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AI workloads are creating unprecedented demand for high-performance compute. We're working directly with customers to scale production and meet this demand. Lip-Bu Tan and Jeetu Patel shared insights from the front lines at #CiscoAISummit - including how Moore's Law timeline has compressed from years to months.

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The biggest driver of AI compute now is real-world deployment. At NVIDIA GTC, IREN's Kent Draper, alongside Lenovo's Vlad Rozanovich, shared what they’re actually seeing on the ground from AI infrastructure demand, and the signal is coming from enterprise use. He pointed to agents, internal enterprise models, and real-world deployments as the workloads now driving demand, and explained that the challenge isn't finding customers, it’s how quickly they can build capacity to keep up. Their strategy? A vertically integrated model focused entirely on power-dense computing, which allows them to scale faster, control the environment, and deliver infrastructure purpose-built for AI rather than multi-tenant data centers trying to support dozens of different workloads.
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The biggest driver of AI compute now is real-world deployment. At NVIDIA GTC, IREN's Kent Draper, alongside Lenovo's Vlad Rozanovich, shared what they’re actually seeing on the ground from AI infrastructure demand, and the signal is coming from enterprise use. He pointed to agents, internal enterprise models, and real-world deployments as the workloads now driving demand, and explained that the challenge isn't finding customers, it’s how quickly they can build capacity to keep up. Their strategy? A vertically integrated model focused entirely on power-dense computing, which allows them to scale faster, control the environment, and deliver infrastructure purpose-built for AI rather than multi-tenant data centers trying to support dozens of different workloads.

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Our approach to AI infra is simple: build the most fungible and flexible fleet to meet the real world's needs across inference and training as Scott Guthrie shared with Alex Kantrowitz. And we are already doing it at scale today, as we power the biggest AI workloads like Copilot and ChatGPT, APIs that power 3P products & enterprise workloads and high scale training.
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The future of compute isn't just about CPUs. Lip-Bu Tan talks top GPU talent to embracing RISC-V and Arm alongside x86 at #CiscoAISummit. Tune in below. 👇
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Production AI workloads demand infrastructure that is governable, efficient, and easy to deploy. Hear how Uniphore is using AMD Instinct MI350P PCIe cards to help deliver secure, enterprise-grade conversational and generative AI for more than 2,500 customers. Watch the full video on YouTube:
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Production AI workloads demand infrastructure that is governable, efficient, and easy to deploy. Hear how Uniphore is using AMD Instinct MI350P PCIe cards to help deliver secure, enterprise-grade conversational and generative AI for more than 2,500 customers. Watch the full video on YouTube:

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At #Yotta25 in Las Vegas, $IREN Co-Founder & Co-CEO Daniel Roberts joined E3 Platforms Co-Founder & CCO Rhea Williams to discuss the accelerating demand for high performance compute and how $IREN's vertically integrated model delivers speed, scale and cost efficiency.

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Selling GPUs won’t scale your margins. Real monetization begins when you evolve from raw compute to AI services and eventually to AI factory platforms. The ultimate goal is simple: stop selling capacity and start delivering outcomes with margins to match. Watch the on-demand webinar to learn how leading teams are making that shift. #AIFactory #NeoCloud #GPU #AIInfrastructure #k0rdentAI

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Best technology M&A of all time has to be NVIDIA's $6.9 Billion acquisition of Mellanox in 2020. It was a special treat to interview Michael Kagan, CTO of NVIDIA and co-founder/CTO of Mellanox, for today's Training Data episode. Michael has been driving forward the Compute Frontier for more than 40 years now, first as Chief Architect at Intel in the 90s, then Co-Founder and CTO at Mellanox, and for the last 5 years CTO at NVIDIA. There's nobody better positioned than Michael to share the complete history of the compute frontier and what's ahead, from decades pushing forward Moore's law (squeezing more transistors on a chip) to the last decade of work scaling beyond single chip physics limitations (scaling out to 100K+ GPU clusters). Interconnect is the secret sauce enabling compute to scale beyond chip-level Moore's law. Connecting a fabric of 100K+ GPUs to function as a single unit of compute is the enabling technology for today's intelligence explosion. But other things break at the 100K+ GPU cluster scale: individual chips inevitably fail, power and networking become more complex, etc etc. Net effect of scale out: we've inflected the silicon frontier from Moore's Law (2x every 2 years) to Huang's law (~10x a year). Very excited about today's episode! Learned so much from Michael with Pat Grady

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We’re designing our own chips — taking what we’ve learned from building frontier models and bringing it directly into the hardware. Building our own hardware, in addition to our other partnerships, will help all of us meet the world’s growing demand for AI. In Episode 8 of the OpenAI Podcast, Sam Altman and Greg Brockman join Broadcom’s Hock Tan and Charlie Kawwas with host Andrew Mayne to discuss how OpenAI-designed chips will power the world’s growing AI demand.
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