NVIDIA's Extreme Co-Design Fuels AI Infrastructure Revolution - Episode Hero Image

NVIDIA's Extreme Co-Design Fuels AI Infrastructure Revolution

BG2Pod with Brad Gerstner and Bill Gurley · · Listen to Original Episode →
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TL;DR

  • The shift from general-purpose computing to accelerated computing, driven by AI, represents a fundamental change in the world's computing infrastructure, requiring trillions of dollars in refreshes and creating a massive Total Addressable Market (TAM).
  • AI's evolution from one-shot inference to "thinking" AI, involving reasoning and research, is compounding compute requirements exponentially, necessitating continuous innovation in hardware and software to meet demand.
  • NVIDIA's "extreme co-design" approach, optimizing chips, software, and systems annually, creates a significant competitive moat by enabling unprecedented performance gains beyond Moore's Law and locking in supply chain visibility.
  • The AI revolution is poised to augment human intelligence, potentially adding trillions to global GDP by increasing individual productivity, thereby driving demand for AI infrastructure and transforming industries.
  • The global race for AI dominance is viewed as existential by nations, akin to nuclear power in the 1940s, necessitating significant investment in domestic AI infrastructure and talent to ensure national security and economic competitiveness.
  • The US faces a critical challenge in attracting and retaining global AI talent, as policy decisions like high H-1B visa fees risk diminishing the "American Dream" brand and potentially accelerating talent migration to other countries.
  • NVIDIA's competitive advantage lies not just in chips but in building AI infrastructure factories, offering a comprehensive system that provides superior total cost of operation, even if individual components are more expensive.

Deep Dive

NVIDIA's accelerated computing and AI infrastructure are redefining the global technology landscape, creating new multi-trillion dollar hyper-scale companies and driving an unprecedented industrial revolution. This shift from general-purpose computing to AI-driven accelerated computing is not merely an upgrade but a fundamental transformation, necessitating massive infrastructure build-outs and collaborations, such as the partnership with OpenAI, to meet the exponential growth in computational demand.

The core argument for NVIDIA's continued dominance lies in its deep, "extreme co-design" approach, integrating hardware, software, and system architecture annually to drive performance gains far exceeding traditional Moore's Law. This strategy, exemplified by their rapid product release cadence (e.g., Hopper, Blackwell, Rubin), creates a widening competitive moat against specialized ASICs, as the total cost of ownership, including power, data center land, and operational intelligence, makes NVIDIA's integrated systems a superior economic choice even if individual components are cheaper. Furthermore, NVIDIA's role extends beyond chip manufacturing to being an "AI infrastructure partner," enabling the development of AI factories and supporting global build-outs, from hyperscalers to sovereign AI initiatives. This positions NVIDIA not just as a hardware provider but as a foundational enabler of the AI economy, driving demand across energy, manufacturing, and national security sectors.

The implications of this AI-driven transformation are profound and far-reaching. Firstly, it signals the end of general-purpose computing as the primary driver of technological advancement, ushering in an era where accelerated computing is paramount for innovation and economic competitiveness. Secondly, the exponential growth in AI capabilities, particularly in reasoning and complex problem-solving, will augment human intelligence across nearly every industry, potentially leading to a 4x acceleration in global GDP growth as AI-powered co-workers enhance productivity. This necessitates a re-evaluation of economic and social structures, including immigration policies and talent recruitment, to ensure that the benefits of AI are broadly shared and that the US maintains its leadership in technological innovation. The strategic imperative for countries to develop sovereign AI capabilities, coupled with NVIDIA's role in providing the underlying infrastructure, highlights the geopolitical significance of AI. Finally, the integration of AI with robotics and advanced data processing promises to create new industries and fundamentally reshape human interaction with technology, positioning AI as a key driver of future economic prosperity and national security.

Action Items

  • Audit AI infrastructure: Identify 3-5 critical components for resilience and scalability in AI factories (ref: AI infrastructure discussion).
  • Create runbook template: Define 5 required sections (setup, common failures, rollback, monitoring) for AI system deployments to prevent knowledge silos.
  • Analyze compute cost drivers: For 3-5 core AI workloads, quantify the impact of perf-per-watt on total operating cost beyond chip price.
  • Track AI model iteration velocity: Measure the rate of new model versions and their impact on compute demand over a 6-month period.
  • Design system architecture: Evaluate disaggregation strategies for AI workloads, considering CPU, GPU, and specialized accelerators for optimal efficiency.

Related Episodes

Key Quotes

"over 40 of your revenue today is inference but inference is about ready because of chain of reasoning right it's about to go up by a billion times right by by by a million x by a billion x that's right that's the part that most people have you know haven't completely internalized this is that industry we were talking about ben this is the industrial revolution honestly"

Jensen Huang explains that inference, a component of AI processing, is poised for exponential growth due to chain-of-reasoning capabilities. Huang frames this shift as an "industrial revolution," highlighting the profound impact of AI on industries.

"we now have three scaling laws right we have pre training scaling law we have post training scaling law post training is basically like ai practicing yes practicing a skill until it gets it right and so it tries a whole bunch of different ways and and in order to do that you've got to do inference so now training and inference are now integrated in reinforcement learning really complicated and so that's called post training and then the third is inference the old way of doing inference was one shot right but the new way of doing inference which we appreciate is thinking so think before you answer and so now you have three scaling laws the longer you think the better the quality answer you get while you're thinking you do research you go check on some ground truth and you know you learn some things you think some more you go learn some more and then you generate an answer don't just generate right off the bat and so thinking post training pre training we now have three scaling laws not one"

Huang elaborates on the evolution of AI scaling laws, introducing pre-training, post-training (which involves AI practicing and refining skills through inference), and a new model of inference that emphasizes "thinking" before answering. This "thinking" process involves research and learning, leading to better quality outputs and a more complex, integrated approach to AI development.

"i think that open ai is likely going to be the next multi trillion dollar hyper scale company okay i think you and i why do you call it a hyper scale company hyper scale like uh like meta's a hyper scale google's a hyper scale they're going to have consumer and enterprise services and and they are very likely going to be the world's next multi trillion dollar hyper scale company"

Huang expresses his strong conviction that OpenAI is positioned to become the next multi-trillion dollar hyperscale company, drawing parallels to existing hyperscalers like Meta and Google. He defines hyperscale companies as those offering both consumer and enterprise services.

"the first point and this is the laws of physics point this is the most important point that general purpose computing is over and the future is accelerated computing and ai computing that's the first point and so the way to think about that is there's how much how many trillions of dollars of computing infrastructures in the world that has to be refreshed right right and when it gets refreshed it's going to be accelerated computing that's right and so the first thing you have to realize is that general purpose computing and nobody disputes that everybody goes yeah we completely agree with that general purpose computing is over moore's law is dead people say these things and so what does that mean so general purpose computing is going to go to accelerated computing"

Huang asserts that general-purpose computing is obsolete, and the future lies in accelerated computing and AI computing. He explains that as the world's computing infrastructure is refreshed, it will increasingly be replaced by accelerated computing solutions.

"the second thing is that the scale of our customers is so incredible now the scale of our supply chains is incredible who's going to start all of that stuff pre build all of that stuff for a company unless they know that nvidia can deliver through isn't that right and they believe that we can we can deliver through to all of the customers around the world they're willing to start several hundred billion dollars at a time this is the scale is incredible"

Huang emphasizes the immense scale of NVIDIA's operations and customer base, stating that customers are willing to place multi-billion dollar orders for NVIDIA's architecture due to the company's proven ability to deliver. This scale extends to NVIDIA's supply chains, which are also built to handle massive production volumes.

"and so the question then becomes what's in the best interest of china of course is that they have a vibrant industry they also publicly say and rightfully that i believe they believe this is that they want china to be an open market they want to attract foreign investment they want companies to come to china and compete in the marketplace and i believe that they i hope i believe and i hope that would return to that in our context answering your question what do i see in the future i do hope because they say it their leaders say it and i take it at face value and i believe it because i think it makes sense for china that what's in the best interest of china is for foreign companies to invest in china compete in china and for them to also come out of china and participate around the world that is i think a fairly sensible outcome"

Huang expresses his belief that it is in China's best interest to maintain an open market, attract foreign investment, and allow companies to compete within China and globally. He states that he takes Chinese leaders' statements about wanting an open market at face value and hopes for a return to that approach.

Resources

External Resources

Books

  • "The American Dream" - Mentioned as a concept fundamental to the United States' brand and the right to rise.

Articles & Papers

  • "The American Dream" - Mentioned as a concept fundamental to the United States' brand and the right to rise.

People

  • Abraham Lincoln - Quoted regarding the fundamental right to rise as part of the American Dream.
  • Bill Gates - Quoted in 2016 on the future of machines thinking for themselves.
  • Brad Gerstner - Co-host of the BG2Pod podcast.
  • Clark Tang - Partner of Jensen Huang, co-host of the BG2Pod podcast.
  • Dan Shevchuk - Producer of the BG2Pod podcast.
  • David Sachs - Mentioned for his role in Washington D.C. regarding AI and for advocating for accelerated export licenses.
  • Doug Burgum - Mentioned as Secretary at the Department of the Interior.
  • Elon Musk - Mentioned as a builder of AI supercomputers and for his work with Colossus 2.
  • Greg Abbott - Mentioned as a governor who wants to remove regulations to accelerate AI.
  • Jensen Huang - Founder and CEO of NVIDIA, guest on the BG2Pod podcast.
  • Larry Page - Mentioned as having stated in 2005-2006 that Google's end state would be when a machine could predict a question before it's asked.
  • Lutnick - Mentioned as Secretary at the Department of Commerce.
  • Ray Kurzweil - Quoted regarding 20,000 years of progress in the 21st century due to accelerating change.
  • Sam Altman - Mentioned as a leader in AI and for his statements regarding AI revenue forecasts.
  • Satya - Mentioned in relation to Microsoft Azure and for statements about token generation rate.
  • Scott Bessen - Mentioned for stating that world GDP growth is going to accelerate.
  • Sri Ram - Mentioned as someone in Washington D.C. who understands CUDA.
  • Yung Spielberg - Provided music for the BG2Pod podcast.
  • Zack - Mentioned in relation to a direct relationship with NVIDIA.
  • Zuckerberg - Mentioned for stating that Meta may overspend by $10 billion on AI infrastructure.

Organizations & Institutions

  • Alibaba - Mentioned as having a plan to increase data center power by 10x.
  • Amazon - Mentioned as a hyperscaler and for its Tranium ASICs.
  • Anthropic - Recommended for use in sovereign AI capabilities.
  • Apple - Mentioned for its smartphone chip and customer-owned tooling for ASICs.
  • ARM - Mentioned as a competitor in the ASIC landscape.
  • ASIC - Mentioned as a type of chip and in the context of competition with GPUs.
  • Baidu - Mentioned as a company demanding workloads driven by accelerated computing.
  • BG2Pod - The podcast where the discussion took place.
  • Broadcom - Mentioned as a competitor in the ecosystem.
  • Bytedance - Mentioned as a company largely owned by American investors and building recommender engines.
  • Cisco - Mentioned as a canonical case for round-tripping revenues from the last bubble.
  • Commerce - Mentioned in relation to Secretary Lutnick.
  • Coreweave - Mentioned as a partner in building AI infrastructure and as an investment.
  • CPUs - Mentioned as the traditional compute engine for hyperscale computing, shifting to GPUs for AI.
  • DataBricks - Mentioned as a company that primarily uses CPUs.
  • Department of Commerce - Mentioned in relation to Secretary Lutnick.
  • Department of Energy - Mentioned in relation to Secretary Lutnick.
  • Energy - Mentioned in relation to Secretary Lutnick.
  • Equitable - Mentioned as a company that has agreed to add to the accounts of children.
  • Equitable Financial - Mentioned as a company that has agreed to add to the accounts of children.
  • Google - Mentioned as a hyperscaler, for its Gemini AI, and for its TPUs.
  • Grok - Recommended for use in sovereign AI capabilities.
  • H-1B - Mentioned in the context of visa costs and immigration policy.
  • Hyperscalers - Mentioned as companies that are shifting from CPUs to GPUs for AI workloads.
  • Intel - Mentioned as a partner for accelerated computing and for its CPUs.
  • Invest America - Mentioned as an initiative to start every child at birth on a capitalist journey with an investment account.
  • LSI Logic - Mentioned as the original company that invented ASICs.
  • Meta - Mentioned as a hyperscaler, for its AI investments, and for its potential overspending on AI infrastructure.
  • Microsoft - Mentioned as a hyperscaler and partner for Azure build-out.
  • National Football League (NFL) - Mentioned as an example in the context of sports analytics.
  • New England Patriots - Mentioned as an example team for performance analysis.
  • NVIDIA - The company founded by Jensen Huang, central to the discussion.
  • NVIDIA AI Ecosystem - Mentioned in relation to NV Fusion.
  • NVIDIA Ethernet Business - Mentioned as the fastest-growing Ethernet business in the world.
  • NVIDIA GPUs - Mentioned as a core component of NVIDIA's offerings.
  • NVIDIA H-100s - Mentioned in the context of Elon Musk's AI supercomputers.
  • NVIDIA H200s - Mentioned in the context of Elon Musk's AI supercomputers.
  • NVIDIA Hopper - Mentioned as part of the annual release cycle.
  • NVIDIA NV Fusion - Mentioned as a way for competitors to participate in NVIDIA's factory.
  • NVIDIA Spectrum X - Mentioned as part of NVIDIA's networking and switching capabilities.
  • NVIDIA TPU - Mentioned in the context of Google's TPUs.
  • NVIDIA Vera Rubin - Mentioned as part of the annual release cycle.
  • NVIDIA Blackwell - Mentioned as part of the annual release cycle.
  • NVIDIA Ultra - Mentioned as part of the annual release cycle.
  • NVIDIA Feynman - Mentioned as part of the annual release cycle.
  • NVIDIA CPX - Mentioned as a chip for context processing and diffusion video generation.
  • NVIDIA Dynamo - Mentioned as disaggregated AI workload orchestration, open-sourced.
  • NVIDIA AI Infrastructure - Mentioned as NVIDIA's core offering.
  • NVIDIA Networking and Switching - Mentioned as part of NVIDIA's expanded capabilities.
  • NVIDIA Software Stack - Mentioned in relation to co-design.
  • NVIDIA Systems - Mentioned in the context of total operating cost.
  • NVIDIA's AI Factory - Mentioned as a system of chips designed together.
  • NVIDIA's Competitive Moat - Discussed in relation to annual release cycles and co-design.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Ecosystem - Mentioned in relation to NV Fusion.
  • NVIDIA's Networking Business - Mentioned as the fastest-growing Ethernet business.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.
  • NVIDIA's Supply Chain - Mentioned as being geared up for demand.

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