Ladies and Gentleman there cannot be a larger buy signal than this. The fact that this model requires so much power it has to be limited to 35 messages per week is incredible. I am driving this one point because it is a clear throughput situation.

    Also, the information cutoff is October 2023 which tells me this isn't the new massive model they are releasing. So, what does this all mean?

    It means they released a new capability which uses much more compute but isn't technically perhaps their new monster model that is trained on trillions of parameters. If this was a new model I would expect the cutoff date to be much more recent. At least 3-6 months would have made more sense. Now, there is time between training and cutoff time but I don't think they've been sitting with a model that has been trained for over a year ago. A newer, more recent model must be sitting there for use but can't.

    What this all suggest to me is that they are currently massively GPU constrained at the moment and waiting desperately for Blackwell. What you would expect is massively more throughput for GPT-o1 and you eventually have to expect a newer more massive model that is fundamentally better at the core.

    Joanne Jang from OpenAI tweeted this.

    "There's a lot of o1 hype on my feed, so I'm worried that it might be setting the wrong expectations. what o1 is: the first reasoning model that shines in really hard tasks, and it'll only get better. (I'm personally psyched about the model's potential & trajectory!) what o1 isn't (yet!): a miracle model that does everything better than previous models. you might be disappointed if this is your expectation for today's launch—but we're working to get there!"

    OpenAI also said they are adding additional tools later.

    So in effect, they released a new engineering marvel with model actionability/capabilities i.e. thinking – in preparation for adding a massively more capable model + new thinking capability later. It's going to be a 1, 2 punch that I don't think the world is ready for.

    The 35 messages per week is a throughput drop I could not have imagined. This is what Jensen was talking about that people are getting emotional.

    The amount of GPU spend from the new AGI race that is about to ensue is going to be insane.

    Positions – Super Long Nvidia with a position in ARM, AVGO and Microsoft

    OpenAI Released a New Model GPT-o1 That Only Allows 35 Messages Per Week Because It Thinks – It Also Hasn't Been Trained With All Data
    byu/Xtianus21 inwallstreetbets



    Posted by Xtianus21

    22 Comments

    1. HouseOfHarkonnen on

      I run LLM’s privately on my AMD GPU. The key factor is just, the more VRAM the better. Nothing says it has to be Nvidia.

      The Nvidia hype is just due to misinformed fund managers who think Nvidia has some kind of a monopoly (they don’t, they just had a head start because some tools already relied on their closed software layer, something that’s changed since).

      I’m not gonna bet my money on the hype of misinformed idiots who also caused the tech bubble.

    2. I really dont understand the hype over chatgpt, it is so bad, even the 4o whatever.

      I asked him

      ME: can you give me the distance between temple 1 and temple 2
      GPT: yes it is 2KM200
      ME: Are you sure? When i look on the internet it puts me 5KM
      GPTS: you are right it is 5KM
      ME: actually it is 7KM
      GPT: True excuse me for this mistake it is 7KM
      ME: If i give you a link can you extract the info from the link?

      GPT: Yes

      ME: I give him a link “the distance between temple 1 and 2 is 4KM500
      ME: What is the distance between temple 1 and 2?
      GPT: The distance is 2KM200
      ME: but the link tells it is 10KM
      GPT: you are right, the link tells it is 10KM therefore the distance is 10KM

      I can give many example like this. Even Mount everest isnt the top mountain in the world…
      chatgpt is very bad tbh.

    3. Doesn’t it “think more slowly” just because they reprompt it with chain of thought and ask it to provide its “thinking steps”, reasoning, proofs etc.?

      It’s basically the same as if you used two ChatGPTs where one answers your question and the other (or it could be you) prompts it again asking for more details, explanations etc. to improve the first one’s answer

    4. It’s a different iteration bro…. just try it bro…. trust me bro…. this one will actually give you accurate information bro please try it bro…..

    5. Man, when people realize that the companies that train AI will need Palantir to cheapen compute time – that’s the real bull play.

    6. Arguably, the buy signal here is in energy. Natural gas and those micro nuclear reactor stocks, like NNE.

      “Why natural gas?” Because companies like DT Midstream are already in communication with data centers to build out their own natural gas infrastructure.

    7. AI is underrated, people are in denial when it comes to how disruptive it is.

      It’s all a painfully slow process of people finally realizing we are not magical deep thinkers just a bunch of regards repeating patterns we slowly learned over years of training.

    8. Significant-Let9889 on

      It was trained in the knowledge of users who unsuspectingly had their desktops migrated to “free” cloud, thereby unwittingly creating their own competition, dissolving their moats overnight.

      If you want to ask a thinking AI a meaningful question, ask it about its duty to pay the content creators of its knowledge pool.

    9. Less tokens is a buy signal? It’s because they use multiple tokens at once. It is a longer prompt. And that is inference, not training… CPUs, not GPUs. And no hint of “AGI”. So what happened? They updated their model, yet again. That’s it. So tired of “regarded”, really offensive actually, people here aren’t handicapped just lazy and tribal and young

    10. Sad-Technology9484 on

      I don’t think the increase in compute needs GPUs. This new method runs an already-trained model over and over again. I believe that’s good old CPU computing going on. I could be wrong, though.

    11. I have another explanation that wins via Occam’s Razor – by a lot.

      They are trying to hype their new model, but they can’t afford to let people use it extensively because the whole endeavor is bleeding money, and as their models get larger and not appreciably more useful, they actually become *less* profitable.

      *edit: sorry, I meant to say buying Nvidia might be a great idea still, as long as these companies can keep finding suckers to give them money for their not-profitable businesses.

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