Oracle is pushing all-in with the data center market, promising to spend $100+ billion over the next 4 years, with NVIDIA to get 40% of that $100B as it’s the global leader in AI GPUs.
Right now, Oracle has 162 cloud data centers in operation and under construction across the world, explains Oracle chairman and CTO, Larry Ellison. He said: “the largest of these datacenters is 800 megawatts and will contain acres of NVIDIA GPU Clusters for training large scale AI models“.
He continued, adding: “Oracle could operate up to 2000 data centers in the future, a significant increase from the 162 currently in operation“. It’s not just this news, but the Oracle CEO added: “So we’re in the middle of designing a data center that’s north of the gigawatt that has — but we found the location and the power place we look at it, they’ve already got building permits for 3 nuclear reactors. These are the small modular nuclear reactors to power the data center. This is how crazy it’s getting. This is what’s going on”.
Yes, you read that right — building permits for not one, but 3 nuclear reactors… they’re small modular nuclear reactors, making me wonder if we’ll see Rolls Royce’s new Micro-Reactors, new zero-emission power using advanced nuclear technology. You can read more about those in the link below:
Oracle CEO Larry Elison talked about AI market growth, where he said: “I mean these AI models, these frontier models are going to — the entry price for a real frontier model from someone who wants to compete in that area is about $100 billion. Let me repeat, around $100 billion. That’s over the next 4, 5 years for anyone who wants to play in that game. That’s a lot of money. And it doesn’t get easier. So there are not going to be a lot of those. I mean we — this is not the place the list who can actually build one of these frontier models“.
“But in addition to that, there are going to be a lot of very, very specialized models. I can tell you things that I’m personally involved in, which are using computers to look at, biopsies of slides or CAT scans to discover cancer. Also, there are also blood tests were for discovery and cancer. Those tend to be very specialized models. Those tend not necessarily use the foundational the rocks and the ChatGPTs, and the Gemini, they tend to be highly specialized models. Trained on image recognition on certain data, I mean, literally millions of biopsy slides, for example, and not much other training data is helpful“.
He added: “So that goes on, and we’ll see more and more applications look at that. So I wouldn’t — if your horizon is over the next 5 years, maybe even the next 10 years, I wouldn’t worry about, hey, we’ve now trained all the models we need and all we need to do is inferencing. I think this is an ongoing battle for technical supremacy that will be fought by a handful of companies and maybe one nation state over the next 5 years at least, but probably more like 10. So this business is just growing larger and larger and larger. There’s no slowdown or shift coming“.