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Comment by toast0 | Hacker News Reader

I only observe this market from the sidelines... but

They're able to get the high end customers, and this strategy works because they can sell the high end customers high end parts in volume without having to have a good software stack; at the high end, the customers are willing to put in the effort to make their code work on hardware that is better in dollars/watts/availability or whatever it is that's giving AMD inroads into the supercomputing market. They can't sell low end customers on GPU compute without having a stack that works, and somebody who has a small GPU compute workload may not be willing or able to adapt their software to make it work on an AMD card even if the AMD card would be a better choice if they could make it work.

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They’re going to sell a billion dollars of GPUs to a handful of customers while NVIDIA sells a trillion dollars of their products to everyone.

Every framework, library, demo, tool, and app is going to use CUDA forever and ever while some “account manager” at AMD takes a government procurement officer to lunch to sell one more supercomputer that year.

Nvidia's 2024 data center revenue was $46B. They got a long fucking way to go to get to trillion dollars of product.

Take a look at this chart going back ~3Y: https://ycharts.com/indicators/nvidia_corp_nvda_data_center_...

Their quarterly data centre revenue is now $22.6B! Even assuming that it immediately levels off, that's $90B over the next 12 months.

If it merely doubles, then they'll hit a total of $1T in revenue in about 6 years.

I'm an AI pessimist. The current crop of generative LLMs are cute, but not a direct replacement for humans in all but a few menial tasks.

However, there's a very wide range of algorithmic improvements available, which wouldn't have been explored three years ago. Nobody had the funding, motivation, or hardware. Suddenly, everyone believes that it is possible, and everyone is throwing money at the problem. Even if the fruits of all of this investment is just a ~10% improvement in business productivity, that's easily worth $1T to the world economy over the next decade or so.

AMD is absolutely leaving trillions of dollars on the table because they're too comfortable selling one supercomputer at a time to government customers.

Those customers will stop buying their kit very soon, because all of the useful software is being written for CUDA only.

Did you look at your own chart? There's no trend of 200% growth. Rather this last few quarters were a huge jump from relatively modest gains the years prior. Expecting 6 years of "merely doubling" is absolutely bonkers lol

Who can even afford to buy that much product? Are you expecting Apple, Microsoft, Alphabet, Amazon, etc to all dump 100% of their cash on Nvidia GPUs? Even then that doesn't get you to a trillion dollars

Meta alone bought 350,000 H100 GPUs, which cost them $10.5 billion: https://www.pcmag.com/news/zuckerbergs-meta-is-spending-bill...

This kind of AI capital investment seems to have helped them improve the feed recommendations, doubling their market cap over the last few years. In other words, they got their money back many times over! Chances are that they're going to invest this capital into B100 GPUs next year.

Apple is about to revamp Siri with generative AI for hundreds of millions of their customers. I don't know how many GPUs that'll require, but I assume... many.

There's a gold rush, and NVIDIA is the only shovel manufacturer in the world right now.

> Meta alone bought 350,000 H100 GPUs, which cost them $10.5 billion

Right, which means you need about a trillion dollars more to get to a trillion dollars. There's not another 100 Metas floating around.

> Apple is about to revamp Siri with generative AI for hundreds of millions of their customers. I don't know how many GPUs that'll require, but I assume... many.

Apple also said they were doing it with their silicon. Apple in particular is all but guaranteed to refuse to buy from Nvidia even.

> There's a gold rush, and NVIDIA is the only shovel manufacturer in the world right now.

lol no they aren't. This is literally a post about AMD's AI product even. But Apple and Google both have in-house chips as well.

Nvidia is the big general party player, for sure, but they aren't the only. And more to the point, exponential growth of the already largest player for 6 years is still fucking absurd.

The GDP of the US alone over the next five years is $135T. Throw in other modern economies that use cloud services like Office 365 and you’re over $200T.

If AI can improve productivity by just 1% then that is $2T more. If it costs $1T in NVIDIA hardware then this is well worth it.

(note to conversation participants - I think jiggawatts might be arguing about $50B/qtr x 24 qtr = $1 trillion and kllrnohj is arguing $20 billion * 2^6 years = $1 trillion - although neither approach seems to be accounting for NPV).

That is assuming Nvidia can capture the value and doesn't get crushed by commodity economics. Which I can see happening and I can also see not happening. Their margins are going to be under tremendous pressure. Plus I doubt Meta are going to be cycling all their GPUs quarterly, there is likely to be a rush then settling of capital expenses.

Another implicit assumption is that LLMs will be SoTA throughout that period, or the successor architecture will have an equally insatiable appetite for lots of compute, memory and memory bandwidth; I'd like to believe that Nvidia is one research paper away from a steep drop in revenue.

Agreed with @roenxi and I’d like to propose a variant of your comment:

All evidence is that “more is better”. Everyone involved professionally is of the mind that scaling up is the key.

However, like you said, just a single invention could cause the AI winds to blow the other way and instantly crash NVIDIA’s stock price.

Something I’ve been thinking about is that the current systems rely on global communications which requires expensive networking and high bandwidth memory. What if someone invents an algorithm that can be trained on a “Beowulf cluster” of nodes with low communication requirements?

For example the human brain uses local connectivity between neurons. There is no global update during “training”. If someone could emulate that in code, NVIDIA would be in trouble.

Once AI becomes a political spending topic like green energy, I think we’ll see nation level spending. Just need one medical breakthrough and you won’t be able to run a political campaign without AI in your platform.

I'd guess that the majority of ML software is written in PyTorch, not in CUDA, and PyTorch has support for multiple backends including AMD. torch.compile also supports AMD (generating Triton kernels, same as it does for NVIDIA), so for most people there's no need to go lower level.

GPUs are used for more than only ML workloads.

CUDA relevance in the industry is so big now, that NVidia has several WG21 seats, and helps driving heterogenous programming roadmap for C++.

You can use PyTorch for more than ML. No need to use backprop. Thinks of it as GPU accelerated NumPy.

I would like to see OctaneRender done in Pytorch. /s

Sure, but if the OctaneRender folk wanted to support AMD, then I highly doubt they'd be interested in a CUDA compatability layer either - they'd want to be using the lowest level API possible (Vulkan?) to get close to the metal and optimize performance.

See, that is where you got all wrong, they dropped Vulkan for CUDA, and even made a talk about it at GTC.

https://www.cgchannel.com/2023/11/otoy-releases-first-public...

https://www.cgchannel.com/2023/11/otoy-unveils-the-octane-20...

And then again, there are plenty of other cases where Pytorch makes absolute no sense in GPU, which was the whole starting point.

> See, that is where you got all wrong

I said that if they wanted to support AMD they would use the closest-to-metal API possible, and your links prove that this is exactly their mindset - preferring a lower level more performant API to a higher level more portable one.

For many people the tradeoffs are different and ability to write code quickly and iterate on design makes more sense.

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