Apple has an entire diversified product roadmap and ecosystem. Nvidia has a gpu. I don’t see longevity for Nvidia.
need dataframes and pandas? cuDF. need compression/decompression? nvcomp. need vector search? cuVS. and the list goes on and on and on.
Sure, but that doesn’t mean I’m going to pay a billion extra for my next cluster – a cluster that just does matrix multiplication and exponentiation over and over again really fast.
So I’d say CUDA is clearly a moat for the (relatively tiny) GPGPU space, but not for large scale production AI.
There's a lot of other things which are very GPU parallelizable which just aren't being talked about because they're not part of the AI language model boom, but to pick a few I've seen in passing just from my (quite removed from AI) job:
- Ocean weather forecasting / modelling - Satellite imagery and remote sensing processing / pre-processing - Processing of spatial data from non-optical sensors (Lidar, sonar) - Hydrodynamic and aerodynamic turbulent flow simulation - Mechanical stress simulation
Loads of "embarrassingly parallel" stuff in the realms of industrial R&D are benefitting from the slow migration from traditional CPU-heavy compute clusters to ones with GPUs available, because even before the recent push to "decarbonise" HPC, people were seeing the increase in "work done per watt" type cost efficiency is beneficial.
Probably "relatively tiny" right now compared to the AI boom, but that stuff has been there for years and will continue to grow at a slow and steady pace, imo. Adoption of GPGPU for lots of things is probably being bolstered by the LLM bros now, to be honest.
CUDA benefits from being early to market in those areas. Mature tools, mature docs, lots of extra bolt-ons, organizational inertia "we already started this using CUDA", etc.
The silence from AMD has been deafening, though. I can't fathom why they're just ignoring the AI market.
I have NEVER even HEARD of Rocm, and neither has anyone in the GPU programming slack group I just asked.
CUDA is absolutely a moat.
For AI, I assume enterprise do care if it's Nvidia. Right now, Nvidia is in the "no one ever got fired for buying Nvidia" camp. You can buy AMD to save a few dollars, run into issues, and get fired.
And sure, for small scale and research activities Nvidia makes sense, even for the long term. But that’s not where the money is at, either.
The only way Nvidia can sustain its valuation is if it can win in the large scale production AI market long term, and that market turns out to be huge. I don’t really see how that can happen. As soon as NN architectures are stable over the economic lifetime of a chip custom ASICs will make so much more sense: 10x more performance and no Nvidia tax. It’s already happening [1].
>10x more performance and no Nvidia tax. It’s already happening
Nvidia can do the same though.
The moat isn't in inference. It's in training.
Sure, but they currently have a P/E ratio of 67… So being vastly bigger in 2035 is not necessarily enough. They have to be enormously bigger and still hugely profitable.
> Nvidia can do the same though.
Yes, but then they don’t have that moat.
> The moat isn't in inference. It's in training.
I’d say it’s even narrower than that: Nvidias AI moat is in training novel / unforeseen NN architectures. Will that be a meaningful moat in 2035?
But it all comes down to how good the GPT5 class of LLMs are for the next 2 years.
I guess what I’m saying is that it really doesn’t.
But in order to make money on shorts you not only have to be right, you also have to get the timing right. That’s a lot more difficult…
When some of Apple’s side-quests are industry giants in their own right, I think it’s fair to say that Apple are diversified.
AirPods are already usable as normal Bluetooth wireless earphones.
What I mean is, think about the addressable market comprised of the people who want to use the watch without iPhone.
I think sometimes we underestimate the number of people out there who do not have an iPhone.
Now account for the drop in companion devices (Airpods and AppleWatch work best with an iPhone, and it's pretty apparent when you aren't using one)
Then take a gander at Cloud services that no longer push themselves via the red badge on every handset, for the photos people are taking on competing platforms (Google Photos is the obvious danger, but even Dropbox through Onedrive pose risks to total revenue)
Should probably account for App Store revenue as well, considering why most Apple customers buy apps, and then how many buy Macintosh just to develop for the massive iPhone market
None of these are exclusively tied to the iPhone, but none look nearly as nice without the iPhone being a (and possibly even the) market leader
https://www.ft.com/content/e30eb646-a7ad-496e-8fa4-ff1b4445e...
If AI doesn't pan out, those H100s are not going to find a lot of sales anywhere and Nvidia could be back to a gaming GPU company.
Yes.
“Apple annual research and development expenses for 2024 were $31.37B, a 4.86% increase from 2023. Apple annual research and development expenses for 2023 were $29.915B, a 13.96% increase from 2022. Apple annual research and development expenses for 2022 were $26.251B, a 19.79% increase from 2021”
— https://www.macrotrends.net/stocks/charts/AAPL/apple/researc...
Chips, for one, don’t research themselves: https://www.apple.com/uk/newsroom/2023/03/apple-accelerates-...
Ex: https://news.ycombinator.com/item?id=41491121
https://news.ycombinator.com/item?id=41948739
Tested by community: https://news.ycombinator.com/item?id=41799324
https://news.ycombinator.com/item?id=42019694
VR was a 10 year or so project but agreed no product-market fit yet.
> They seem to have lost faith in their own ability to innovate.
But here's the truth: there is no average user. Or rather, the average user is not representative of the distribution of users. If you build for average, you build for no one. It is hard to invent things, so use the power of scale. It is literally at your fingertips if you want it. Take advantage of the fact that you have a cash cow. That means you can take risks, that you can slow down and make sure you are doing things right. You're not going to die tomorrow if you don't ship, you can take on hard problems and *really* innovate. But you have to take off the chains. Yes, powerful tools are scary, but that doesn't mean you shouldn't use them.
What does this mean? Just thinking about iPhones: As of September 2024, there are an estimated 1.382 billion active iPhone users worldwide, which is a 3.6% increase from the previous year. In the United States, there are over 150 million active iPhone users.
If you're remotely familiar with high dimensional statistics, one of the most well known facts is that the density of a normal ball lies on the shell while the uniform ball is evenly distributed. Meaning if you average samples of a normal ball, the result is not representative of the samples. The average is inside the ball, but remember, all the sampling comes from the shell! It is like drawing a straight line between two points on a basketball, the middle of that line is going to be air, not rubber. But if you do for a uniform ball, it is. That's the definition of uniform... Understanding this, we know that users preference is not determined by a single thing, and honestly, this fact becomes meaningful when we're talking like 5 dimensions...[0]. This fact isn't just true for normal balls, it is true for any distribution that is not uniform.
To try to put this is more English: there are 1.382 billion active iPhone users world wide. They come from nearly 200 countries. The average person in Silicon Valley doesn't want the same thing as the average person in Fresno California. Do you think the average person in Japan wants the same thing as the average Californian? The average American? The average Peruvian? Taste and preference vary dramatically. You aren't going to make a meal that everyone likes, but if you make a meal with no flavor, at least everyone will eat it. What I'm saying is that if you try to make something for everyone, you make something with no flavor, something without any soul. The best things in life are personal. The things you find most enjoyable are not always going to be what your partner, your best friends, your family, or even your neighbor finds most enjoyable. We may have many similarities, but our differences are the spice of life, they are what make us unique. It is what makes us individuals. We all wear different size pants, why would you think we'd all want to put the same magic square in our pockets (if we even have pockets). We can go deeper with the clothing or food analogy, but I think you get that a chef knows how to make more than one dish and a clothing designer knows you need to make more than one thing in different sizes and colors.
It would make reading the thread easier ..
At the very start they had the Apple1.
In addition to the GPUs (which they invented) that Nvidia designs and manufactures for gaming, cryptocurrency mining, and other professional applications, the company also creates chip systems for use in vehicles, robotics, and other tools.
The only reason they are this big right now is because they are selling H100s, mostly to other big tech companies.
The term "GPU" was coined by Sony in reference to the 32-bit Sony GPU (designed by Toshiba) in the PlayStation video game console, released in 1994.
The device in the PS1 has also been referred to as a "Geometry Transfer Engine"
You can see it's features and specs here: https://en.m.wikipedia.org/wiki/PlayStation_technical_specif...
Some may say that it is not a "real GPU" or certain features (like 3d) are missing to make it one.
The Nvidia claim is for the GeForce 256 released in 99.
This makes me wonder if our grandkids will be debating on what the first "real AI chip" was - would it be what we call a GPU like the H100 or will a TPU get that title?
I actually had one of these cards; https://en.wikipedia.org/wiki/S3_ViRGE
It sucked but it was technically one of the first "GPU"s.
Also let us not forget 3dfx and the Voodoo series cards.
Don't let Nvidia rewrite history please.
I don't want to rewrite history either.
It's partially telling that you write "2D/3D accelerators" which means that was a different class of thing - if they were a full GPU then you would have called them as such.
My point being - what defines what a GPU is? Apparently there were things called GTEs, accelerators, and so on. Some feature or invention crossed the line for us to label them as GPUs.
Just like over the last ~10 years we have seen GPUs losing graphical features and picking up NN/AI/LLM stuff to the point we now call these TPUs.
Will the future have confusion over the first ~AI CHIP~? Some conversation like
"Oh technically that was a GPU but it has also incorporated tensor processing so by todays standards it's an AI CHIP."
It's because that's what they were called at the time. Just because some one calls a rose a different name doesn't mean it doesn't smell the same.
What defines a GPU? it's a compute unit that process graphics, yes it is that simple. There were many cards that did this before Nvidia.
An A.I. chip is just a tensor processing unit a TPU, this not that hard to grasp, I think, in my opinion.
But you originally declared the Sony chip as the first GPU. There were many things that processed graphics before that, as you have declared. Apparently going back to the Amiga in the 70s.
It is this muddyness with retroactively declaring tech a certain kind that I'm questing here.
Development of the Amiga started in 1982, and it was launched in 1985.
The Tele-Games Video Arcade was released in 77 and renamed to the Atari 2600 in 82.
It is true that everything exists on a gradient, but for practical purposes we have to draw the line somewhere and it seems reasonable to me to draw it roughly here.