OpenAI is procuring those inputs while deepening its financial dependence on the company it just benchmarked. On August 17, Nvidia agreed to provide up to $105 billion in financing for an OpenAI-leased data center campus in Ohio. "Nvidia is a really good partner, and we continue to need a lot of Nvidia," Richard Ho, OpenAI's vice president of hardware, told Bloomberg in an interview following the announcement.
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vanadiel007 I can see this going to same way as crypto mining with video cards: replacement with ASIC. I wonder what the next big thing is going to be. Has to be something with GPU's. Reply
Bigshrimp More money to throw into the bottomless pit of AI. Even with the gains in performance per kilowatt, they will just throw more hardware in and it will still pull a ton of power as if there were no gains. Might eek out more performance, but that's about it. Reply
Darkhands Can't wait till the day nvidia loses its lead in the AI race. They ditched gamers to grab all that AI pie, and they'll need to come crawling back. Reply
bit_user I just have to point out that efficiency scaling is definitely not on Nvidia's side, here. If they reduced clockspeeds a little bit, they could save a ton of power. The reason they've been clocking their server parts so aggressively is that their hardware costs datacenter operators a lot more than the power to run & cool them. So, Nvidia is all about maximizing the performance because that lets them charge more since they just need to deliver more perf/$ than either their old hardware or competitors' systems. The article said: Jalapeño wasn't tested against Vera Rubin, the Nvidia platform that's slated to power the first gigawatt of Nvidia systems OpenAI agreed to deploy in the second half of 2026. Ah, see? This is the problem everyone has when they think they can compete against Nvidia. Usually, it's Nvidia's previous generation they end up being competitive with. Nobody has managed to move fast enough to keep ahead of Nvida, except maybe Cerebras. Reply
bit_user vanadiel007 said: I can see this going to same way as crypto mining with video cards: replacement with ASIC. I wonder what the next big thing is going to be. Has to be something with GPU's. Nvidia bought Groq for the part that's most ASIC-friendly. That's also what OpenAI's new chip does. I'm sure Jalapeno is nowhere near as fast or efficient at inference, when compared to a Nvidia solution with Groq 3 LPU doing the same thing. Reply
alan.campbell99 More competition for fab capacity? I'd also wonder about the unit economics if said fabs are hiking prices, also being wafer scale how much of an issue defects would be. Putting aside all the other issues I have with this nonsense. Reply
usertests vanadiel007 said: I can see this going to same way as crypto mining with video cards: replacement with ASIC. I wonder what the next big thing is going to be. Has to be something with GPU's. I'm not sure they've done anything that Nvidia can't copy. And it wasn't compared against Rubin. When I think of an AI ASIC, I think of Taalas, which AMD recently acquired. You get one permanently baked smaller-sized model, with some ability to fine-tune it, running at extraordinary speeds. Reply
bit_user alan.campbell99 said: More competition for fab capacity? I don't really look at it like that. I think OpenAI would be buying chips, whether theirs/Broadcom's or Nvidia's. Maybe, by saving money on theirs, they can consume more wafers for the same $. So, it could be slightly higher contention for fab capacity. However, as the article points out, their solution relies on HBM, which is almost certainly the production bottleneck. alan.campbell99 said: also being wafer scale how much of an issue defects would be. Uh, Jalapeno doesn't appear to be a wafer-scale engine. They're just showing an uncut wafer, as is traditional for these sorts of chip announcements. It's probably a first run or otherwise marginal wafer with too many defects to actually use. I guess showing the wafer is proof that it reached the production stage. Reply
bit_user usertests said: I'm not sure they've done anything that Nvidia can't copy. And it wasn't compared against Rubin. Nvidia is somewhat chained down by the legacy of CUDA compatibility. However, the Groq LPU is not. That's the key to Rubin's competitiveness on inference workloads. Reply
Trake_17 Darkhands said: Can't wait till the day nvidia loses its lead in the AI race. They ditched gamers to grab all that AI pie, and they'll need to come crawling back. Hate to break it to you but this day will never come. The dealer has never come crawling back to the addict in all of history. Beyond that, Nvidia is in no danger of losing its footing on this front, particularly given OpenAIs dependence on Nvidia and Nvidias in estments in OpenAI. Reply
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