“In 10 years, computers will be doing this a million times faster.” The head of Nvidia does not believe that there is a need to invest trillions of dollars in the production of chips for AI::Despite the fact that Nvidia is now almost the main beneficiary of the growing interest in AI, the head of the company, Jensen Huang, does not believe that
Despite the fact that Nvidia is now almost the main beneficiary of the growing interest in AI, the head of the company, Jensen Huang, does not believe that additional trillions of dollars need to be invested in the industry.
*Because of
You heard it, guys. There’s no need to create competition to Nvidia’s chips. It’s perfectly fine if all the profits go to Nvidia, says Nvidia’s CEO.
This isn’t necessarily about just hardware. Current ML architectures and inference engines are far from being at peak efficiency. Just last year we saw 20x speedups for llm inference on some hardware. “a million times” is obviously hyperpole though.
Literally reading preprint papers daily on more efficient implementations of self attention approximations.
Sorry I have doubts, because that would require a factor 4x increase every year for 10 years! 4x^10 = 1,048,576x
Considering they historically have had problems achieving just twice the speed per year, it does not seem likely.Honestly as someone who has watched the once-fanciful prefixes “giga” and “tera” enter common parlance, and saw kilobytes of RAM turn to gigabytes, it’s really hard for me to think what he’s saying is impossible.
Nobody is saying it won’t happen eventually. But a million times within the next decade, i.e. 4x better every year for 10 years?
This generation isn’t better than last generation by even close to that. Nevermind doing 4x for 10 years straight.
He was probably not being literal with the number, but when you’re the head of a computer chip hardware company you should pick numbers carefully.
Even if he is accurate, specialist hardware will outperform generic hardware at what it is specialized for.
I remember a story sometime in the 00s about PCs finally getting to the point where they were as fast as one of the WWII code breaking computers (or something like that). It wasn’t because we backtracked in computer speeds after WWII, but because even that ancient hardware was able to get good performance when it was purpose-built, but it couldn’t do anything else and likely would have required a lot of work to adjust to a different kind of cypher scheme, if it could be adapted at all.
So GP compute might be a million times faster in a decade, but specialist AI chips might be a million times faster than that.
A hardware neural net might be able to eliminate memory latency by giving each neuron fast resisters to handle all their memory needs. If it doesn’t need to change connections, each connection could be hard wired. A GPU wouldn’t have a chance at keeping up no matter how wide that memory bus gets or how many channels it gets split into. It might even use way less power (though with the elimination of memory latency, it could go fast enough to use way more, too).
So a Cerebras wafer will be 10^6 faster for the same computation as now, for the same price, in just 10 years? Not after Moore scaling ended many years ago and neural hardware architecture has matured. You can sure go analog, but that’s not the same computation. And that’s the end of the line, not without true 3d integration.
It requires 4X speed increase every year, production quality scale can’t provide even close to half of that, maybe 25%, then another 25% from design, and regarding increasing die sizes they are already close to the end. So the only way to get from 150% to 400% per year is by using multi chip designs, meaning they will have to use 2.5x more chips per year. so the multi chip package in 10 years will probably have to have almost 10,000 chips! All of them bleeding edge!!!
The H200 is estimated to cost $40K, the future 10 year chip will be more like $40 million. Or maybe more like impossible to achieve.
If chips = cpus, here, then I imagine that will hit a limit also (Amdahl’s law).
A chip is also called a die, it’s the piece cut out from the wafer, which is then packaged onto a chip package.
Since traditionally there were always 1 chip per chip package, the 2 words were used almost synonymously.
I this case it’s basically GPU chips, which AFAIK AMD has already figured out how to use in multi chip packages. Meaning one package contains multiple chips that work “almost” as well as a single chip of similar size.The advantage of multichip packages are obvious, production costs are way lower because smaller dies causes lower percentage of flawed dies, and allows for better binning of higher end parts.
Additionally it allows designs of way more complex packages, than would be possible with monolithic chips. This is the reason AMD has been taking marketshare in server markets from Intel. Because Intel has not been able to match the multichip design AMD introduced with Epyc in 2016/17, which originally was 4 Ryzen chiplets/chips/dies packaged together as one big 32 core server chip. Where the biggest Intel could make was 28 cores.But packaging almost 10000 GPU chips together is completely different, and I don’t think that will be relevant within 10 years.
Amdahls law however is part obvious and part bullshit. Everything your mind is able to do semi efficiently, can be multithreaded, it is very few things that can’t.
Amdahls law is basically irrelevant with regard to AI, as AI has a lot of patten recognition, and pattern recognition is perfect for multi threading.And to add: currently TSMC nodes have a reticle limit of 858mm². I.e. that’s the largest chips you can make on their wafers. Then in the real world you do it slightly below that.
Future nodes are reducing this to the 350-450mm² range.
High end GPUs/HPC cards basically have to go to multi-die, even in the fantasy world of 100% perfect yields.
Then stop making new chips each year with a 5-7% performance improvement with a 20% increase in prices.
So, for a bit more tech illiterate, their claim is bs?
I mean 1mio x is a big claim anyway.
Yeah really, semiconductor has begun stagnating in progress recently due to fundamental limits. I’m gonna call bull on this one, I think they are rather forecasting pluging demand.
It depends what you call AI.
True artificial intelligence likely requires quantum computing because there’s some quantum stuff happening our brains and probably the smartest living human (Sir Roger Penrose) thinks that’s where the secret to consciousness is hiding after spending the last couple decades investigating that after helping Hawking finish up Einstein’s work
If you just mean a chat bot that can pass the Turing test, then yeah we can just wait a decade instead of developing special tech for AI.
I mean, if we really develop artificial intelligence before we understand our own consciousness, we’re probably fucked anyways.
It’d be like somehow inventing a nuclear bomb before understanding what radiation was. We’d have no idea what we’re creating or what the consequences of flipping the switch would be.
Roger Penrose is a mathematician who made important contributions to theoretical physics in the 1960ies, for which he received a Nobel Prize. In later decades, he published speculative books on consciousness, quantum physics, and neurobiology. These ideas have been out there for about 30 years now but have not been able to convince scientists in general. Rather, they are generally considered implausible or outright contradicted by the evidence. Simply put: It’s wrong.
The idea that quantum physics plays a direct role in brain function is very much on the fringes of science.
No offense meant. I know these ideas are very important to many spiritual people, but I felt the casual reader should know that it is not important in science.
Do you know if there are, or if there are plans for a “new” Turing test ?
The turing test is a rhetorical tool by turing to outline his logical positivist beliefs. Turing did believe in its use as an actual test but it’s not a discrete test, it’s a test of hypothetically infinite time.
Yes. There is a newer test called Voigt-Kampf that can test advanced AI
Because the ceos copy has to sound good for the shareholders on either side.
Can we just pray for the poor engineers who actually have to build these million times faster machines
“But if you invest this money, I can eat prime rib all week.”
Bet
Oh well. The world is going to burn anyway. Fuck this shit hole we call earth
