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]

Core Insights - The discussion highlights the growing importance of Tensor Processing Units (TPUs) in AI and deep learning, emphasizing their efficiency and lower costs compared to GPUs [1][5][6] - Google is recognized for its advancements in AI and chip development, positioning itself as a strong competitor in the market [4][5][6] - The market for AI-related chips is projected to reach a demand of $7 trillion by 2030, indicating significant growth opportunities for companies involved in this sector [8][22][24] Group 1: Technology and Market Dynamics - TPUs are purpose-built for AI applications, offering advantages in training and inference, which makes them more power-efficient than traditional GPUs [1][5] - Google has been developing its chips for five years, leading to a fully vertically integrated system that enhances efficiency and reduces reliance on external suppliers [5][6] - The diversification of chip sources is becoming crucial as companies seek alternatives to Nvidia, with Google and AMD positioned to provide competitive options [10][14] Group 2: Competitive Landscape - Companies like Oracle and Microsoft are expected to explore Google's CPUs as they look to diversify their chipsets [13] - The competition in large language models is intensifying, with Google's Gemini 3 showing strong performance against rivals like ChatGPT [15][16] - Despite Nvidia's current dominance, the demand for various chip types suggests that the market is not a zero-sum game, allowing for multiple players to thrive [8][9][10] Group 3: Future Projections - The anticipated market cap for Nvidia is projected to peak at around $7 trillion, driven by the demand for sovereign AI and physical AI applications [22][23][24] - The ongoing evolution in AI technology and chip development is expected to shape market dynamics significantly over the next few years [20][24]

As you probably know, AI is already redefining the world economy from financial services to healthcare to automotive, energy, manufacturing, public sector. We are really starting to see great new AI applications come and this is all in partnerships with Nvidia, our great partner who has been pushing us on the envelope of innovation. So what we are talking about here is yes we've been here for many years 27 years in fact but the last 10 years has been amazing in 2015 almost 10 years ago we were supercharging ...

Core Insights - VEON Ltd. has entered a strategic cooperation agreement with MeetKai to develop next-generation large language models for local-language AI services across its operating markets [1][2][3] Group 1: Partnership Details - The partnership aims to create nationally governed AI capabilities that are culturally fluent and securely operated within each country's borders [2] - MeetKai's Sovereign AI platform will provide a customizable national AI stack, allowing countries to govern the training, hosting, and deployment of AI models [2][3] - The collaboration will enable VEON to offer AI digital services to over 150 million customers in Kazakhstan, Uzbekistan, Ukraine, Pakistan, and Bangladesh [3] Group 2: AI Service Applications - The new AI services will span various sectors, including education, healthcare, agritech, public services, and enterprise productivity [3] - AI agents similar to QazCode's Aventa will be developed to operate across multiple languages and cater to local community needs [3][4] Group 3: Company Backgrounds - QazCode is recognized for its leadership in developing large language models for under-resourced languages and has previously launched the first Kazakh large language model, KazLLM [5] - MeetKai specializes in post-training optimization and local-language reasoning models, providing a platform for governments and enterprises to build sovereign AI ecosystems [6] - VEON serves nearly 150 million connectivity users and 140 million digital users across five countries, focusing on technology-driven services that promote economic growth [8]

Core Insights - Nvidia exceeded analyst expectations for its third fiscal quarter, generating renewed enthusiasm in a sector that has faced declining investor optimism [1] Company Performance - Nvidia reported strong financial results, which contributed to a positive outlook for the company and the broader industry [1]

Core Insights - Nvidia exceeded analyst expectations for its third fiscal quarter, generating renewed enthusiasm in a sector that has faced declining investor optimism [1] Company Performance - Nvidia reported strong financial results for the third fiscal quarter, which positively impacted market sentiment [1] Market Impact - The strong performance of Nvidia is seen as a catalyst for increased investor interest in the technology sector, particularly in companies related to graphics processing and artificial intelligence [1]

Core Insights - VEON Ltd. has entered a partnership with MeetKai to develop and train language models for AI-based digital products across its five operating markets: Kazakhstan, Uzbekistan, Ukraine, Pakistan, and Bangladesh [1][2][3] Group 1: Partnership Details - The collaboration aims to create regional AI solutions that enhance local language capabilities, support regional dialects, and better understand cultural nuances [2][3] - QazCode will manage data and local computing resources, while MeetKai will focus on training the AI models [3][4] Group 2: Strategic Importance - The partnership is positioned as a means to provide AI benefits to under-resourced languages, with a focus on building localized sovereign AI solutions [3][4] - VEON and QazCode have a history of innovation in AI, with QazCode having developed the first Kazakh large language model, KazLLM, which won the GSMA Foundry Excellence Award in 2025 [4][6] Group 3: Market Impact - The initiative is expected to lead to the launch of various new products, including educational services, healthcare applications, and agritech platforms, all utilizing AI agents capable of operating in local languages [3][4] - VEON serves nearly 150 million connectivity users and 140 million digital users across its markets, emphasizing its role in transforming lives through technology [7][8]

My name is Pre Mela. I am the senior director of international marketing at data direct networks DDN. And here with me is a pioneer of our ages, one of the pioneers of AI infrastructure, Paul Block, who is the president and co-founder of DDN.I'm looking forward to having this conversation with him on all things related to AI infrastructure. Hello Paul, how are you. >> Good Prem, good to be in Dubai.>> So Paul, let's start with the origin story of DDN. Can you tell us about the origin story and can you also ...

So three product announcements today. Ddian core which is combining our Exa and Infinia together in order to provide a unified cohesive platform that lowers the cost per token, lowers the cost and the footprint of power and the data center, accelerates checkpointing and lowers the cost to ingest while keeping the GPU utilization at 99%. That's number one.Number two, AI fasttrack, which accelerates AI adoption. What we've seen over and over from organizations is yes, we want AI. Yes, we see the benefits of A ...