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]

AI Infrastructure & Performance - Slow storage significantly hinders AI training and inference speeds, impacting GPU utilization [2] - Fast storage is crucial for various AI applications and other accelerated workloads [3] - Spectrum X technology, initially designed for GPU-to-GPU communication, is now being adapted to accelerate storage traffic [4][5] - Spectrum X improves GPU storage bandwidth by approximately 50% and enhances performance in noisy environments [6] Technical Innovations & Solutions - Traditional Ethernet struggles with large data flows ("elephant flows") due to flow-by-flow load balancing, leading to ECMP collisions [7][8] - Spectrum X employs packet-by-packet load balancing to achieve optimal fabric utilization, requiring a full-stack solution with technology in storage appliances, GPU servers, and switches to handle out-of-order packets [8][9] - Spectrum X addresses incast congestion issues arising from multiple GPUs sending data to storage or vice versa [10][11] - The technology mitigates performance degradation caused by link failures in large-scale deployments [12][13][14] Testing & Validation - Nvidia uses its supercomputer, Israel 1, as a proving ground for Spectrum X development and testing, including storage applications [18][19] - Tests on Israel 1, involving 300 GPUs across four scalable units, demonstrated that Spectrum X accelerates write performance by nearly 50% compared to Rocky [20][21][23] - DDN validated Spectrum X with their full stack, publishing a white paper and technical blog on the results [24] Visibility & Management - Spectrum X provides enhanced visibility into the entire fabric, enabling partners like DDN to monitor and predict potential issues using APIs [17]

AI Data Platform Performance - DDN boosts Nvidia's GPU performance, achieving 10x faster AI pipelines [1] - DDN's AI data platform is deployed across over 500,000 GPUs in data centers and clouds globally [2] - DDN optimizes data movement and accelerates checkpoint/model load operations, freeing up GPU resources and reducing training times [4] ROI and Business Value - DDN enhances Nvidia investments, leading to significantly better ROI [3] - DDN boosts ROI by a factor of 30x, translating into meaningful revenue gains for customers [4] - DDN delivers highest performance data management outcome [1] AI Application and Deployment - DDN enables AI enterprise with safe and rapid deployment, without disrupting existing workflows and infrastructures [3] - DDN supports AI agents and large language models with hundreds of billions or trillions of data points [1] - DDN's AI data platform makes next-generation optimized AI a reality [1]

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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]