351–360 of 385 posts
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I suspect major algorithmic breakthroughs would accelerate the demand for GPUs instead of making it fall off, since the cost to apply LLMs would go down.
Some changes to the algorithms and implementations will allow cheaper commodity hardware to be used.
It goes to nearly zero if China invades Taiwan, and that seems like it has at least a 10% chance of happening in the next year or two.
If China does invade Taiwan, I feel like most people are going to have bigger problems than the Nvidia stock price.
It is economic MAD.
Or China can wait 20-30 years and the US will no longer care about Taiwan or have the resources to have much presence in the eastern hemisphere.
I think the saber rattling over Taiwan is just to get the US to spend themselves further into oblivion in the short term. We are in the war already and the saber rattling is an incredibly effective, asymmetric financial weapon. It builds up the Chinese military kinetic capacity long term while weakening the US military kinetic capacity long term by forcing the US to prepare for something that is never going to happen.
When China takes Taiwan it will be without firing a shot. I would bet the house on that because it kind of has to be that way to win the war and not just a self destructive battle.
China is achieving its objectives brilliantly. The US is increasingly isolated and this is the process of retreating into the western hemisphere. NATO is being destroyed without firing a shot.
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…
This is like saying Apple stock will inevitably slide once everybody owns a smartphone.
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You'd be surprised how quickly improvement of autoregressive language models levels off with epoch count (though, admittedly, one epoch is a LOT). Diffusion language models otoh indeed keep profiting for much longer, fwiw.
Does this also apply to LLM training at scale? I would be a bit surprised if it does, fwiw.
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Doesn't even necessarily need to be CUDA compatible... there's OpenCL and Vulkan as well, and likely China will throw enough resources at the problem to bring various libraries into closer alignment to ease of use/development. 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 wi…
> there's OpenCL and Vulkan as well OpenCL is chronically undermaintained & undersupported, and Vulkan only covers a small subset of what CUDA does so far. Neither has the full support of the tech industry (though both are supported by Nvidia, ironically). It feels like nobody in the industry wants to beat Nvidia badly enough, yet. Apple and AMD are trying to supplement raster hardware with inference silicon; both of…
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I’ll be so happy to buy a EOL H100! But no, there’s none to be found, it is a 4 year, two generations old machine at this point and you can’t buy one used at a rate cheaper than new.
Not sure why this "GPUs obsolete after 3 years" gets thrown around all the time. Sounds completely nonsensical.
According to nvidia’s 2025 annual report [1], 34% of their sales for 2025 comes from just 3 customers. Additionally, they mentioned that customers can cancel purchases with little to no penalty and notice [2]. This is not unique for hardware companies, but to think that all it takes is just one company to get their sales down by 12% (14b$). To cut to the point, my guess is that nvidia is not sustainable, and at some…
None of those customers can afford to cancel their orders. OpenAI, Google and Meta cannot afford to get cheap on GPUs when presumably they believe GAI is around the corner. The first company to achieve GAI will win because at that point all gains will become exponential. All the AI companies are locked in a death loop where they must spend as much money as possible otherwise everything they invested will immediately…
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Run out of training data? They’re going to put these things in humanoids (they are weirdly cheap now) and record high resolution video and other sensor data of real world tasks and train huge multimodal Vision Language Action models etc. The world is more than just text. We can never run out of pixels if we point cameras at the real world and move them around. I work in robotics and I don’t think people talking about…
Yeah, another source of "unlimited data" is genetics. The human reference genome is about 6.5 GB, but these days, they're moving to pangenomes, wanting to map out not just the genome of one reference individual, but all the genetic variation in a clade. Depending on how ambitious they are about that "all", they can be humongous. And unlike say video data, this is arguably a language. We're completely swimming in unma…
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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…
Nvidia is moving to a 1 year release life cycle for data center, and in Jensen's words once a new gen is released you lose money for being on the older hardware. It makes no longer financially sense to run it.
Companies can’t buy new Nvidia GPUs because their older Nvidia GPUs are obsolete. However, the old GPUs are only obsolete if companies buy the new Nvidia GPUs.