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]

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]

DDN's AI Infrastructure Strategy - DDN is scaling AI infrastructure to meet growing demands [1] - DDN is transforming storage into a data intelligence platform [1] - DDN is fueling national AI strategies, indicating a focus on sovereign AI and national data control [1] AI Applications and Use Cases - DDN is powering various applications, including digital twins and robotics [1] - Real-world AI use cases span finance, healthcare, autonomous vehicles, and defense [1] Company Focus and Vision - DDN, a billion-dollar company, remains mission-focused despite industry hype [1] - The discussion extends beyond AI to the underlying infrastructure driving global transformation [1]

AI Infrastructure & Sovereign Intelligence - DDN's President discusses the rapid rise of AI infrastructure and sovereign intelligence [1] - Sovereign AI is becoming mission-critical [1] - France and the global tech ecosystem are racing toward a future powered by a million GPUs [1] - Data intelligence is the true currency of innovation [1] DDN's Capabilities & Performance - DDN powers NVIDIA's most advanced AI systems [1] - DDN's Infinia demonstrates game-changing performance vs AWS in RAG workloads [1] AI Applications & Impact - AI has real-world impact across finance, healthcare, defense, and energy [1] - Building an AI factory is worth billions [1] Future Vision - A vision for the future where humans and machines shape intelligence together [1]

Company Overview & HPC Leadership - Evident, a spin-off of ATOS, specializes in high-performance computing (HPC) solutions, including mainframes, processors, and high-speed networks [2] - The company positions itself as the largest HPC provider in Europe and a significant provider in Latin America and India [2][3] - Evident is a partner with DDN, utilizing DDN as its back end for most of its delivered systems [2] HPC & AI Integration - Evident is bringing HPC to AI, exemplified by a cluster of approximately 40 DGX units implemented in Ecuador for computer vision, processing 5 gigabytes per second of video stream from over 18,000 cameras [4][5] - The Ecuador system utilizes a 31 petabyte Luster file system and a 21 petabyte system based on S400 and X2, chosen for its fast failover and recovery capabilities [6] Storage Challenges & Solutions - The presentation highlights challenges related to storage, including inefficient small file storage (7KB) leading to performance issues [7][8] - Another challenge involved seismic image processing requiring 900 gigabytes of data reads and writes, which was addressed using local caching [9][10] Large Language Models (LLMs) & System Failures - Meta's model training, involving an 800 gigabyte model and a cluster of 25,000 GPUs (using only 16 initially), faced frequent failures (419 failures in 45 days, approximately one failure every 3 hours) [11][12] - Checkpointing is deemed a necessity due to the high failure rate of components (GPUs, interconnects, power supplies, software bugs) in large-scale systems [13][14] Scalable & Efficient HPC Systems - Evident is building its first exascale system based on its technology, aiming for high efficiency, with one model ranking number one and another in position six on the Green500 list [15] - The company is delivering approximately five exascale systems in Brazil, utilizing DDN storage solutions focused on IOPS, bandwidth, and space [16][17] Key Takeaways for Large Workloads - Large workloads like LLMs require large systems, which are prone to component failures [17][18] - Mitigating failures requires a robust design in network and storage, compatible with the number of GPUs, and the use of checkpointing [18][19]

Service Launch - Google 和 DDN 联合直播，介绍 Google Cloud Managed Lustre 服务的发布 [1] - 直播将深入探讨 Lustre 背后的技术和托管服务 [1]

Collaboration and Technology - Chineka collaborates with DDN for almost 10 years to support large-scale systems like Leonardo and Galileo 100 [1][2] - DDN's platform enables Chineka to process 30% more data, crucial for civil protection scenarios like geological hazards [2] - DDN is synonymous with performance for AI workloads, handling massive data ingestion and processing [5] Project Outcomes and Impact - Projects improve human research in areas from weather prediction to artificial intelligence, nuclear fusion, and astrophysics [1] - Digitalization projects preserve Italian and European history by creating digital twins of historical sites [3] - Data simulation time reduced from 40 minutes to 5 minutes, significantly improving efficiency [4] - Leonardo supported research to find medicine against COVID-19, contributing to the development of a treatment [4] Future Strategy - Chineka is transitioning between High-Performance Computing (HPC) and Artificial Intelligence (AI) ecosystems [5] - Chineka's mandate is to support both HPC and AI ecosystems, requiring systems capable of managing petabyte and exabyte-level data [5] - DDN technology enables Chineka to manage petabyte-scale data now and exabyte-scale data in the future [5]

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] - Initial AI focus is on banking and financial services, with significant value seen in call center agentic applications to empower people and improve productivity [2] - Positive results are being observed in oil and gas, agriculture, and healthcare, where AI can empower doctors with better diagnoses [3] - AI plays a significant role in Indosat's capital expenditure (capex) and network planning, enabling the company to achieve more with less [3][4] Strategic Importance of Data and AI - Indosat is committed to building sovereign capability, requiring partnerships with companies like Nvidia and DDN [5] - Countries should ensure that 40-70% of workload is sovereign, with data residing within the country and compliant with local laws and regulations [5] - Data and intelligence are considered as strategic as any other natural resource for a country [5] Technology and Partnerships - DDN helps Indosat store intelligence in a scalable manner, achieving a competitive Total Cost of Ownership (TCO) and serving customer needs [4]

AI Infrastructure & Market Focus - Singal's mission is to empower every generation with products ranging from telecommunication to ICT, focusing on AI solutions for operational efficiency and new revenue streams [1] - The company is expanding its value proposition into AI infrastructure, building sovereign GPU clouds for mission-critical applications in both public and private sectors [4] - Top verticals include research (model training for R&D), public sector (nationwide AI-enabled citizen services and public safety), and fintech (fraud and scam prevention) [4][5] Challenges in AI Adoption - A significant challenge is the lack of AI-ready talent, prompting the need for no-code/low-code platforms to empower domain experts [2] - Enterprises face challenges related to AI-ready infrastructure, particularly concerns about data sovereignty and data leaving the country [3] Strategic Partnerships & Solutions - The company partners with providers like Nvidia and DDN to offer a unified platform, leveraging DDN as the data intelligence platform within the AI infrastructure stack [5] - The company provides turnkey digital infrastructure, from data centers to cloud infrastructure, enabling seamless workload hosting for enterprises [5]

Industry Trend - The shift from High-Performance Computing (HPC) to Artificial Intelligence (AI) is redefining infrastructure [1] - Understanding how AI workloads differ from HPC is crucial for infrastructure planning [1] DDN's Offerings - DDN is hosting a roundtable to discuss supporting next-generation AI workloads [1] - DDN offers strategies for scaling across hybrid and converged environments [1] - DDN provides lessons from the field and live Q&A with DDN experts [1] Resources - DDN encourages engagement through various platforms including LinkedIn, X (formerly Twitter), and Instagram [1] - DDN provides access to resources such as their website, product information, customer use cases, and blog [1] - DDN offers a contact option to connect with a storage specialist [1]