AI's Future and Nvidia's Fortunes Ride on the Race To Pack More Chips Into One Place (yahoo.com) 15
Leading technology companies are dramatically expanding their AI capabilities by building multibillion-dollar "super clusters" packed with unprecedented numbers of Nvidia's AI processors. Elon Musk's xAI recently constructed Colossus, a supercomputer containing 100,000 Nvidia Hopper chips, while Meta CEO Mark Zuckerberg claims his company operates an even larger system for training advanced AI models. The push toward massive chip clusters has helped drive Nvidia's quarterly revenue from $7 billion to over $35 billion in two years, making it the world's most valuable public company.
WSJ adds: Nvidia Chief Executive Jensen Huang said in a call with analysts following its earnings Wednesday that there was still plenty of room for so-called AI foundation models to improve with larger-scale computing setups. He predicted continued investment as the company transitions to its next-generation AI chips, called Blackwell, which are several times as powerful as its current chips.
Huang said that while the biggest clusters for training for giant AI models now top out at around 100,000 of Nvidia's current chips, "the next generation starts at around 100,000 Blackwells. And so that gives you a sense of where the industry is moving."
WSJ adds: Nvidia Chief Executive Jensen Huang said in a call with analysts following its earnings Wednesday that there was still plenty of room for so-called AI foundation models to improve with larger-scale computing setups. He predicted continued investment as the company transitions to its next-generation AI chips, called Blackwell, which are several times as powerful as its current chips.
Huang said that while the biggest clusters for training for giant AI models now top out at around 100,000 of Nvidia's current chips, "the next generation starts at around 100,000 Blackwells. And so that gives you a sense of where the industry is moving."
AI has not future (Score:3)
At least the LLM-variant does not beyond somewhat better search, generation of crappy text and images and crappy code, with the occasional hallucination thrown in. I seriously doubt that will be enough to justify the cost. Sure, eventually, the tech may become cheap enough and then it may play a minor role, but that time has not arrived.
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See also: Sun Microsystems - "The network is the computer!" Turns out they were right. Didn't help them though.
Self-driving cars might be a wildcard since it has to be done locally (inference, not training) but I still doubt there will be m
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At least the LLM-variant does not beyond somewhat better search, generation of crappy text and images and crappy code, with the occasional hallucination thrown in. I seriously doubt that will be enough to justify the cost. Sure, eventually, the tech may become cheap enough and then it may play a minor role, but that time has not arrived.
The research in AI is still evolving. Future progress will depend most on (1) the invention of new and different AI models and architectures and (2) the discovery of new use cases. Sure, simply scaling out the number of chips and memory with the same architectures is likely to at best incremental. Transformers were invented a few years ago. Why do we think that transformers are the very last possible significant invention? And all this talk about AGI. That's what journalists, scifi writers, and non-in
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One nuclear power station to run the AI servers, two nuclear power stations to run the cooling system. Liquid ammonia cooling?
A thought occurs, if they could build the chip to handle 250 F the outlet temperature from the cooling system would be high enough to heat the feed water to a multiple effect evaporator to desalinate water. Then they could get some use out of the energy other than getting the wrong answer quicker.
So are they going to make GenAI progress? (Score:4, Interesting)
Logically, if only time and computing power were limiting it, you'd assume Generative AI would be ROCK SOLID in a few simple and common areas and more scatter shot everywhere else. Everything I've tried it for, both easy and hard, I saw at least one failure per question.
I basically don't feel like I can trust any AI I've seen. Even the IntelliJ one...often suggests code that doesn't even compile...or resemble working...in things like the core IO libs...not that JetBrains is the industry gold standard, but you'd think if your domain was a single core JDK API and all you do is train Java and there's a fuckton of open source Java in the world using that core API...you'd at least be able to figure out a String can't be passed where it's expecting an int or a long.
Re: So are they going to make GenAI progress? (Score:2)
Integrate! (Score:1)
By itself I don't think it will solve key gaps even if more horse-power added, but find a way to integrate it with logic engines and knowledge bases, say Cyc Project, and then it can attack problems from multiple angles that pattern-matching alone just can't.
This is fine, as long as... (Score:2)
...CEOs and investors understand that AI is a long term research project with a significant chance of failure and little chance of short term profit
From Quake to Quick (Score:2)
Not bad for something that plays Quake without the softeare renderer.
The past keeps repeating itself (Score:2)
In the early days of the computer industry, there were those big computers and the mini computers. Everything was centralized, and you had dumb terminals to allow people to make use of the computers. Then, the age of the PC hit, suddenly you had things that would work on PCs, and then you had networking to let individual PCs to talk to each other or to a server. The idea of client-server has always made sense for those workloads that can't be handled by less powerful machines. We also have clusters,
Capabilities not leveled off yet (Score:2)
I was lured out of retirement to build these monsters.
Current capabilities are not yet defined. Until folks know the ultimate performance envelope, any application or lateral move outside of core AI can be obsoleted almost instantly.
You'll see this continue until that leveling happens, or the AI starts to improve itself and there's a clear correlation between capabilities and power consumption.
Until then, it's wide open throttle. Most people either are having a very hard time accepting they're going to be r
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I'm considering the context of that claim. Right now, LLMs support very good search and summary. They are useful for creating prototypes, but the prototypes need careful review. They are currently untrustworthy for production-quality data/code/information.
I guess the only forward path is for them to improve in quality until they are good at production. (The economic incentives for companies are too great NOT to i
Bad scale! (Score:1)
Packing more chips is how I got obese. [slashdot.org]