Sam Altman says OpenAI will own 'well over 1 million GPUs' by the end of the year — ChatGPT maker continues to expand rapidly
100,000,000 GPUs.
OpenAI CEO Sam Altman isn’t exactly known for thinking small, but his latest comments push the boundaries of even his usual brand of audacious tech talk. In a new post on X, Altman revealed that OpenAI is on track to bring “well over 1 million GPUs online” by the end of this year. That alone is an astonishing number—consider that Elon Musk’s xAI, which made waves earlier this year with its Grok 4 model, runs on about 200,000 Nvidia H100 GPUs. OpenAI will have five times that power, and it’s still not enough for Altman going into the future. “Very proud of the team...” he wrote, “but now they better get to work figuring out how to 100x that lol.”
we will cross well over 1 million GPUs brought online by the end of this year!very proud of the team but now they better get to work figuring out how to 100x that lolJuly 20, 2025
The “lol” might make it sound like he’s joking, but Altman’s track record suggests otherwise. Back in February, he admitted that OpenAI had to slow the rollout of GPT‑4.5 because they were literally “out of GPUs.” That wasn’t just a minor hiccup; it was a wake-up call considering Nvidia is also sold out till next year for its premier AI hardware. Altman has since made compute scaling a top priority, pursuing partnerships and infrastructure projects that look more like national-scale operations than corporate IT upgrades. When OpenAI hits its 1 million GPU milestone later this year, it won’t just be a social media flex—it’ll be cementing itself as the single largest consumer of AI compute on the planet.
Anyhow, let’s talk about that 100x goal, because it’s exactly as wild as it sounds. At current market prices, 100 million GPUs would cost around $3 trillion—almost the GDP of the UK—and that’s before factoring in the power requirements or the data centers needed to house them. There’s no way Nvidia could even produce that many chips in the near term, let alone handle the energy requirements to power them all. Yet, that’s the kind of moonshot thinking that drives Altman. It’s less about a literal target and more about laying down the foundation for AGI (Artificial General Intelligence), whether that means custom silicon, exotic new architectures, or something we haven’t even seen yet. OpenAI clearly wants to find out.
The biggest living proof of this is OpenAI’s Texas data center, now the world’s largest single facility, which consumes around 300 MW—enough to power a mid-sized city—and is set to hit 1 gigawatt by mid-2026. Such massive and unpredictable energy demands are already drawing scrutiny from Texas grid operators, who warn that stabilizing voltage and frequency for a site of this scale requires costly, rapid infrastructure upgrades that even state utilities struggle to match. Regardless, innovation must prevail, and the bubble shouldn't burst.
Fun math:100,000,000 GPUs × $30,000 per GPU = $3,000,000,000,000$3 trillionJuly 20, 2025
The company isn’t just hoarding NVIDIA hardware, either. While Microsoft’s Azure remains its primary cloud backbone, OpenAI has partnered with Oracle to build its own data centers and is rumored to be exploring Google’s TPU accelerators to diversify its compute stack. It’s part of a larger arms race, where everyone from Meta to Amazon is building in-house AI chips and betting big on high-bandwidth memory (HBM) to keep these monster models fed. Altman, for his part, has hinted at OpenAI’s own custom chip plans, which would make sense given the company’s growing scale.
Altman’s comments also double as a not-so-subtle reminder of how quickly this field moves. A year ago, a company boasting 10,000 GPUs sounded like a heavyweight contender. Now, even 1 million feels like just another stepping stone toward something much bigger. OpenAI’s infrastructure push isn’t just about faster training or smoother model rollouts; it’s about securing a long-term advantage in an industry where compute is the ultimate bottleneck. And, of course, Nvidia would be more than happy to provide the building blocks.
Is 100 million GPUs realistic? Not today, not without breakthroughs in manufacturing, energy efficiency, and cost. But that’s the point. Altman’s vision isn’t bound by what’s available now but rather aimed at what’s possible next. The 1 million GPUs coming online by year’s end are a real catalyst for marking a new baseline for AI infrastructure, one that seems to be diversifying by the day. Everything beyond that is ambition, and if Altman’s history is any guide, it might be foolish to dismiss it as mere hype.
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Hassam Nasir is a die-hard hardware enthusiast with years of experience as a tech editor and writer, focusing on detailed CPU comparisons and general hardware news. When he’s not working, you’ll find him bending tubes for his ever-evolving custom water-loop gaming rig or benchmarking the latest CPUs and GPUs just for fun.
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usertests Even as these things start pushing into kilowatt power consumption territory, performance per watt is going up.Reply
100 million GPUs is not achievable for OpenAI very soon. It could take over 5 years of the AI bubble not bursting and significant new capacity being rushed online to make more wafers.
10-20 years from now, we could see a push into 3D designs/architectures that blow everything that came before them away in performance per watt, creating a ton of power-guzzling e-waste. Like we may see 10-100x perf/watt by 2040 from conventional designs, and 10,000x or more after that from exotic 3D, optical/analogue, etc.
It's not like this hasn't happened before, but we have been lured into complacency by the slower progress of the past 10-20 years. -
thisisaname I feel current "Ai" models have a big problem with diminishing returns as they add more hardware.Reply
https://techcrunch.com/2024/11/20/ai-scaling-laws-are-showing-diminishing-returns-forcing-ai-labs-to-change-course/ -
Giroro But don't worry, investors, he's going to totally make all that money back for you.Reply
https://robertkaplinsky.com/wp-content/uploads/2013/04/drevil_cover.jpg
He pinky promised. -
YSCCC I have a feel that either the AI bubble will burst soon, or we have a new accelerated way to self destruct our environment and extinct our own species at nuclear war peace... AI (LLM type) no doubt have helped do replace some donkey works like drafting some formal boring letters or use it as a more powerful Google for some generic answers.Reply
But time and time again when it comes to something really professional, or say, specialized knowledge, since LLM is token based training from existing documents, it almost always fail to give something really useful and more ofter some "looks professional BS", so in that sense, in the most critical/difficult use cases it more than often will trick you into trouble.
Now with the progression of LLM models improvements the competition for the gigawatt power the GPU array use to train it, and the more power and material to produce all those AI GPUs are creating astronomical amount of pollution and waste heat dumping into our planet.
In the past decade we are trying to use green energy to slow down our own extinction, and now we build local power plants just to power the AI array sounds really suicidal -
usertests Reply
https://ourworldindata.org/energy-production-consumptionYSCCC said:Now with the progression of LLM models improvements the competition for the gigawatt power the GPU array use to train it, and the more power and material to produce all those AI GPUs are creating astronomical amount of pollution and waste heat dumping into our planet.
In the past decade we are trying to use green energy to slow down our own extinction, and now we build local power plants just to power the AI array sounds really suicidal
Global energy consumption typically increases at least 2% a year, sometimes as much as 5%. It spiked in 2021 by +5.09% following a 3.48% pandemic-related decline in 2020. Then it was +1.82%, +1.88%, and finally +2.61% in 2024. Maybe that is indicative of the AI boom, but it's not a significant deviation from the norm.
It would be better if we were using less energy to achieve similar results, and driving that down every year, but that's unrealistic, especially with the global population increasing every year by about +0.85%.
So if "AI" or other technology are being used to accelerate scientific and technological progress (to counter the negative effects of our civilization), we might as well spend whatever power is demanded of it. Big LLMs from OpenAI et al. aren't doing that much good for science, but that could change later. The big exascale supercomputers that are being used for science are pushing 30-40 megawatts each. The top 10 on the Top500 list could be using over 166 megawatts at the same time (probably much less in practice).
~186,000 terawatt hours globally translates to over 21 million megawatts continuous. If OpenAI starts using a gigawatt in 2026, that's 0.00476% of all power (one twenty-one thousandth). That's not a significant heating/pollution impact. If OpenAI and all of its competitors started using 100 gigawatts in total, which would be nuts and maybe infeasible, It'd be around 0.5%. Big, but not astronomical.
I didn't cover energy production needed to continuously manufacture and ship millions of GPUs or AI accelerators, but I'd bet it doesn't come close to global fertilizer production (2% of energy). -
Arkitekt78 Where is the outrage from environmentalists? Seems like a lot of energy and resource drain just so some teenagers can cheat on their homework and those people bad at their jobs gain 2% more productivity...Reply -
usertests Reply
Even if they use a gigawatt, It's not even 0.01% of global power. It's a drop in the bucket compared to typical annual increases in energy consumption driven by a growing global population (that could grow another 35%).Arkitekt78 said:Where is the outrage from environmentalists? Seems like a lot of energy and resource drain just so some teenagers can cheat on their homework and those people bad at their jobs gain 2% more productivity...