Strategic Vision - Sovereign AI focuses on promoting local innovation through homegrown and trusted intelligence, grounded in local data, values, culture, and priorities [1] - Orange aims to be the trusted platform in the era of intelligence, extending beyond connectivity to include cloud, cybersecurity, and AI services for enterprises [3][4] - The future of agentic AI must be responsible, trusted, and human-centered [4] GenAI Adoption and Implementation - Orange initiated its GenAI adoption journey early and launched an internal platform called Live Intelligence within ten months of ChatGPT's release [2] - Currently, 80,000 Orange employees are using GenAI daily across various departments, including HR, finance, customer support, operations, and sales [2] - Orange has 14,000 AI assistants available in its library for employees and has begun offering these services to enterprise customers [2] Partnerships and Infrastructure - Telcos are positioned to capitalize on the opportunity to build trusted AI services based on a foundation of infrastructure and services [3] - Orange is expanding its partnership with NVIDIA by joining the NVIDIA Cloud Partner Program to access NVIDIA software and its partner ecosystem [3] Service Expansion - Orange's trusted platform goes beyond connectivity, offering cloud, cybersecurity, and AI services, including sovereign and trusted AI solutions tailored to enterprise customer needs [4]

Well, thank you very much for having me to glad to be able to speak to all of you again. So, I'm Preston Smith from Purdue University and I'm going to talk about how our AI computing investment is is part of the foundation for our institutional strategy at Purdue. So, Purdue right now has four major pillars strategic projects.You can see here a new campus in Indianapolis. If you if you're familiar with Indiana geography, Purdue is between Indianapolis and Chicago about halfway and Indianapolis will be at th ...

Core Insights - Traefik Labs has joined the HPE Unleash AI Partner Program, integrating its Triple Gate security architecture with HPE Private Cloud AI to provide a sovereign AI infrastructure with zero external dependencies [1][3][4] Group 1: Partnership and Technology Integration - The collaboration with HPE and NVIDIA aims to deliver a sovereign AI runtime platform, combining Traefik's AI Gateway, MCP Gateway, and API Gateway for offline prompt filtering and agent governance [2][4] - The HPE Unleash AI Partner Program is designed to create a strategic ecosystem of software providers to deliver production-ready AI solutions [3][6] Group 2: Security Architecture - Traefik's Triple Gate security architecture includes: - Gate 1: AI Gateway for deploying NVIDIA Nemotron Safety models to filter malicious prompts [4] - Gate 2: MCP Gateway for enforcing Task-Based Access Control for AI agents accessing enterprise systems [4] - Gate 3: API Gateway for protecting backend services with centralized credential management and rate limiting [4] Group 3: Key Benefits - The solution allows organizations to deploy complete AI workloads on customer-controlled HPE infrastructure, ensuring compliance with security and governance objectives [6][7] - The platform supports air-gapped environments, enabling true sovereignty without external service dependencies [7] - Governance policies can be defined once and deployed uniformly across various environments, enhancing operational efficiency [7] Group 4: Company Background - Traefik Labs has over 3.4 billion downloads and is trusted by Fortune 500 companies, providing enterprise-grade security for cloud-native applications and AI workloads [8]

Company Strategy & Market Positioning - DDN is doubling down on AI and expanding its global presence, recognizing the importance of markets beyond the US, Europe, and Japan, including the Middle East, South Asia, and the Nordics [2][3][4] - DDN supports multiple GPU vendors like AMD, Intel, and Cerebras, but highlights Nvidia as the current market leader [4][5] - DDN emphasizes the importance of data strategy alongside GPU acquisition, highlighting data as the key differentiator for AI success [9][10][11] - DDN is widening its go-to-market strategy, engaging with resellers, VARs, global system integrators (Accenture, Deloitte, Cognizant, HCL), and AI server/hardware vendors (Super Micro, Lenovo, Eviden, Dell, HP) [19][20] Product & Technology - DDN's Exoscaler platform is designed for large-scale HPC and AI training, supporting multi-model training with thousands of GPUs and offering easy deployment and upgrades [16] - DDN's Infinia technology offers fast object storage and can run on various platforms, including Nvidia's Bluefield DPUs, with a roadmap including scale-out NFS [12][17][18] - DDN now has a T1 offering inside Google Cloud Platform (GCP), allowing users to test Exoscaler directly [21] Financial & Investment - DDN received a $300 million investment from Blackstone, valuing the company at a few billion dollars, demonstrating its long-term commitment [23] Recognition & Partnership - DDN was recognized as the second-ranked company worldwide (first outside of China) for AI and enterprise in a recent Gartner report [24][25] - Nvidia relies on DDN for its supercomputers, highlighting the strong partnership and its expansion into the enterprise world [27]

Core Insights - NVIDIA's strategic investment of $2 billion in Synopsys aims to enhance its competitive edge in chip design, allowing for faster product development and integration of its technology into core design software [5][6][14] - The partnership with Synopsys is expected to significantly reduce chip design simulation times from weeks to hours by leveraging NVIDIA's GPUs [2][10] - NVIDIA's strong financial position, highlighted by record revenue of $57 billion and free cash flow of $22.1 billion, supports its ability to make substantial investments while returning capital to shareholders [8][7] Financial Performance - NVIDIA reported a 62% year-over-year increase in revenue, reaching $57 billion [8] - The company generated $22.1 billion in free cash flow over three months, indicating robust liquidity [8] - NVIDIA returned $37 billion to shareholders through stock buybacks and dividends in the first nine months of the fiscal year [7] Market Position and Competitive Landscape - The investment in Synopsys is seen as a strategic move to maintain NVIDIA's dominance in the semiconductor industry amid rising competition, particularly from companies like Alphabet [4][9] - Analysts have responded positively to the investment, with Morgan Stanley raising its price target for NVIDIA to $250, reflecting confidence in the company's growth potential [4] Technological Advancements - The collaboration with Synopsys is part of NVIDIA's broader strategy to embed its GPUs into the design processes of competitors, ensuring its technology remains integral to chip development [10][14] - The investment supports NVIDIA's expansion into Physical AI, which involves complex simulations for sectors like robotics and autonomous vehicles [12] Future Outlook - NVIDIA is projecting $65 billion in revenue for Q4, driven by demand for its Blackwell chips and the emerging trend of Sovereign AI [13] - The company is positioning itself not just as a hardware vendor but as a foundational player in the semiconductor supply chain, enhancing its role in the AI economy [15]

DDN's Core Focus - DDN positions itself as an enabler and accelerator of data-driven innovation across industries and use cases, akin to Nvidia's role in compute [5] - DDN emphasizes the importance of high-performance data, pivoting from high-performance computing to address the data needs of scientific discovery, business outcomes, and financial outcomes [3] - DDN aims to accelerate value and outcomes by feeding compute with data, irrespective of use case, whether it's in universities, government agencies, or organizations in various sectors [6] Challenges and Solutions - Organizations face challenges such as GPUs sitting idle (40%), power limitations, and fragmented hardware/software in AI data [10][11] - DDN claims to keep GPU utilization high (99% or even 999% in some cases), significantly lowering the cost per token (70% lower) and reducing power/cooling/data center footprint [11][12] - DDN addresses these challenges with solutions like Diate Core, AI Fasttrack, and AI Blueprint, designed to lower costs, accelerate AI adoption, and provide reference architectures for sovereign AI implementations [13][14][15] Product and Technology - Diate Core combines EXA and Infinia to provide a unified platform that lowers costs, accelerates checkpointing, and optimizes GPU utilization [13] - AI Fasttrack aims to simplify AI adoption for organizations seeking to achieve business, scientific, and financial outcomes [14] - DDN's architecture combines HPC scale, enterprise reliability, and AI-native speed [20] Partnerships and Ecosystem - DDN partners with cloud providers like Google (DDN WEP offering), OCI, and Corewave to enable distributed consumption and global enablement [8][9][19] - DDN collaborates with Nvidia, center, and Deloitte to develop and validate sovereign AI blueprints [19] - Nvidia is a partner and customer of DDN, with DDN technology deployed internally by Nvidia [8] Industry Applications - DDN solutions aim to deliver faster simulations and better fraud models for financial services, and faster screening for life sciences [23] - DDN enables AI factories to operate 24/7, ensuring continuous operation without interruption [24][25] - DDN optimizes its platform for specific industries and use cases, recognizing that requirements differ between financial services, academia, life sciences, and autonomous driving [28][29] Sovereign AI Blueprint - DDN is involved in large sovereign AI implementations globally, including in the US, Europe, Asia-Pacific, and the Middle East [15] - The sovereign AI blueprint provides a reference architecture for building successful AI implementations at high speed [15] - Yoda Shakti in India utilizes 8,000 B200 GPUs at 99% utilization with 40% power savings based on DDN's blueprint [19]

Core Insights - Hewlett Packard Enterprise Company (HPE) is developing a comprehensive solution for IT networking and infrastructure following the acquisition of Juniper, enhancing its unified data center and hybrid cloud offerings [1] Group 1: Company Developments - HPE is positioned to create a one-stop-shop solution for IT networking and infrastructure [1] - The acquisition of Juniper is a strategic move to strengthen HPE's market position in unified data center and hybrid cloud services [1] Group 2: Analyst Background - Michael Del Monte is a buy-side equity analyst with expertise across various sectors including technology, energy, and industrials [1]

Group 1 - Hewlett Packard Enterprise Company (HPE) is developing a comprehensive solution for IT networking and infrastructure following the acquisition of Juniper [1] - The integration aims to provide more unified data center and hybrid cloud offerings, positioning HPE favorably in the market [1] Group 2 - Analyst Michael Del Monte specializes in technology, energy, industrials, and materials sectors, with over a decade of experience in professional services [1]

You want me to. Welcome back everyone. I'm John Fer with The Cube.We are live here at supercomputing 2025. I'm here with Dave Volante, my co-host Jackie Magcguire, Savannah P. The whole team is here unpacking the wave of AI infrastructure that continues to accelerate the value uh to the enterprise and to large cloud hyperscalers and neoclouds.A lot of action happening. Alex Bazari here, CEO of DDN is back on the cube. Alex, great to see you as always. as always. Always a real pleasure.You guys uh continuing ...

AI Chip Landscape - Tensor Processing Units (TPUs) are purpose-built for AI and deep learning, offering lower total costs and greater power efficiency compared to GPUs [1] - Google has been developing TPUs for some time, aiming for efficiency and supply chain diversification beyond Nvidia [2][3] - Google's full-stack approach, from chip to application, provides significant efficiencies of scale [5][6] - Diversifying chip base is crucial, as different chips excel in different tasks, similar to diversifying cloud providers [10][11] Market Demand and Competition - The AI market is projected to reach a $7 trillion market cap by 2030, indicating substantial demand [8] - The market demand is large enough to accommodate multiple players, suggesting it's not a zero-sum game between CPU and GPU [8][9] - Hyperscalers not directly competing with Google, pharmaceutical giants, energy companies, and governments are potential adopters of TPUs [13][14] - AMD and Google are positioned to provide alternatives to Nvidia's dominance in the AI chip market [15] Google's AI Capabilities - Gemini 3 is competitive with other leading large language models like ChatGPT, Claude, and Perplexity, excelling in various use cases [16][17] - Sovereign AI and companies building data centers/physical AI will drive market headlines in 2026 [24] Nvidia's Outlook - Models suggest Nvidia has the potential for another $1 trillion in sovereign AI market cap and another $1 trillion in physical AI market cap, potentially peaking around $6.5 to $7 trillion market cap [22][23]