NUS120 Distinguished Speaker Series | Dr William Dally
ABOUT NUS120* The National University of Singapore celebrates its 120th anniversary in 2025, commemorating a legacy, forged ...
Chief Scientist & Senior Vice President of Research, Nvidia
Search every verified William Dally interview, podcast appearance, and on-the-record quote — each transcript cross-checked by AI and human review to confirm speaker identity. William Dally, Chief Scientist and Senior Vice President of Research at Nvidia, gave a keynote at GTC Taipei in May 2026 where he discussed the economics of AI factories, stating that tokens "are now profitable units of revenues" and that compute demand in Taiwan has "skyrocketed" as a result. He estimated the cost of a single gigawatt-level AI factory has risen from $30 billion to between $60 and $100 billion, and argued that "compute is revenues" and "performance per watt is your revenues," cautioning against choosing architecture solely on chip cost. Dally also said the number of software engineers is increasing, describing claims that AI reduces jobs as "complete nonsense," citing the productivity gain of $3 trillion worth of software engineer salary generating $9 trillion in output. In a June 2026 lecture at the National University of Singapore, Dally attributed the deep learning revolution to GPU hardware enabling algorithms and data that had existed for decades, and stated that progress remains "gated by how fast a GPU is." He contrasted Nvidia's product development, where "it has to work or we're going out of business," with Nvidia Research, where the ability to fail allows for innovations that can achieve "2x or 4x performance per unit energy on the next generation." He also referenced an earlier 2020 talk where he described a 317x increase in single-chip inference performance over eight years, a trend he termed "Huang's Law," and credited specialized tensor core instructions for allowing GPUs to achieve efficiency near that of dedicated hardware.
ABOUT NUS120* The National University of Singapore celebrates its 120th anniversary in 2025, commemorating a legacy, forged ...
Dr. Bill Dally is the Chief Scientist and Senior Vice President of Research at Nvidia, and a Professor of Computer Science at Stanford University. Dr. Dally has had a storied career with contributions to parallel computer architectures, interconnection networks, GPUs, accelerators and more. He has a history of designing innovative and experimental computing systems such as the MARS accelerator, the MOSSIM simulation engine, the J-Machine and M-machine, to name a few. He talks to us about computing innovation in the post-Moore era, domain-specific accelerators, and technology transfer in comput…
In this GTC China keynote, NVIDIA’s Chief Scientist Bill Dally reveals how accelerated hardware and software are solving the world’s most demanding computing problems. Discover the power of the Ampere A100—the world’s largest 7nm chip with 54 billion transistors—and its ability to exploit structured sparsity for a massive jump in deep learning performance. Key topics covered in this presentation: Huang’s Law: Learn about the staggering 317x increase in single-chip inference performance achieved over the last eight years. Energy Efficiency: Why the Selene supercomputer ranked #1 on the Green 50…
In this 60-minute wide-ranging discussion, NVIDIA Chief Scientist and GPU architect Bill Dally engages in a focused dialogue with ...
Biography: Dr. Bill Dally is Chief Scientist and Senior Vice President of Research at NVIDIA Corporation and an Adjunct Professor and former chair of Computer Science at Stanford University. Bill is currently working on developing hardware and software to accelerate demanding applications including machine learning, bioinformatics, and logical inference. He has a history of designing innovative and efficient experimental computing systems. While at Bell Labs Bill contributed to the BELLMAC32 microprocessor and designed the MARS hardware accelerator. At Caltech he designed the MOSSIM Simulation…
The Lectio Magistralis is the flagship event of the annual programme of the CSP-IAS – Institute for Advanced Study, the joint initiative promoted by the Italian Institute of Artificial Intelligence (AI4I) and the Compagnia di San Paolo Foundation to foster international dialogue among science, industry, and culture on artificial intelligence. Each year, the Lectio brings to Turin a leading global figure in research and innovation, offering a space for reflection on the questions and frontiers that define the evolution of AI.
... shows the evolution of of Nvidia hardware for running deep learning over um basically the last 12 years um and Blackwell is up ...
In this talk, Bill Dally, NVIDIA Chief Scientist and Senior Vice President of Research, discusses NVIDIA’s recent progress on deep learning hardware and reviews the origin of this technology with DARPA and DoE funded projects at MIT and Stanford. This supercut highlights the government-funded academic research that was critical both in developing key technologies and training the people needed to take these technologies to product.
The explosion of generative AI-powered technologies has forever changed the tech landscape. But the path to the current AI ...
Bill Dally , Chief Scientist and Senior Vice President of Research at NVIDIA gives an ECE Distinguished Lecture on April 10, 2024 at Georgia Tech. Abstract: “Directions in Deep Learning Hardware” The current resurgence of artificial intelligence, including generative AI like ChatGPT, is due to advances in deep learning. Systems based on deep learning now exceed human capability in speech recognition, object classification, and playing games like Go. Deep learning has been enabled by powerful, efficient computing hardware. The algorithms used have been around since the 1980s, but it has only b…
This fireside chat at NVIDIA GTC 2024 features Fei-Fei Li of Stanford and Bill Dally of NVIDIA. Their discussion focuses on the impact of AI on humans, its potential risks, the future of AI in healthcare, the role of AI in scientific discovery, and the importance of public sector involvement in AI research and development. ▫️ Fei-Fei Li is Sequoia Professor of Computer Science, Stanford University, Denning Family Co-Director, Stanford Institute for Human-Centered AI (HAI), Stanford University. She has served in various roles, including Director of Stanford's AI Lab, Vice President at Google…
NVIDIA Chief Scientist Bill Dally joins Daniel Whitenack and Chris Benson for an in-depth conversation about ‘everything AI’ at NVIDIA. As the leader of NVIDIA Research, Bill schools us on GPUs, and then goes on to address everything from AI-enabled robots and self-driving vehicles, to new AI research innovations in algorithm development and model architectures. This episode is so packed with information, you may want to listen to it multiple times. Join the discussion (https://changelog.zulipchat.com/#narr...) Changelog++ (https://changelog.com/++) members support our work, get closer…
“I also kind of disagree with the assertion that we're running out of data. I feel like there's an awful lot of data in the world that is not yet being used to train these models.”
“A lot of people think GPUs just appeared and everything was fine, but GPUs didn't just appear — fundamental work on parallel computing and stream processing laid the groundwork, and those ideas (and the students trained by government programs) were essential to building many American industries.”
“The average productivity improvement at NVIDIA using deep learning is about 3x, both on the hardware and the software side.”
“We do some accounting here ... was really only about 3x across this entire 12‑year period. And you know that really shows that Moore's law is over — we're not getting very much from better process technology.”
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