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Nvidia’s $589B DeepSeek rout

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Re: Nvidia’s $589B DeepSeek rout

#731

Post by Jlagreen »

fldskfjdslkfj wrote: post #673
giantrobot wrote: post #662

Earlier quoted context omitted.

> Nvidia loses it's biggest moat and ability to monopolize the tech, CUDA isn't enough CUDA is plenty for right now. AMD can't/won't get their act together with GPU software and drivers. Intel isn't in much better of a position than AMD and has a host of other problems. It's also unlikely the "let's just glue a thousand ARM cores together" hardware will work as planned and still needs the software layer. CUDA won't b…

CUDA will still be a moat for the near future and nobody is saying that Nvidia will die, but the thing is that Nvidia margins will drop like crazy and so will it's valuation. It will go back down to being a "medium tech" company. Basically training got way cheaper, and for inference you don't really need nvidia, so even if there's an increase for cheaper chips there's no way the volume makes up for the loss of margin…

No, Nvidia's margins won't drop at all and the proof for this is Apple.

The units of AI accelerators will explode, the market will explode.

At the end of the day, Nvidia will have 20-30% of the unit share in AI HW and 70-80% of the profit share in the AI HW market. Just like Apple makes 3x the money compared to the rest of the smartphone market.

Jensen has considered Nvidia a premium vendor for 2 decades and track record of Nvidia's margins show this.

And while Nvidia remains a high premium AI infrastructure vendor, they will also add lots of great SW frameworks to make even more profit.

Omniverse has literally no competition. That digital world simulation combines all of Nvidia's expertise (AI HW, Graphics HW, Physics HW, Networking, SW) into one huge product. And it will be a revolution because it's the first time we will be able to finally digitalize the analog world. And Nvidia will earn tons of money because Omniverse itself is licensed, it needs OVX systems (visual part) and it needs DGX systems (AI part).

Don't worry, Nvidia's margins will be totally fine. I would even expect them to be higher in 10 years than they are today. Nobody believes that but that's Jensen's goal.

There is a reason why Nvidia has always been the company with the highest P/S ratio and anyone who understands why, will see the quality management immediately.

Re: Nvidia’s $589B DeepSeek rout

#732

Post by 01100011 »

asadotzler wrote: post #693
01100011 wrote: post #683

Earlier quoted context omitted.

> Can you guess what the next step will be? He fixes the cable? But seriously, video encoding isn't AI. Video encoding is a well understood problem. We can't even make "AI" that doesn't hallucinate yet. We're not sure what architectures will be needed for progress in AI. I get that we're all drunk on our analogies in the vacuum of our ignorance but we need to have a bit of humility and awareness of where we're at.

Including considering that it can't be made much better, that the hallucinations are a fundamental trait that cannot be eliminated, that this will all come tumbling down in a year or three. You seem to want to consider every possible positive future if we just work harder or longer at it, while ignoring the most likely outcomes that are nearer term and far from positive.

There are already strategies to reduce hallucinations but, guess what? I'll let you fill in the rest.

Re: Nvidia’s $589B DeepSeek rout

#733

Post by 01100011 »

dylan604 wrote: post #687
01100011 wrote: post #683

Earlier quoted context omitted.

> Can you guess what the next step will be? He fixes the cable? But seriously, video encoding isn't AI. Video encoding is a well understood problem. We can't even make "AI" that doesn't hallucinate yet. We're not sure what architectures will be needed for progress in AI. I get that we're all drunk on our analogies in the vacuum of our ignorance but we need to have a bit of humility and awareness of where we're at.

Conversely, can you name one computing thing that used to be hard when it was first created that is still hard in the same way today after generations of software/hardware improvements?

Simulations and pretty much any large scale modelling task. Why do you think people build supercomputers?

Now that I mentioned it, I think supercomputers and the jobs they run are the perfect analog for AI at this stage. It's a problem that we could throw nearly limitless compute at if it were cost effective to do so. HPC encompasses a class of problems for which we have to make compromises because we can't begin to compute the ideal(sort of like using reduced precision in deep-learning). HPC scale problems have always been hard and as we add capabilities we will likely just soak them up to perform more accurate or larger computational tasks.

To quote Andrej Karpathy (https://x.com/karpathy/status/1883941452738355376): "I will say that Deep Learning has a legendary ravenous appetite for compute, like no other algorithm that has ever been developed in AI. You may not always be utilizing it fully but I would never bet against compute as the upper bound for achievable intelligence in the long run. Not just for an individual final training run, but also for the entire innovation / experimentation engine that silently underlies all the algorithmic innovations."

Re: Nvidia’s $589B DeepSeek rout

#734

Post by talldayo »

epicureanideal wrote: post #488
segasaturn wrote: post #430

Earlier quoted context omitted.

That still means that that AI firms don't have to buy as many of Nvidia's chips, which is the whole thing that Nvidia's price was predicated on. FB, Google and Microsoft just had their their billions of dollars in Nvidia GPU capex blown out by $5M side-project. Tech firms are probably not going to be as generous shelling out whatever overinflated price Nvidia was asking for as they were a week ago.

Although there’s the Jevon’s Paradox possibility that more efficient AI will drive even more demand for AI chips because more uses will be found for them. But possibly not super high end NVDA chips but instead little Apple iPhone AI cores or smartwatch AI cores, etc. Although not all commodities will work like fossil fuels did in Jevon’s Paradox. It could be the case that demand for AI doesn’t grow fast enough to kee…

> But possibly not super high end NVDA chips but instead little Apple iPhone AI cores or smartwatch AI cores, etc.

We tried that, though. NPUs are in all sorts of hardware, and it is entirely wasted silicon for most users, most of the time. They don't do LLM inference, they don't generate images, and they don't train models. Too weak to work, too specialized to be useful.

Nvidia "wins" by comparison because they don't specialize their hardware. The GPU is the NPU, and it's power scales with the size of GPU you own. The capability of a 0.75w NPU is rendered useless by the scale, capability and efficiency of a cluster of 600w dGPU clusters.

Re: Nvidia’s $589B DeepSeek rout

#736

Post by mrtksn »

SOLAR_FIELDS wrote: post #721
deadbabe wrote: post #628

Earlier quoted context omitted.

Most people know Jevon’s Paradox there is just rarely an opportunity to bring it up, similar to Poe’s Law.

I never knew there was an actual term for this, but I knew of the concept in my professional work because this situation often plays out when the government widens roads here in the States. Ostensibly the road widening is intended to lower congestion, but instead it often just causes more people to live there and use it, thereby increasing congestion. Probably a decent amount of professions have some variation of thi…

IMHO it happens as long as you can find use cases that were previously unfeasible due cost or availability constraints.

At some point the thing no longer brings any benefits because other costs or limitations overtake. for example, even faster broadband is no longer that big of a deal because your experience on most websites is now limited by their servers ability to process your request. However maybe in the future the costs and speeds will be so amazing that all the user devices will become thin clients and no one will care about their devices processing power, therefore one more increase in demand can happen.

Re: Nvidia’s $589B DeepSeek rout

#737

Post by dragonwriter »

culi wrote: post #664
dragonwriter wrote: post #459

Earlier quoted context omitted.

What about DeepSeek negates NVidia’s advantages over other GPU vendors?

You can train, or at least run, llms on intel and less powerful chips

> You can train, or at least run, llms on intel and less powerful chips

The claimed training breakthrough is an optimization targeting NVidia chip, not something that reduces NVidia's relative advantage. Even if it is easily generalizable to other vendors hardware, it doesn't reduce NVidia's advantage over other vendors, it just proportionately scales down the training requirements for a model of a given capacity. Which, maybe, very short term reduces demands from the big existing incumbents, but it also increases the number of players for which investing in GPUs for model training at all is worthwhile, increasing aggregate demand.

Re: Nvidia’s $589B DeepSeek rout

#739

Post by culi »

TeaBrain wrote: post #578
culi wrote: post #445

Earlier quoted context omitted.

Why mention it if you're not gonna link it?

The chart is within the below linked article. The below link I've provided is a gift link, so you should be able to access the article through it. https://www.bloomberg.com/opinion/articles/2025-01-27/deepse...

Thanks! I have the Bypass Paywalls extension so i forgot bloomberg even had paywalls

https://github.com/bpc-clone/bypass-paywalls-firefox-clean

Re: Nvidia’s $589B DeepSeek rout

#740

Post by cyberax »

AnthonyMouse wrote: post #718
plaidfuji wrote: post #640

Here’s a take I haven’t seen yet: If training and inference just got 40x more efficient, but OpenAI and co. still have the same compute resources, once they’ve baked in all the DeepSeek improvements, we’re about to find out very quickly whether 40x the compute delivers 40x the performance / output quality, or if output quality has ceased to be compute-bound.

> If training and inference just got 40x more efficient Did training and inference just get 40x more efficient, or just training? They trained a model with impressive outputs on a limited number of GPUs, but DeepSeek is still a big model that requires a lot of resources to run. Moreover, which costs more, training a model once or using it for inference across a hundred million people multiple times a day for a year?…

> DeepSeek is still a big model that requires a lot of resources to run

I can run the largest model at 4 tokens per second on a 64GB card. Smaller models are _faster_ than Phi-4.

I've just switched to it for my local inference.

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