We are Now a Power-Limited Industry, says Jensen Huang
AI has reached a critical juncture, becoming more intelligent and useful due to its reasoning ability. This advancement has led to a significant increase in computational requirements, with the industry needing much more computing power than previously anticipated. The generation of tokens for reasoning is a key factor in this increased demand, according to NVIDIA CEO Jensen Huang, who recently addressed the future of AI and computing infrastructure at the GTC 2025 summit in San Jose earlier this week. His keynote highlighted AI’s rapid evolution and the immense computational power required to support its growth. “Every single data centre in the future will be power-limited. We are now a power-limited industry,” he said. With AI models growing exponentially in complexity and scale, the race is on to build data centres, or what Huang calls “AI factories”, that are not only massively powerful but also energy-efficient. The Rise of the AI Factory Huang introduced the concept of AI factories as the new standard for data centre infrastructure. These centres, which are no longer simply repositories of computation or storage, have a singular focus—to generate the tokens that power AI. He described them as “factories because it has one job, and that is to generate these tokens that are then reconstituted into music, words, videos, research, chemicals, or proteins”. AI factories, according to Huang, are becoming the foundation for future industries. “In the past, we wrote the software, and we ran it on computers. In the future, the computer is going to generate the tokens for the software.” Huang predicts a shift from traditional computing to machine learning-based systems. This transition, combined with AI’s growing demand for infrastructure, is expected to drive “data centre buildouts to a trillion-dollar mark very soon”, he believes. Power Problem is Also a Revenue Problem As data centres expand, they will face significant power limitations. This underscores the need for more efficient technologies, including advanced cooling systems and chip designs, to manage energy consumption effectively. Huang noted that the computational requirements for modern AI, especially reasoning and agentic AI, are “easily a hundred times more than we thought we needed this time last year”. This explosion in demand places enormous strain on data centres’ energy consumption. His keynote made it clear that moving forward, energy efficiency isn’t just a sustainability concern; it will be directly tied to profitability. “Your revenues are power limited. You could figure out what your revenues will be based on the power you have to work with,” he said. This shift will influence everything from how AI models are trained and deployed to how entire industries operate. In this regard, power is the ultimate constraint in AI-dominated computation. This limitation is reshaping both the design and operation of data centres around the world. “The more you buy, the more you make,” Huang quipped, encouraging businesses to view their investments in NVIDIA’s accelerated computing platforms as the key to unlocking the full potential of AI-driven value creation. Scaling Up Before Scaling Out Huang explained NVIDIA’s approach to managing this power limitation, which would be a fundamental rethinking of scale. “Before you scale out, you have to scale up,” he stated. NVIDIA’s new Blackwell platform demonstrates this principle with its extreme scale-up architecture, featuring “the most extreme scale-up the world has ever done”. A single rack delivers an astonishing one-exaflop performance within a fully liquid-cooled, high-density design. By scaling up, data centres can dramatically reduce inefficiencies that occur when spreading workloads across less integrated systems. Huang explained that if data centres had scaled out instead of scaling up, the cost would have been way too much power and energy. He pointed out that, as a result, deep learning would have never happened. Blackwell, a Path to 25x Energy Efficiency With the launch of NVIDIA’s Blackwell architecture, Huang highlighted a leap in performance and efficiency. According to him, the goal is to deliver the most energy-efficient compute architecture you can possibly get. Huang believes NVIDIA has cracked the code for future-ready AI infrastructure by combining innovations in hardware, such as the Grace Blackwell system and NVLink 72 architecture, with softwares like NVIDIA Dynamo, which he described as “the operating system of an AI factory”. Explaining the broader significance, he said, “This is ultimate Moore’s Law. There’s only so much energy we can get into a data centre, so within ISO power, Blackwell is 25 times [better].” AI Factories at Gigawatt Scale NVIDIA’s ambitions don’t stop with Blackwell. Huang outlined a roadmap extending years into the future, with each generation bringing new leaps in scale and efficiency. Upcoming architectures like Vera Rubin and Rubin Ultra promise “900 times scale-up flops” and AI factories at “gigawatt” scales. As these AI factories become the standard for data centre design, they will rely heavily on advancements in silicon photonics, liquid cooling, and modular architectures. Huang likened the current AI revolution to the dawn of the industrial era, naming NVIDIA’s AI factory operating system Dynamo in homage to the first instrument that powered the last industrial revolution. “Dynamo was the first instrument that started the last industrial revolution—the industrial revolution of energy. Water comes in, electricity comes out. [It’s] pretty fantastic,” he said. “Now we’re building AI factories, and this is where it all begins.”
The NVIDIA CEO introduced the concept of ‘AI factories’ as the new standard for data centre infrastructure.
The NVIDIA CEO introduced the concept of ‘AI factories’ as the new standard for data centre infrastructure.
["AI Data Center", "computing", "energy", "GTC Conference", "NVIDIA"]
["AI Data Center", "Go", "API", "energy", "machine learning", "agentic AI", "programming_languages:R", "AI", "innovation", "GTC Conference", "RAG", "deep learning", "computing", "NVIDIA", "R"]
["AI", "machine learning", "deep learning", "agentic AI", "RAG", "R", "Go", "API", "innovation", "programming_languages:R"]
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