4 ms·

+1. My favorite essay on the topic: https://petewarden.com/2023/09/10/why-nvidias-ai-supremacy-is-only-temporary/ https://petewarden.com/2023/09/10/why-nvidias-

• gitfan86 3y ago
  I sold my GOOG shares from 2005 to buy NVDA last year and definitely agree with the article. The thing is that Wall St doesn't understand any of the technical details and will just see huge profit growth from NVDA and drive the price crazy high.
  • Mistletoe 3y ago
    I don’t understand; hasn’t that already happened and you need to get out now?
    • gitfan86 3y ago
      We are still at the beginning of Wall St freaking out
      • Mistletoe 3y ago
        NVDA has a PE of 80 and is up 6x since October 2022. It's the third highest company by market cap in the SP500, how much higher do you think it could possibly go?
        • pokepim 3y ago
          [dead]
        • gitfan86 3y ago
          It will keep going up until a Wall St analyst puts in an insane price target like $5000. Then it will be time to sell. It should follow AMZN from the dotcom bubble.
• anonylizard 3y ago
  The thesis of that essay, is that 1. Nvidia GPUs are dominant at training 2. Inference is easier than training, so other cards will become competitive in inference performance. 3. As AI applications start to proliferate, inference costs will start to dominate training costs. 4. Hence Nvidia's dominance will not last. I think the most problematic assumption is 3. Every AI company we see thus far is locked in an arms race to improve model performance. Getting overtaken by another company's model is very harmful for business performance (See Midjourney's reddit activity after DALLE-3), while a SOTA release instantly results in large revenue leaps. We also haven't reached the stage where most large companies can fine-tune their own models, given the sheer complexity of engineering involved. But this will be solved with a better ecosystem, and will then trigger a boom in training demand that does scale with the number of users. Will this hold in the future? Not indefinitely, but I don't see this ending in say 5 years. We are far from AGI, so scaling laws + market competition mean training runs will grow just as fast as inference costs. Also, 4 is very questionable. Nvidia's cards are not inherently disadvantaged in inference, they may not be specialized ASICs, but are good enough for the job with an extremely mature ecosystem. The only reason why other cards can be competitive against Nvidia's cards in inference, is because of Nvidia's 70% margins. Therefore, all Nvidia needs, to defend against attackers, is to lower their margins. They'll still be extremely profitable, their competitors not so much. This is already showing in the A100 H100 bifurcation. H100s are used for training, while the now old A100s used for inference. Inference card providers will need to compete against a permanently large stock of retired Nvidia training cards. Apple is still utterly dominant in the phone business after nearly 2 decades. They capture the majority of the profits despite 1. Not manufacturing their own hardware 2. The majority of the market share by units sold is say Chinese/Korean If inference is easy, while training is hard. It could just lead to Nvidia capturing all the prestigious and easy profits from training, while the inference market is a brutal low margin business with 10 competitors. This will lead to the Apple situation.
  • anon291 3y ago
    I also think it's ludicrous to think that NVIDIA hasn't witnessed the rise of alternate architectures and isn't either actively developing them (for inference) or seriously deciding which of the many startups in the field to outright buy.