Not that we should be at all surprised, but Nvidia leans on AI pretty hard for speeding up how it plans and designs its next generation of GPUs

Not that we should be at all surprised, but Nvidia leans on AI pretty hard for speeding up how it plans and designs its next generation of GPUs | PC Gamer Skip to main content Join The Club – Join our community JOIN NOW 11Premium Benefits 24/7Access Available 28K+Active Members Exclusive Articles Member-only articles Weekly Newsletters Gaming & entertainment news Commenting Join the discussion Member Badges Earn badges as you go Exclusive Competitions Members-only prize draws Early Access See the latest gaming news first GET CLUB ACCESS QUICK For the quickest way to join, simply enter your email below and get access. We will send a confirmation and sign you up to our newsletter to keep you updated on all your gaming news. Contact me with news and offers from other Future brands By submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over. FIND OUT ABOUT OUR MAGAZINE Want to subscribe to the magazine? 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Subscribe to our newsletter Jeff Dean and Bill Dally, Advancing to AI’s next Frontier, Nvidia GTC 2026 – YouTube Watch On For more than 16 years, Nvidia’s annual GTC event has been packed full with talks and presentations about all the things you can do with a GPU beyond rendering 3D graphics. This year’s conference was no different, of course, but hidden amongst all the talk was an insight into how Nvidia goes about designing its chips, and AI is naturally a big part of it all.It was Bill Dally, Nvidia’s chief scientist, who let us in on this behind-the-scenes glimpse, whilst chatting with his equivalent at Google, Jeff Dean, on the topic of ‘advancing to AI’s next frontier’. The first bit that caught my attention (thanks to Bearly AI on X) was how Nvidia uses an AI agent when it switches to a new process node.”Every time we have a new semiconductor process, we have to port our standard cell library to it. It’s about 2,500 – 3,000 cells. And that used to take a team of eight people about 10 months, so 80 person months,” begins Dally.”Then we developed a program based on reinforcement learning called NVCell—I think we’re up to NVCell 2 or 3 now—and it’s overnight on one GPU, and the results are actually better than the human designs… in measures of goodness, of size of the cells, power dissipation, delay. It matches or exceeds the human.”A cell library is basically a big set of pre-designed plans of logic gates, electronic components, and interconnections. Instead of recreating everything from scratch when you want to lay out the texture units in a new GPU, for example, you let software pull everything it needs from this library. However, with each new process node that a wafer manufactu