271–280 of 385 posts
Earlier quoted context omitted.
China is restricting purchases of H200s. The strong likelihood is that they're doing this to promote their own domestic competitors. It may take a few years for those chips to catch up and enter full production, but it's hard to envision any "trillion dollar" Nvidia defense empire once that happens.
It's very easy to envision. America needs chips, and Intel can't do most of this stuff.
Earlier quoted context omitted.
I really don't understand the argument that nvidia GPUs only work for 1-3 years. I am currently using A100s and H100s every day. Those aren't exactly new anymore.
Not saying your wrong. A few things to consider: (1) We simply don't know what the useful life is going to be because of how new the advancements of AI focused GPUs used for training and inference. (2) Warranties and service. Most enterprise hardware has service contracts tied to purchases. I haven't seen anything publicly disclosed about what these contracts look like, but the speculation is that they are much more…
This article goes more into the technical analysis of the stock rather than the underlying business fundamentals that would lead to a stock dump. My 30k ft view is that the stock will inevitably slide as AI datacenter spending goes down. Right now Nvidia is flying high because datacenters are breaking ground everywhere but eventually that will come to an end as the supply of compute goes up. The counterargument to th…
I hear your argument, but short of major algorithmic breakthroughs I am not convinced the global demand for GPUs will drop any time soon. Of course I could easily be wrong, but regardless I think the most predictable cause for a drop in the NVIDIA price would be that the CHIPS act/recent decisions by the CCP leads a Chinese firm to bring to market a CUDA compatible and reliable GPU at a fraction of the cost. It shoul…
I do think China is still 3-5 years from being really competitive, but still even if they hit 40-50% of NVidia, depending on pricing and energy costs, it could still make significant inroads with legal pressure/bans, etc.
Earlier quoted context omitted.
Who wants to buy GPUs that were redlined for three years in a data center? Maybe there's a market for those, but most people already seem wary of lightly used GPUs from other consumers, let alone GPUs that were burning in a crypto farm or AI data center for years.
> Who wants to buy who cares? that's the beauty of the lease. once it's over, the old and busted gets replaced with new and shiny. what the leasing company does is up to them. it becomes one of those YP not an MP situations with deprecated equipment.
Earlier quoted context omitted.
Even if LLMs didn't advance at all from this point onward, there's still loads of productive work that could be optimized / fully automated by them, at no worse output quality than the low-skilled humans we're currently throwing at that work.
inference requires a fraction of the power that training does. According to the Villalobos paper, the median date is 2028. At some point we won't be training bigger and bigger models every month. We will run out of additional material to train on, things will continue commodifying, and then the amount of training happening will significantly decrease unless new avenues open for new types of models. But our current LL…
It isn’t the panacea some make it out to be, but there is obvious utility here to sell. The real argument is shifting towards the pricing.
Earlier quoted context omitted.
Not sure why this "GPUs obsolete after 3 years" gets thrown around all the time. Sounds completely nonsensical.
I agree that there is hyperbole thrown around a lot here and its possible to still use some hardware for a long time or to sell it and recover some cost but my experience in planning compute at large companies is that spending money on hardware and upgrading can often result in saving money long term. Even assuming your compute demands stay fixed, its possible that a future generation of accelerator will be sufficien…
> One of the questions of the 2026 acx prediction contest is whether Nvidia’s stock price will close below $100 on any day in 2026. Maybe I’m missing something, but isn’t this just a standard American put option with a strike of $100 and expiry of Dec 31st?
Earlier quoted context omitted.
inference requires a fraction of the power that training does. According to the Villalobos paper, the median date is 2028. At some point we won't be training bigger and bigger models every month. We will run out of additional material to train on, things will continue commodifying, and then the amount of training happening will significantly decrease unless new avenues open for new types of models. But our current LL…
> We will run out of additional material to train on This sounds a bit silly. More training will generally result in better modeling, even for a fixed amount of genuine original data. At current model sizes, it's essentially impossible to overfit to the training data so there's no reason why we should just "stop".
Earlier quoted context omitted.
I’m sad about Grok going to them, because the market needs the competition. But ASIC inference seems to require a simpler design than training does, so it’s easier for multiple companies to enter. It seems inevitable that competition emerges. And eg a Chinese company will not be sold to Nvidia. What’s wrong with this logic? Any insiders willing to weigh in?
I'm not an insider, but ASICs come with their own suite of issues and might be obsolete if a different architecture becomes popular. They'll have a much shorter lifespan than Nvidia hardware in all likelihood, and will probably struggle to find fab capacity that puts them on equal footing in performance. For example, look at the GPU shortage that hit crypto despite hundreds of ASIC designs existing. The industry badl…
This article goes more into the technical analysis of the stock rather than the underlying business fundamentals that would lead to a stock dump. My 30k ft view is that the stock will inevitably slide as AI datacenter spending goes down. Right now Nvidia is flying high because datacenters are breaking ground everywhere but eventually that will come to an end as the supply of compute goes up. The counterargument to th…
Actually "technical analysis" (TA) has a very specific meaning in trading: TA is using past prices, volume of trading and price movements to, hopefully, give probabilities about future price moves.
https://en.wikipedia.org/wiki/Technical_analysis
But TFA doesn't do that at all: it goes in detail into one pricing model formula/method for options pricing. In the typical options pricing model all you're using is current price (of the underlying, say NVDA), strike price (of the option), expiration date, current interest rate and IV (implied volatility: influenced by recent price movements but independently of any technical analysis).
Be it Black-Scholes-Merton (european-style options), Bjerksund-Stensland (american-style options), binomial as in TFA, or other open options pricing model: none of these use technical analysis.
Here's an example (for european-style options) where one can see the parameters:
https://www.mystockoptions.com/black-scholes.cfm
You can literally compute entire options chains with these parameters.
Now it's known for a fact that many professional traders firms have their own options pricing method and shall arb when they think they find incorrectly priced options. I don't know if some use actual so forms of TA that they then mix with options pricing model or not.
> My 30k ft view is that the stock will inevitably slide as AI datacenter spending goes down.
No matter if you're right or not, I'd argue you're doing what's called fundamental analysis (but I may be wrong).
P.S: I'm not debatting the merits of TA and whether it's reading into tea leaves or not. What I'm saying is that options pricing using the binomial method cannot be called "technical analysis" for TA is something else.