training a model to the journey now of us uh uh taking advantage of these incredible frontier models and AI models and turning them to AI applications that are for inference and and solving solving large problems. One of the most important things people forgot uh is the importance of uh data that is necessary uh during application not just during training and so so of course you want to train on a vast amount of of data for pre-training um but during use the AI has to access information and uh AI would like ...

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]

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]

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]