It seems deeply overvalued to me, as it's pretty likely that NVIDIA's competitors will have parallel computation hardware that competes well enough. I'm personally not a fan of NVIDIA's drivers or the reliability of their hardware.
As compared to AMD or Intel? I wish there was real competition to Nvidia but there isn't. I'm not a fan of their defacto monopoly but they do have the best product on the market and their competition has been asleep for 10 years. AMD and Intel barely knew what deep learning was 10 years ago (and certainly did not appreciate the opportunity) and Nvidia was already investing heavily.
that sounds very interesting, can you link/share/describe some details on it? how much did they invest? how? into CUDA? what else?
I am curious about your PaaS because we were analyzing that business for fun first. A small thread here in HN: https://news.ycombinator.com/item?id=39329764
Here's an old tutorial video of the product: https://youtu.be/V2q9hVdi80w
We were doing cool things and on the cutting edge but ultimately couldn't make a business out of it and weren't talking to the right people.
CUDA 1.0 was released in 2007:
* https://insidehpc.com/2007/07/nvidia-releases-cuda-10/
* https://developer.nvidia.com/cuda-toolkit-archive
From SIGGRAPH 2007, "GPU computing with NVIDIA CUDA":
* https://dl.acm.org/doi/10.1145/1281500.1281647
"NVIDIA: The Era of the Personal Supercomputing":
* https://www.nvidia.com/content/events/siggraph_2007/supercom...
Before AI/ML was hot, and before even the Bitcoin paper was released. NVidia was investigating/experimenting/investing in the concept before there was any kind of 'killer app' for it.
Later on they acquired PGI, which thanks to PTX, had C, C++ and Fortran compilers, thus adding Fortran into the mix.
Followed along by all the IDE, graphical debuggers tooling and library ecosystem.
Meanwhile Intel and AMD were doing who knows what at Khronos stuck in their "C is good enough" mentality, and barely released useful developer experiences.
All this says is that AI and LLMs are extremely over hyped, and the market believes Nvidia's tech is the only viable supplier of the platform LLMs run on.
These are things we already knew, so it's not surprising the market is quadrupling what it thinks Nvidia is worth.
As a thought experiment, imagine buying 50% of a company on the open market and then seeing the price go up accordingly as the market does, then saying it must be even more valuable than I thought! And buying the other half of the company at the higher price. You caused that "value" by buying.
No one investor has a trillion dollar opportunity, and no competitor does either. Making an assumption that a competitor will come along and zero out Nvidia because they have a better AI chip is not rational. For one the value of Nvidia isn't solely based on this, and by the time you've made your AI chip the market is going to have changed.
Considering how insanely inflated the public's expectations of what LLMs can do, despite the fact that they are only a mirage of intelligence, it would probably be foolhardy to build a new AI chip to replace them.
I suppose for AMD they could see their stock increased by such a margin if they could just produce a chip that convinced the market, but were they not already trying to do that?
Intel has already been making good low-end GPUs (with lousy but rapidly-improving drivers). If they're smart, they'll keep at it.
Now they're completely outclassed by TSMC and have to partner with UMC to compete.
https://www.xda-developers.com/intel-roadmap-2025-explainer/
Same could have been told about Intel, but Apple anyway beat them in some ways and took away big market share
Which could lead to Intel realizing the opportunity they have. Create decent libraries that work across every vendor's GPUs. In the short term this helps AMD at the expense of Nvidia, which in itself helps Intel by preventing Nvidia from maintaining a moat. In the medium term Intel then has people using Intel's libraries to write code that will work on their future GPUs and then their problem is limited to producing competitive hardware.
But the weird thing about the "Nvidia will remain undefeated forever" theory is that it seems to assume they have some kind of permanent advantage.
Nvidia was well positioned to make an early investment in this because they had the right combination of existing technology and resources. Other companies would have had to invest more because they were starting from a different place (e.g. Microsoft), or didn't have the resources to invest at the time (AMD).
But now the market is proven and there are more than half a dozen 800 pound gorillas who all want a piece of it. It's like betting that Tesla will retain their 2022 market share in electric car market even as the market grows and everyone else gets in. Maybe some of the others will stumble, but all of them? Apple, AMD, Google, Intel, Microsoft, Amazon and Facebook?
Yes. Especially once you take the Chinese electric car companies into account, that are already outselling Tesla.
But maintaining the 80% they had a few years ago, much less 100%? That's not optimism, it's fantasizing.
Btw, I was talking about global electric car production. I don't know whether Tesla ever did 80% of global electric car sales?
Please tell us more of your expertise and deep insight, FAANG employee #1,908,680.
Google has developed their own chips. Apple has developed their own chips. It’s really not that hard if your pockets are deep enough or the bottom line checks out.
When Apple launches their AI offering this year, it’s not going to need NVIDIA.
Apple isn’t going to launch with an Nvidia killing alternative, I’ll bet you $1,908,680 it’s backed by Nvidia.
They will likely use their neutral chips for local models, but their data center stuff will be 100% Nvidia.
Also, Apple has a longstanding dislike for Nvidia and even if they weren't going to design their own chips at launch, they could be using AMD.
I once read a fascinating corporate history of Xerox, and that company became deeply, deeply f'ed up in ways that are of their time but do have strong parallels to the issues I understand that FAANG, particularly Google, have.
This book lauded Xerox's success at reforming its corporate culture, regaining a strong position in the photocopier market and even spent a chapter detailing their success in getting into electronic typewriters!
With the benefit of hindsight the company didn't survive its core product losing all relevance any better than Kodak did, but that was still some way in the future (if foreseeable by the 1980s).
That said, much of the material about a hugely bloated organisation, with a sclerotic bureaucracy and lots of cushy middle managers assembled through a previous period of explosive growth, turning out poor-quality product, sounds very reminiscent of some of what we now hear about the current big tech companies.
The title reflects another American obsession at the time - the idea that the US was "losing" to Japan.
Perhaps Google until around 2010? Or Goldman Sachs until the 1990s?
You get a weird insider bias where all you see are bug reports and problems, so you get the impression the product is shit, even if 99% of customers love it.
A bit funny thing is that they are essentially software company which focuses on software quality, if you look at the stats how many people are working with software over there. And still it is not good enough?
In 3-5 years what will a 10% performance difference matter to you? Then calculate how much that 10% performance difference is going to cost in real dollars to run on nvidia hw and then the fun math should start.
Given that data centers only have so much power and AI really needs to be in the same data center as the data, if you can squeeze out a bit more power efficiency so you can fit more cards, you are getting gains there as well.
When I was mining ethereum, the guy who wrote the mining software used an oscilloscope to squeeze an an extra 5-10% out of our cards and that was after having used them for years. That translated to saving about 1 MW of power across all of our data centers.
Let me also remind you that GPUs are silicon snowflakes. No two perform exactly the same. They all require very specific individual tuning to get the best performance out of them. This tuning is not even at the software level, but actual changes to voltage/memory timings/clock speeds.
I suspect a lot of AI inference (thought probably not the majority) will happen on mobile devices in the future. There power is also at a premium, and less fungible with money.
Untrue. I have filled 3 very large data centers where there was no more power to be had. Data centers are constrained by power. At some limit, you can't just spend more money to get more power.
It also becomes a cooling issue, the more power your GPUs consume, the more heat they generate, the more cooling that is required, the more power that is required for cooling. Often measured in PUE.
But you can use money to rent more data centres.
You can also pay to get more data centres built.
Even still, power is limited. You can build DC's but if you can't power them... what are you going to do? This isn't just throw more money at the problem.
Have you noticed that data center stock, like EQIX, are at all time highs?
Though the FTSE All-World index (or the S&P 500) is also at all time highs, so I would expect most stocks to be at all time highs, too.
> Even still, power is limited. You can build DC's but if you can't power them... what are you going to do? This isn't just throw more money at the problem.
I guess you can try to outbid other people? But thanks: I didn't know the data-centre-building industry was so supply constrained at the moment.
> I didn't know the data-centre-building industry was so supply constrained at the moment.
The whole supply chain is borked. Try to buy 800G mellanox networking gear. 52 week lead time. I've got a fairly special $250 cable I need that I can't get until April. I could go on and on...
I've seen some of that playing out in a business that was using GPUs for deep learning as applied to financial market making. They were throwing a lot of money at nvidia, too.
I wonder if it's enough money in total in AI to show up in countrywide GDP figures anytime soon. Because either AI's hunger for ever more computing power has to slow down, or world GDP has to increase markedly.
Given the speed of light as an upper limit, in the very long run we can at most have a cubic growth, not an exponential growth. Something will have to give eventually.
(OK, you also probably need to Bekenstein bound. Otherwise, you could try sticking more and more into information into the same amount of space. But there's a limit to that, before things turn into black holes.)
I think there is no way that you will see compilers that advanced within 3 years, sadly.
As a result there is a lot of sceptism as to whether it's actually faster in real world scenarios. There are a few articles explaining this situation. Here is one: https://www.forbes.com/sites/karlfreund/2023/12/13/breaking-...