Jlagreen

6 ms·

31.
by Jlagreen 1y ago
Actually, Nvidia has already built the ecosystem. Now, they are refining and adapting it to the fast research in AI. Others talk about chips when Nvidia thought about interconnects 8 years ago. Today, competitors try to catch up on this whi
32.
by Jlagreen 1y ago
And yet, Nvidia innovates in gaming with SW 10x more than anyone. Strange side business it is which is also worth billions by the way. Did you know that Nvidia has a gaming cloud running which might become the largest in the world at some t
33.
by Jlagreen 1y ago
Because Nvidia focuses on being a partner in each industry you mention. See it that way, if you have an OS/SW for all the industries you mention then who is your competitor? Not the participants in that industries. Nvidia can partner w
34.
by Jlagreen 1y ago
One of the most important contributors to QC is Nvidia because they try to help with GPUs in creating SW for QC and simulations. Nvidia has cuda quantum for years now.
35.
by Jlagreen 1y ago
Yes, but the cloud customers "who finance TPUs" have NO INTEREST in TPUs and in Nvidia GPUs instead. How does Google pay for TPUs internally? By Google Search and Google Cloud of course. Google Search uses TPUs, Google Cloud howev
36.
by Jlagreen 2y ago
Yes, we can see how Big Tech and Apple's huge margins get compressed all the time, they never expand. They are going to zero soon it seems :( You can't say that in general because it also depends on the moat. Apple has 75% profits
37.
by Jlagreen 2y ago
What are you talking about? Nvidia has been a high margin and great performing business for the last decade. Nvidia had better gross margins than apple by selling gaming GPUs only 10 years ago and that's in a market where you can easil
38.
by Jlagreen 2y ago
The way I see it is that Nvidia might reacht $1 trillion in revenue before AMD reaches $100 billion in revenue. So the upside in revenue growth is higher for Nvidia. People think that because a company has grown very large very quickly that
39.
by Jlagreen 2y ago
Ah, I'm sorry you're right a misunderstood your comment. I agree and Nvidia positions itself for exactly that. See how fast DeepSeek will come to NIM. People are already wondering how well DeepSeek will run on Digits. Nvidia also
40.
by Jlagreen 2y ago
That was never the moat. Nvidia's moat is controlling compute for 80-90% of workloads. Why do you think Steam has more RTX 4090 than 4080 in their survey? Ever since RTX 2080TI you could buy multi server consumer GPUs for ML. We sell P
41.
by Jlagreen 2y ago
Nvidia had 65% margins 5 years ago when they primarily sold gaming GPUs. What margins do you think Nvidia is charging for RTX which getting more and more expensive every generation? What margins do you think Project Digits will be for Nvidi
42.
by Jlagreen 2y ago
Without Nvidia, you would still wait for ChatGPT moment and not even think about AI at all. I have been invested in Nvidia for 9 years and I have not only witnessed what Nvidia has done in ML/AI but also how the entire field evolved. T
43.
by Jlagreen 2y ago
So the focus is trying to find things where LLMs are bad instead of trying to find out where they are good and find applications for that? That's basically like trying to embarass a IQ 180 student on emotional intelligence. But I guess
44.
by Jlagreen 2y ago
is llama405 a distilled model like DeepSeek or a trained frontier model? I honestly ask because I haven't researched but that's important to know before one compares.
45.
by Jlagreen 2y ago
For 2 decades we were told how Apple will have to cut their margins due to competition and so on. Today, it's simple. Apple has 25% unit share in smartphone markets and 75% profit share. Apple makes 3x the profit of ALL OTHER smartphon
46.
by Jlagreen 2y ago
Nvidia is already doing that. What needed 1000k of Voltas, needed 100k of Amperes, needed 10k of Hopper, will need 1k of Blakwell. Nvidia has increased compute by a factor of 1 million in the past decade and it's no where near enough.
47.
by Jlagreen 2y ago
The good thing is: Blackwell DC is $40k per piece and Digits is $3k per piece. So if 13x Digits are sold then it's the same turnover as a DC GPU for Nvidia. Yes, maybe lower margin but Nvidia can easily scale digits into masses compare
48.
by Jlagreen 2y ago
And yet, Blackwell is sold out. What does that tell us? The industry is compute starved and that makes totally sense. The tranformer model on which current LLMs are based on are 8 years old. But why took it so much time to get to the LLMs o
49.
by Jlagreen 2y ago
The $5M was the cost of the training itself. You can rent 10k H100 for 20 days with that money. Go and knock yourself out because that compute is probably higher than what DeepSeek received for that money. And that is public cloud pricing
50.
by Jlagreen 2y ago
Your implication is that we have unlimited compute and therefore know that LLMs are stalled. Have you considered that compute might be the reason why LLMs are stalled at the moment? What made LLMs possible in the first place? Right, compute
51.
by Jlagreen 2y ago
That's wrong. DeepSeek is a problem for Big Tech, not for Nvidia. Why? Imagine a small startup can do something better than Gemini or ChatGPT or Claude. So it can be disruptive. What can Big Tech do to avoid disruption? Buying every SI
52.
by Jlagreen 2y ago
No, Nvidia's margins won't drop at all and the proof for this is Apple. The units of AI accelerators will explode, the market will explode. At the end of the day, Nvidia will have 20-30% of the unit share in AI HW and 70-80% of th
53.
by Jlagreen 2y ago
My entry into Nvidia is 2016, my portfolio has never been red since then.
54.
by Jlagreen 2y ago
Deepseek was trained on Nvidia GPUs H800. Nvidia is still selling GPUs to China and the reason even the reduced chip performance is easily negated by scaling. US government is stupid because they asked for certain limits on chip base first
55.
by Jlagreen 2y ago
I would shoot myself if I had to wait 9 seconds for a query. I sometimes even would like to kill by browser taking seconds to open a page...
56.
by Jlagreen 2y ago
Yes, so Chinese did what they are best at, taking something working (foundational model which is free), copying it and improving it. Nothing special to see here. The good thing is that Chinese models are dependent on open sourced US models.
57.
by Jlagreen 2y ago
What do you think ASI will be? We will only advance in AI if one day AI learns from itself otherwise we don't need AI. Because AI learning from itself at X times faster than humanity is the reason we want to get there in the first plac
58.
by Jlagreen 2y ago
That's why the shovel maker from back then are selling mining machines today. Everyone here thinks Nvidia is dommed because of training efficiency. But what has Nvidia been doing for the past decade? Correct increasing training and inf
59.
by Jlagreen 2y ago
THIS is the only correct statement in all of this. The goal for AGI and ASI MUST BE to train, inference, train, inference and so on and that all on the fly in fractions of a second from every token produced. Now good luck calculating the co
60.
by Jlagreen 2y ago
The issue is it feels like we came to a stop but Hyperscalers are simply waiting for Blackwell. That's all. Why buy 100k Hoppers if 20k Blackwell offer the same compute so then it's better to buy 100k Blackwells right? Backwell wi

More ›