The demand for Nvidia GPUs should now go up. Now anyone can run a GPT-like model by themselves. It's a prime time for businesses to start investing and setting up on-prem infra for that. I know some have been avoiding ChatGPT due to legal concerns and sensitive data.
It's an objective fact that many people find these things useful. Whether or not they're willing to PAY is another issue.
That day is certainly coming, but today is not that day
The fact that DeepSeek-R1 is so much better than DeepSeek-V3 at various important tasks means that Chain-of-though / thinking-before-answering models are better. But they are also more compute intensive at inference time than their instruction non-thinking counterparts.
So even if the DeepSeek-V3 pretraining + GRPO COT post-training procedure was cheaper than anticipated to reach o1 grade performance, inference is still costly, even if you use a distilled model.
What was amazing output last year is today's slop.
NV is in no immediate danger; medium/long term, to anyone knowing what "75% gross margin" means it's no secret that there will be serious threats. You don't need DeepSeek for that particular realization.
History is littered with things that were made orders of magnitude better, people who claimed nobody would be happy until that happened, and then nobody writ large cared .
Tech in particular has a grand history of claiming we need to produce the technically best thing, but in practice, the market almost always chooses somewhere in the range of useful products, which is not the same at all - the technically best thing has rarely, if ever, become the market leader.
If there was serious competition for cards to do GPU training that might spell trouble for Nvidia. But so far we only went from needing an absurdly huge cluster of Nvidia cards to a huge cluster of Nvidia cards, with Nvidia having a near-monopoly on cards used in deep-learning training all the way down to networks that fit on single cards.
But there's a more subtle point here which I don't see a lot of people talking about, maybe because they know more about this than me. Why wouldn't frontier model developers take DeepSeek R1 techniques and make their models even better or even larger? Or another way to ask: Are the DeepSeek R1 innovations only for making models cheaper (and slightly worse) or can the algorithms developed by the DeepSeek team be scaled up to make more powerful frontier models? Leading edge model developers don't just care about cost, they also want to release models with maximum capabilities. And as we've seen over the past few years, they're willing to pay almost anything to achieve this.
I think most AI researchers know there are still many things to explore in this space so disruptive innovations shouldn't be seen as a bubble burst but as more opportunity.
NVIDIA is in an extremely strong position right now. Even if someone has a major breakthrough on the hardware design side that dramatically lowers the cost of compute for AI workloads (which is highly unlikely), NVIDIA will just create their own implementation that will outperform the original since they have a stranglehold on an entire stack-up of technology: circuits, drivers, libraries and software.
I don’t think the Nvidia CEO is losing any sleep. He doesn’t have a planet-sized ego, like some. They are still selling pickaxes during a gold rush, as the saying goes. And the stock is still up 100% in the past year. CEOs don’t control the stock price (much as some try), anyway, the market does. Also, the market is insane. You also can’t control the competition, or what new innovations come along. You just have to run a good business, which they are doing.
Innovation is good for all players. Reality-checks are good. Nothing here is unexpected. There are lots of smart researchers in the world. Software innovations that make better use of hardware are expected. People just like drama.
Like if Tim does a better job at a skateboard trick that has always been Tom’s thing, people want to be that kid who is the one to say, “Oh, snap!!” and won’t stop talking about it at school, because they were there. And how it’s so mind-blowing and previously inconceivable.
Also, inference is easier to get disrupted in just because of how it works.
All told, this makes it a much less likely play.
Finally, the assumption that it goes up assumes people want on-prem infrastructure for this (in your case). Maybe true, maybe not.
Overall, going from being a "sure" thing selling 200m worth of clusters to random companies hand over fist to not necessarily being able to do that is definitely "bad'.
There are other common claims about why it will increase demand, and they rely on different assumptions (that you won't hit a good enough point quickly, etc)
Or will all of the efficiency savings get immediately absorbed by the demand for better performance and feed the demand for inference?
But i agree in practice that over time nvidia's path now depends heavily on the answer to "Or will all of the efficiency savings get immediately absorbed by the demand for better performance and feed the demand for inference?"
Before, their next 5-10 years did not really depend on meaningful efficiency savings existing and getting them absorbed - nobody expected meaningful efficiency savings.
Now it does depend on that
Those savings don't usually make national news because they aren't the target. They aren't "man on the moon" moments, they are "our cannonball flies 96% as far as yours using 30% less powder" moments.
This is still a race for tools that essentially brute force their way to greater utility. Until we hit a utility quotient or hit a wall, the race doesn't really end. The quickest and easiest path to advantage in the race is still more compute.
I'm aware - i've run the relevant teams at large scale companies like Google :)
Is your claim anyone has made 98% total vs, say, 5 years ago?
If so, evidence please?
Otherwise, seems sort of irrelevant.
AFAIK, nobody has come anywhere close to this, and the only possible way they would is through externalizing roughly all costs onto someone else.
(IE certainly google TPU's are much more efficient than 5 years ago, but since you have to account for development cost, they are not saving 98% vs 5 years ago)
I understand if new CPU/GPU can outperform Nvidia and DeepSeek was developed using another GPU, but this is not the case. Lower requirement for higher performance historically never reduced the need for computational capacity. There is very poor reasoning for this move other than purely “technical” (trading-wise) reasons.
Nvidia will still sell many chips. It just won’t be the only one capable of selling them. The hype is gone. The moat is gone. The excitement and enthusiasm from investors is gone. This is called a correction.
CUDA begs to differ.
If the training was too expensive previously is it now cheap enough or does it need to be even cheaper? As if it weren't too expensive everyone who were going to invest did. And if it is still too expensive, well prices have to go lower or more efficiency has to be found.
I am not entirely sure if there is huge amount of unmet demand for training.
this conversation reminds me of people when the PS2 came out saying that by 2010 games would look literally better than real life, because they thought graphics quality would exponentially improve...
Na, we have a long way to go with models, especially when you start adding different modes. We'll still need a metric shitload of compute for a long time.