDN's technology is designed to address AI requirements at any scale [3] Technological Advancement - DDN aims to make AI-enabled data centers more efficient, reliable, and cost-effective [2] - DDN targets a 10x improvement in performance and efficiency, while reducing power requirements and footprint by 10x [2] Market Position - DDN powers more than 500,000 GPUs across all industries globally [3] - DDN is solving problems inside the data center and cloud, and above the stack [3] Industry Application - DDN is providing solutions for autonomous driving, manufacturing, and the energy sector [4]
DDN Infinia: Next-Gen Object Store for AI, Multi-Tenancy & Data Workflows
DDN· 2025-08-04 18:00
Product Overview - Infinia is a ground-up developed product intended as more than just an object store, offering multiple data interfaces [1] - The system provides database access through an SQL interface and can be used as a notification bus, critical for AI workflows [2] - It is a pure software product designed to solve complicated problems, making it attractive for service providers [3] Key Features & Capabilities - Built-in architectural capabilities around multi-tenancy provide technology as a service [4] - Deep integration capabilities through an SDK enable customers to accelerate applications and AI workflows [4] Ecosystem & Partnerships - DDN has ongoing partner development with ecosystem partners to accelerate technology adoption through the Infinia SDK [4]
Jensen Huang on Accelerated Computing: Beyond Moore’s Law to AI Breakthroughs
DDN· 2025-08-04 15:42
Computing Acceleration - Moore's Law 的放缓促使行业寻求新的加速计算方法 [2] - 公司通过算法重构和并行处理,实现了计算加速,提高了成本和能源效率 [3] - 这种加速使得在计算领域进行机器学习和人工智能成为可能 [4] Technological Innovation - 公司致力于通过 CUDA 来增强应用层 [1] - 公司通过极端的计算方式,让计算机能够自主发现洞察 [4]
DDN Infinia Performance Demo in Oracle Cloud | High-Speed S3 Object Storage Benchmark
DDN· 2025-08-01 20:49
Overview - DDN Infinia 在 Oracle Cloud Infrastructure (OCI) 上的性能展示,但强调这仅为技术预览,未来可能发生变化 [1] - Infinia 架构提供广泛的数据管理能力,包括多种数据 IO 路径、核心存储组件(如 scale-out KV 存储、always-on 加密和数据缩减)、原生多租户等 [2] - Infinia 完全软件定义和容器化,可在物理或虚拟化硬件上运行,适用于云部署 [2] Technical Details & Performance - 在 OCI 内部测试使用了 6 个 BM dense ioe5 计算实例作为 Infinia 集群的主机,以及 6 个 BM standard E5.192% 实例作为客户端,客户端实例具有单个 100 GB 连接 [2] - 在 dense ioe5 实例中,Infinia 软件仅使用了 128 个可用核心中的 32 个 [2] - 使用 warp 在分布式基准测试模式下进行 IO 生成,确保每个客户端并发地向所有 Infinia 集群节点发送操作,并在所有客户端和所有 Infinia 集群节点之间创建完整的 IO 网格 [3] - Put 操作的吞吐量约为 28 GB/s 到 30 GB/s,每个客户端和每个 Infinia 节点平均处理速度约为 4800 MB/s (约 5 GB/s) [5] - Get 操作的吞吐量约为 35 GB/s 到 37.5 GB/s,负载均匀分布在所有客户端和 Infinia 节点上,约为 6100 MB/s (约 6.5 GB/s) [6] - 实现了 5 毫秒的 time to first byte,对于 S3 对象 IO 来说非常出色 [6] Conclusion - 软件定义的 Infinia 不仅可以在云中的 Oracle 计算基础设施上运行,而且即使是小型六节点集群也能实现出色的性能 [7]
Jensen Huang on Why Data Intelligence is the Future of AI
DDN· 2025-07-31 16:13
AI应用与数据 - AI应用正从训练模型转向利用前沿模型解决大型问题[1] - 应用阶段的数据重要性被低估,AI需要访问信息而非原始数据[1] - 行业正在将对象和原始数据的存储重构为数据智能[1] 存储与计算 - 数据智能为全球企业提供AI运行所需的信息结构[1] - 这代表着计算和存储关系的非凡重构[1]
The Origin of DDN Infinina
DDN· 2025-07-28 19:08
AI Infrastructure Vision - Infinia initiated a new product development in 2017, driven by the need for a different architecture for AI, diverging from existing solutions [1] - The company aimed for an architecture that scales efficiently for training and offers very low latency [1] - Infinia envisioned a distributed, on-premise, and multi-cloud solution where data payloads (images, video) do not move due to cost, relying instead on metadata and tagging [2] Challenges and Innovation - The initial architectural concept for AI was met with skepticism, deemed impossible by some [2] - Infinia adopted a problem-solving approach that disregarded past constraints like file systems [3] - The development process took seven years [3]
Empowering Indonesia with AI: Indosat Ooredoo Hutchison & DDN’s Vision for a Sovereign Future
DDN· 2025-07-25 15:13
Company Overview - Indosat Ooredoo Hutchison (Indosat) was the first brand to connect Indonesia to the world 57 years ago [1] - Indosat's mission is to help Indonesia with early AI adaptation and democratization [2] AI Initiatives and Results - Indosat has an AI factory that is up and running, showing good early trends [2] - AI is being applied in banking financial services, oil and gas, agriculture, and healthcare [2][3] - AI empowers doctors to improve diagnosis in a country with doctor shortages [3] - AI plays a significant role in telco operators' capital expenditure and network planning [3] - AI helps Indosat achieve more with less capital expenditure [4] Strategic Partnerships and Data Sovereignty - Indosat is committed to building sovereign capability with partners like Nvidia and DDN [5] - Countries are aiming for 40% to 70% sovereign workload, emphasizing data localization [5] - Data within the country must comply with local laws and regulations [5] - A country's intelligence and data are considered strategic natural resources [5]
Ask the Experts Multi Tenancy Final
DDN· 2025-07-25 10:19
AI Infrastructure Challenges - AI workloads (inference, training, RAG) competing for resources can cause performance bottlenecks and delays [1] - Mixed-tenant AI loads can lead to noisy-neighbor issues, impacting performance [1] Solutions & Benefits - Next-gen AI infrastructure provides full control over the environment, regardless of workload complexity [1] - Dynamic resource isolation prevents noisy-neighbor issues [1] - Efficient scaling of AI infrastructure while maintaining performance is achievable [1] Key Learning Objectives - Guarantee performance under heavy, mixed-tenant AI loads [1] - Prevent noisy-neighbor issues with dynamic resource isolation [1] - Scale AI infrastructure efficiently while maintaining performance [1]
Ask the Experts: Mastering AI Cloud
DDN· 2025-07-24 14:49
AI Infrastructure Challenges - Scaling AI deployments faces data challenges, not just compute limitations [1] DDN Infinia Solutions - DDN Infinia guarantees consistent performance under heavy AI loads [1] - DDN Infinia prevents tenant interference and enforces intelligent QoS (Quality of Service) [1] - DDN Infinia provides full visibility and control across dynamic, multi-tenant environments [1] Session Highlights - The session includes a live demo of DDN Infinia's capabilities [1] - A live expert Q&A session is available for attendees [1]