Ask HN: Confused about how DeepSeek hurts Nvidia
I’m genuinely confused about why people think Deepseeks results will mean fewer GPUs being needed in the future. DeepSeek won’t be top dog forever. At some point, all their big competitors will figure out how they created their model, copy the approach, and get the same efficiencies. After that, why wouldn’t every competitor add more compute to go beyond DeepSeek’s capabilities and each other? Is there some experimental evidence out there that having 10X or 100X the compute DeepSeek used for training wouldn’t result in a much more advanced model?
49 comments
[ 3.7 ms ] story [ 114 ms ] threadI agree with this in the sense that no model will be top dog forever. However, it's important to note their contributions to open source. They're raising the bottom bar, and that is important.
So noting your long term investment ideas seem plausible, what do you think is the immediate short term impact on this kind of spend? Do you think Nvidia will sell more or less units in the next reporting interval? Because thats what most people are reacting to.
It would not surprise me if there are plenty of willing buyers, looking to buy in a dip and sell on the inevitable upward swing.
I am not a direct investor. I have no idea what my pension fund did, if anything.
Many small companies, which would never think about training models in house, could now do it.
I see this will only boost the AI hardware market.
US's anti-China policies further forces China to develop their own GPUs, breaking Nvidia's dominance within 10 years or something.
US government is stupid because they asked for certain limits on chip base first but AI uses GPU clusters. In a GPU cluster you don't have full utilization anyway so slower GPUs don't matter as much as slower networking. China still gets pretty high bandwith Nvidia HW for building large clusters for training/inferencing.
Chips from Huawei still seem to be way too unstable for the job. In training/inference stability is even more important than performance. Imagine you have a fast chip but it can't run without errors for 2 months and you training never gets done. That's DoA.
Why do you except Google and Anthropic?
Cisco kept making and selling network hardware, and probably (citation needed) sold more from 2000-2006 than 1994-2000, but the stock trade was over. The web did become a serious thing, but only once people got broadband at home.
The Nvidia valuation was getting pretty weak. Lots of FAANGs with deep pockets started to invest in their own hardware, and it got good enough to start beating Nvidia. Intel and AMD are still out there and under pressure to capture at least some of the market. Then this came along and potentially upended the game, bringing costs down by orders of magnitude. It might not be true, and it might even drive up sales long-term, but for now, but the NVDA trade was always a short-term thing.
NVDA has been going up for the last 10 years (with 2022 being the only exception).
AI today is better than anyone could hope for, and I don’t see any reasons to not expect further advances.
I hope for an AI that can actually reason and doesn't bullshit its users though
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.
To say Nvidia is toast is like saying US is toast after Sovjets send a rocket into space.
Anything else, you can just google it because 9/10 there's a SO thread about it, and when there isn't, the documentation is usually good enough. GPT is not going to help you there.
But you can see it in the small things. For example if you ask an intelligent person "how do I install X on docker?" it will search google and then find a tutorial or some resource to reference. Then they will break it down and adapt it to your scenario.
GPT instead will give you... whatever it feels like giving you in that moment. It will not look on the f*n internet to make sure that what it says is correct and up to date. It does not update an internal knowledge base with factual information that it can then reference to produce a coherent plan. It has no concept of truth so it can't use logic.
You can throw as much compute and chain of thought as you want to the problem, it's the architecture itself that is disappointing.
* DeepSeek appears to be credible evidence there may be clever optimizations to achieve higher model quality with less GPU cycles than previously thought. Basically, if you're making scarce oil derricks in a gasoline shortage and your stock price has been bid way up on the expectation of insatiable future gas demand, a more gas-efficient engine design is going to be adverse to your valuation. Especially if it's free and easy to implement.
* DeepSeek's weights are open source under a permissive license. Much of OpenAI (and similar company's) current revenue is from AI startups and other companies buying usage hours of proprietary leading edge models (eg O3) as cloud services through an API and reselling the output in their own applications targeting various verticals. If some of those companies start using a free open source model like DeepSeek (or it's future descendants/competitors) for some of their offerings - that'll reduce the income and war chest of some of today's biggest GPU buyers. Lower current revenue lowers valuations meaning the equity OpenAI et al use to buy GPUs will be devalued.
It's not just hardware though: you can't run CUDA on non-Nvidia hardware, which in my understanding is a major moat for Nvidia. I'd love to hear rebuttals on this though, because GPU programming is something I've only dabbled with.
From what I've read, most of the investments by FAANGs/startups in building specialised hardware has been in the inference space.
Most of the biggest Nvidia clients are valued on speculation of future revenue from their closed models (secret sauce). Deepseek is fully open source so those revenue expectations crashed and investors are having second thoughts on throwing more money at companies like OpenAI. And this hits the expected sales growth of Nvidia for the next few years.
Dark fibre eventually was used but it took many years. And it was bought for cheap by companies like Google and CloudFlare.
[1] https://en.wikipedia.org/wiki/Wavelength-division_multiplexi...
[2] https://en.wikipedia.org/wiki/Dark_fibre
[3] https://arxiv.org/html/2412.19437v1 (3.2.1DualPipe and Computation-Communication Overlap)
no joke, the hype around DWDM is why I got into networking -- waves are the future, man!
It's always funny when someone asks me "So how did you know so far ahead of everyone else [that (my startup's product) would launch (category)]", and I tell them it's because we were inexperienced and didn't fully understand the market, so we guessed based on assumptions which turned out to be wrong. I try to always fess up to the fact that success in tech startups is a combination of insanely hard work, being (mostly) smart, luck, and being stubborn enough to keep trying again after failing until your luck turns.
I am actually interested to see those who hold this view explain their logic here further. It's quite an interesting, if unorthodox take because we would think that algorithmic brilliance is not a moat, even when the code is not open source.
Which is... worse for Nvidia? If someone else disrupts DeepSeek, do they train a similarly performing model for $600k?
Deepseek's cheaper LLM services + providing open models for other hosts to provide
=> overall prices for using LLM services will fall due to competition (lower prices + more hosts entering the market); AI users won't pay so much for LLM services
=> LLM hosts/providers won't be able to project such high revenues or even purchase as many GPUs (and will receive less capital investment to buy GPUs since revenues per dollar invested are lower)
=> demand for and prices of Nvidia cards will fall
On the basis of this possible logic, portfolio managers and algorithms project lower growth/revenue for Nvidia and sell off its stock, setting off the usual chain reaction as other managers notice the downward price action and follow suit in order to stop further losses.
Market is upset because monopolies are basically busted with opensource AI