[HN Gopher] AI: Nvidia Is Taking All the Money
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AI: Nvidia Is Taking All the Money
Author : TradingPlaces
Score : 53 points
Date : 2023-06-06 15:54 UTC (7 hours ago)
(HTM) web link (seekingalpha.com)
(TXT) w3m dump (seekingalpha.com)
| gumballindie wrote:
| I am surprised that AMD is lagging behind so badly. Their GPUs
| have plenty of ram available yet they are pretty useless for ml.
| javchz wrote:
| The main issue it's CUDA. Most ML developers use it, or use
| libraries that depends on CUDA as an envoirments.
|
| There are brand agnostic alternatives, but they are less
| popular and usually slower.
| LegitShady wrote:
| I don't think its CUDA. If it was CUDA people would use AMDs
| Radeon Open Compute and save tons of money on cards with more
| memory.
|
| The issue is that the Radeon platform isn't well developed,
| and AMD is charging as much as they can get for their cards
| just like Nvidia. if they had working competitive compute
| they'd also charge as much as Nvidia, and make more money on
| fewer cards sold.
|
| I think there's just something with card architecture that
| makes it not as good as Nvidia for this purpose. Just like
| mining bitcoins or VR, sometimes card architecture makes a
| difference.
| WeMoveOn wrote:
| Eh its more so that the entire ecosystem is built on CUDA. I
| don't think most developers would care or even notice if you
| swapped out CUDA for something that'll work seamlessly with
| the existing Python libraries. Sadly such an alternative
| doesn't exist and probably won't exist.
| TradingPlaces wrote:
| OP here. That's exactly right. In the article, I describe
| CUDA and the rest of the software suite as their competitive
| moat to hardware competition from AMD or AI accelerators.
| p4ul wrote:
| Yes, CUDA is their moat. And it's a very deep moat, filled
| with sharks, alligators, and mines. It will take a truly
| massive effort to unseat CUDA and NVIDIA as the dominant
| software/hardware tools for deep learning.
|
| I'm curiously watching Intel and their OneAPI platform; but
| it just feels like they're starting from so far behind. And
| Intel hasn't exactly had a stellar few years on top of
| that.
| lmpdev wrote:
| I really hope CUDA doesn't end up being the PostScript of ML
| adam_arthur wrote:
| There wasn't a strong incentive to get either ML training or
| inference working on non-nvidia devices before. You can sure
| bet there is now though.
|
| To expect that NVDA will be the only player for years to come
| is quite likely to be proven false. Google already has their
| foot in this game too via their TPU. The financial incentives
| have recently gotten an order of magnitude stronger
| [deleted]
| bob1029 wrote:
| I have a tiny suspicion that a new technique is going to emerge
| soon that will cause some frustration for Nvidia's investors. For
| instance, observing how little quantization error seems to matter
| makes me believe there are probably some other massive wins
| lurking throughout. Before I'd invest, I'd consider the
| "doomsday" scenario wherein someone develops a technique for
| running an OpenAI, GPT-4-scale model on a MacBook or iPhone.
|
| I know it probably seems impossible to many on HN that there
| could be another bucket of 3-4 orders of magnitude sitting on the
| table, but progress over the last ~6 months seems to provide a
| compelling argument against that point of view.
|
| There are also radically-different architectures that have seen
| zero serious effort put towards them. Mostly things that are CPU-
| bound. In my view, GPU is starting to cause more harm than good
| with regard to development of new concepts for problem solving.
| There are neural network architectures that simply don't work
| well across the PCIe bus. Eventually, someone is going to start
| playing around with these ideas as GPU scarcity rages onwards.
| m463 wrote:
| I thought the whole deal with machine learning was that
| inference is easy, but training is hard.
|
| So creating the models from the data is what takes the billions
| of transistors on many cards.
|
| running the models doesn't need so much hardware.
|
| also... with respect to powerful processing - I haven't seen
| graphics cards tapering off. There's a ravenous demand for
| better graphics hardware each year. For every technique that is
| commoditized, the next year there's a new way of doing things
| that is better. I remember things like lighting, or realistic
| hair, or physics or whatever making a new graphics card better.
| Why wouldn't AI stuff be any different?
| paulddraper wrote:
| 1. There is not a 3-4 order of magnitude improvement on the
| table. 1? Maybe.
|
| 2. If AI gets more capable/efficient, the demand will increase
| not decrease.
|
| Did weapons manufactures go out of business when machine guns
| were invented?
| heyitsguay wrote:
| Yeah but the challenge lately has been that efficiency gains
| have also made the capabilities of large compute clusters even
| more powerful. Transformers were introduced as an efficient
| alternative to RNNs and conv nets, then it turned out that
| however much they supercharged a single GPU, they were
| dramatically more powerful deployed at datacenter scale.
| Tuna-Fish wrote:
| > I'd consider the "doomsday" scenario wherein someone develops
| a technique for running an OpenAI, GPT-4-scale model on a
| MacBook or iPhone.
|
| That's not a doomsday scenario for Nvidia. There is essentially
| infinite demand for better AI, only limited by what can be
| provided at acceptable cost. If you can run GPT-4 on a macbook,
| you can do even better with a more massive model. If you can
| run a good image model on a macbook, then the next frontier is
| running a good video model, etc.
|
| The real doomsday scenario for Nvidia is that there seems to
| very little differentiating their hardware. Their lead in the
| space exists because they developed good software support
| early, which lead to everyone standardizing on them. But AI is
| not like graphics where the problem domain is complex and the
| APIs are very ill-defined, and you can do all these tricks to
| make it faster and better. Instead, AI is almost entirely doing
| just a handful of very simple operations. Other vendors should
| be capable of providing good software support eventually, and
| at that point, what is Nvidia's moat? What justifies their
| margin?
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