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Comment by paxys | Hacker News Reader

IMO this is less about DeepSeek and more that Nvidia is essentially a bubble/meme stock that is divorced from the reality of finance and business. People/institutions who bought on nothing but hype are now panic selling. DeepSeek provided the spark, but that's all that was needed, just like how a vague rumor is enough to cause bank runs.

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Not quite, I believe this sell off was caused by DeepSeek showing with their new model that the hardware demands of AI are not necessarily as high as everyone has assumed (as required by competing models).

I've tried their 7b model, running locally on a 6gb laptop GPU. Its not fast, but the results I've had have rivaled GPT4. Its impressive.

That's a pretty terrible take.

People who can use the 585B model will use the best model they can have. What DeepSeek really did was start an AI "space race" to AGI with China, and this race is running on Nvidia GPUs.

Some hobbyists will run the smaller model, but if you could, why not use the bigger & better one?

Model distillation has been a thing for over a decade, and LLM distillation has been widespread since 2023 [1].

There is nothing new in being able to leverage a bigger model to enrich smaller models. This is what people that don't understand the AI space got out of it, but it's clearly wrong.

OpenAI has smaller models too with o1 mini and o4 mini, and phi-1 has shown that distillation could make a model 10x smaller perform as well as a much bigger model. The issue with these models is that they can't generalize as well. Bigger models will always win at first, then you can specialize them.

Deepseek also showed that Nvidia GPUs could be more memory-efficient, which catapults Nvidia even further ahead of upcoming processors like Groq or AMD.

[1] https://arxiv.org/abs/2305.02301

I believe you that it had to do with the selloff, but I believe that efficiency improvements are good news for NVIDIA: each card just got 20x more useful

That still means that that AI firms don't have to buy as many of Nvidia's chips, which is the whole thing that Nvidia's price was predicated on. FB, Google and Microsoft just had their their billions of dollars in Nvidia GPU capex blown out by $5M side-project. Tech firms are probably not going to be as generous shelling out whatever overinflated price Nvidia was asking for as they were a week ago.

Although there’s the Jevon’s Paradox possibility that more efficient AI will drive even more demand for AI chips because more uses will be found for them. But possibly not super high end NVDA chips but instead little Apple iPhone AI cores or smartwatch AI cores, etc.

Although not all commodities will work like fossil fuels did in Jevon’s Paradox. It could be the case that demand for AI doesn’t grow fast enough to keep demand for chips as high as it was, as efficiency improves.

> But possibly not super high end NVDA chips but instead little Apple iPhone AI cores or smartwatch AI cores, etc.

We tried that, though. NPUs are in all sorts of hardware, and it is entirely wasted silicon for most users, most of the time. They don't do LLM inference, they don't generate images, and they don't train models. Too weak to work, too specialized to be useful.

Nvidia "wins" by comparison because they don't specialize their hardware. The GPU is the NPU, and it's power scales with the size of GPU you own. The capability of a 0.75w NPU is rendered useless by the scale, capability and efficiency of a cluster of 600w dGPU clusters.

Wrong conclusion, IMO. This makes inference more cost effective which means self-hosting suddenly becomes more attractive to a wider share of the market.

GPUs will continue to be bought up as fast as fabs can spit them out.

The number of people interested in doing self-hosting for AI at the moment is a tiny, tiny percentage of enthusiast computer users, who indeed get to play with self-hosted LLMs on consumer hardware now.. but the promise of these AI companies is that LLMs will be the "next internet", or even the "next electricity" according to Sam Altman, all of which will run exclusively on Nvidia chips running in mega-datacenters, the promise of which was priced into Nvidia's share price as of last Friday. That appears on shaky ground now.

I'm not talking about enthusiastic computer users. To be frank, they're rather irrelevant here. I'm talking about companies.

> That still means that that AI firms don't have to buy as many of Nvidia's chips

Couldn’t you say that about Blackwell as well? Blackwell is 25x more energy-efficient for generative AI tasks and offer up to 2.5x faster AI training performance overall.

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 only 2 years ago?

Simple, Nvidia first had to push the compute at scale strongly. Try training GPT4 on Voltas from 2017. Good luck with that!

Current LLMs are possible thanks to the compute Nvidia has provided in the past decade. You could technically use 20 year old CPUs for LLMs but you might need to connect a billion of them.

It means personal ai on every computer. No privacy concerns, but saying that it is quite weird coming from a Chinese start up :)

It won't last long. Agents are where AI is going to go imho. That means giving the ai software access to the internet, and that means telemetry.

Always hilarious to see westerners concerned about privacy when it comes to China, yet not concerned at all about their own governments that know far more about you. Do they think some Chinese policeman is going to come to their door? Never heard of Snowden or the five eyes?

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 for single H100. I'm sure if you ask for 10k H100 you'll get them at half price so easily 40 days of training.

DeepSeek has fooled everyone by telling them that they need only so less money and people think that they only need to "buy" $5M worth of GPU but that's wrong. The money is the training costs of renting the GPU training hours.

Somebody had to install the 10k GPUs and that's paying $300M to Nvidia.

Imagine what you can do with all that Nvidia hardware using the deep mind techniques.

They only got more useful if the AI goldrush participants actually strike, well, gold. Otherwise it's not useful at all. Afaict it remains to be seen whether any of this AI stuff has actual commercial value. It's all just speculation predicated on thoughts and prayers.

When your business is selling a large number of cards to giant companies you don't want them to be 20x more useful because then people will buy fewer of them to do the same amount of work

or people do 30x more work and buy 50% more cards

each card is not 20x more useful lol. there's no evidence yet that the deepseek architecture would even yield a substantially (20x) more performant model with more compute.

if there's evidence to the contrary I'd love to see. in any case I don't think a h800 is even 20x better than a h100 anyway, so the 20x increase has to be wrong.

We need GPUs for inference, not just training. The Jevons Paradox suggests that reducing the cost per token will increase the overall demand for inference.

Also, everything we know about LLMs points to an entirely predictable correlation between training compute and performance.

Jevons paradox doesn't really suggest anything by itself. Jevons paradox is something that occurs in some instances of increased efficiency, but not all. I suppose the important question here is "What is the price elasticity of demand of inference?"

Personally, in the six months prior to the release of the deepseekv3 api, I'd made probably 100-200 api calls per month to llm services. In the past week I made 2.8 million api calls to dsv3.

can i ask what kind of api calls you're making to dsv3? Crunching through huge amounts of unstructured data or something?

Processing each english (word, part-of-speech, sense) triple in various ways. Generating (very silly) example sentences for each triple in various styles. Generating 'difficulty' ratings for each triple. Two examples:

High difficulty:

        id = 37810
      word = dendroid
       pos = noun
     sense = (mathematics) A connected continuum that is arcwise connected and hereditarily unicoherent.
       elo = 2408.61936886416
 sentence2 = The dendroid, that arboreal structure of the Real, emerges not as a mere geometric curiosity but as the very topology of desire, its branches both infinite and indivisible, a map of the unconscious where every detour is already inscribed in the unicoherence of the subject's jouissance.

Low difficulty:

        id = 11910
      word = bed
       pos = noun
     sense = A flat, soft piece of furniture designed for resting or sleeping.
       elo = 447.32459484266
 sentence2 = The city outside my window never closed its eyes, but I did, sinking into the cold embrace of a bed that smelled faintly of whiskey and regret.

People act like Jevons Paradox is an universal law thanks to Satya's tweet.

the jevons paradox isn't about any particular product or company's product, so is irrelevant here. the relevant resource here is compute, which is already a commodity. secondly, even if it were about GPUs in particular, there's no evidence that nvidia would be able to sustain such high margins if fewer were necessary for equivalent performance. things are currently supply constrained, which gives nvidia price optionality.

Uhhh, isn’t it about coal?

> there's no evidence yet that the deepseek architecture would even yield a substantially more performant model with more compute.

It's supposed to. There was an info that the longer length of 'thinking' makes o3 model better than o1. I.e. at least at inference compute power still matters.

> It's supposed to. There was an info that the longer length of 'thinking' makes o3 model better than o1. I.e. at least at inference compute power still matters.

compute matters, but performance doesn't scale with compute from what I've heard about o3 vs o1.

you shouldn't take my word for it - go on the leaderboards and look at the top models from now, and then the top models from 2023 and look at the compute involved for both. there's obviously a huge increase, but it isn't proportional

To me this rings a lot like “640KB ought to be enough for anybody”

Similarly, as fast as processors have gotten, people still complain their applications are slow. Because they do so much more.

Generally applicable ML is still in its infancy, and usage is exploding. All those newfound spare cycles will get soaked up fairly quickly.

It’s made a NVIDIA Digits even more attractive to me now.

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 compareds to Blackwell DC GPUs.

In the end, the winner is Nvidia because Nvidia doesn't care if DC GPU, Gaming GPU, Digits GPU, Jetson GPU is used for AI as long as Nvidia is used 98% of time for AI workloads. That is the world domination goal, simple as that.

And that's what Wallstreet doesn't get. Digits is 50% more turnover than the largest RTX GPU. On average gaming GPU turnover is probably around $500 per GPU. Nvidi probably sells 5 million gaming GPUs per quarter. Imagine they could reach such amounts of Digits. That would be $15b revenue and almost half of current DC revenue with Digits only.

Not quite, I believe this sell off was caused by Shockley showing with their "transistor" that the electricity demands of computers are not necessarily as high as everyone has assumed (as required by vacuum tubes).

Electricity demands will plummet when transistors take the place of vacuum tubes.

None of the models other than the 600b one are R1. They’re just prev gen models like llama or qwen trained on r1 output making them slightly better

"Slightly" is an understatement, though. Distillations of R1 are significantly better than the underlying models.

Yeah but the second comment you see believes they are, and belief is truth when it comes to stock market gambling.

> I've tried their 7b model

Anything other than their 671b model are just distilled models on top of Qwen and Llama using their 671b reasoning data output, right?

Correct. Its the best model I've been able to run locally, by a long shot

I've run their distilled 70B model and didn't come away too impressed -- feels similar to the existing base model it was trained on, which also rivaled GPT4

If that's the case, then I have high hopes that the increase in efficiency will result in more demand, not less.

If only I could figure out how to buy NV stock quickly before it rebounds

Exactly, and firing up reactors to train models just lost all its luster. Those standing before the Stargate will be bored with the whole thing by then end of the week.

that's a Milchmädchenrechnung. if it turns out that you can achieve status quo with 1% of the expected effort then that just mean you can achieve approximately 10 times the status quo (assuming O(exp)) with the established budget! and this race is a race to the sky (as opposed to the bottom) ... he who reaches AGI first takes the cake, buddy.

Hype buyers are also Hype sellers - anything Nvidia was last week is exactly what it is this week - DeepSeek doesn't really have any impact on Nvidia sales - Some argument could be made that this can shift compute off of cloud and onto end user devices, but that really seems like a stretch given what I've seen running this locally.

The full DeepSeek model is ~700B params or so - way too large for most end users to run locally. What some folks are running locally is fine-tuned versions of Llama and Qwen, that are not going to be directly comparable in any way.

Many people are missing this due to journalists completely missing the point when presenting these facts.

Distilled models are nothing new.

Ollama calling the distilled models deepseek-r1:7b etc. doesn't help

I agree hype is a big portion of it, but if DeepSeek really has found a way to train models just as good as frontier ones for a hundredth of the hardware investment, that is a substantial material difference for Nvidia's future earnings.

> if DeepSeek really has found a way to train models just as good as frontier ones for a hundredth of the hardware investment

Frontier models are heavily compute constrained - the leading AI model makers have got way more training data already than they could do anything with. Any improvement in training compute-efficiency is great news for them, no matter where it comes from. Especially since the DeepSeek folks have gone into great detail wrt. documenting their approach.

> leading AI model makers have got way more training data already than they could do anything with.

Citation needed.

If you include multimodal data then I think it's pretty obvious that training is compute limited.

Also current SOTA models are good enough that you can generate endless training data by letting the model operate stuff like a C compiler, python interpreter, Sage computer algebra, etc.

Is it? Training is only done once, inference requires GPUs to scale, especially for a 685B model. And now, there’s an open source o1 equivalent model that companies can run locally, which means that there’s a much bigger market for underutilized on-prem GPUs.

I'd be really curious about the split in hardware for training vs inference - I got the read that it was a very high ratio to the point the training is not a significant portion of the requisite hardware but instead the inference at scale sucks up most of the available datacenter gpu share.

Could be entirely wrong here - would love a fact-check by industry insider or journalist.

I don't see how that follows.

Making training more effective makes every unit of compute spent on training more valuable. This should increase demand unless we've reached a point where better models are not valuable.

The openness of DeepSeek's approach also means that there will be more smaller entities engaging in training rather than a few massive entities that have more ability to set the price they pay.

Plus reasoning models substantially increase inference costs, since for each token of output you may have hundreds of tokens of reasoning.

Arguments on the point can go both ways, but I think on the balance I would expect any improvements in efficiency increase demand.

Unless we get actual AGI I don't honestly care as a non coder. The art is slop and predatory, the chatbots are stilted and pointless, anytime a company uses AI there is huge backlash and there are just no commercial products with any real demand. Make it as cheap as dirt and I still don't see what use it is besides for scammers I guess...

1. Nobody has replicated their DeepSeek's results on their reported budget yet. Scale.ai's Alexander Wang says they're lying and that they have a huge, clandestine H100 cluster. HuggingFace is assembling an effort to publicly duplicate the paper's claims.

2. Even if DeepSeek's budget claims are true, they trained their model on the outputs of an expensive foundation model built from a massive capital outlay. To truly replicate these results from scratch, it might require an expensive model upstream.

https://xyzlabs.substack.com/p/berkeley-researchers-replicat...

Given they've reproduced earlier model's and vetted it - I think it's probably safe to assume that these new models are not out of thin air - but until somebody reproduces it, it's up in the air.

Or Nvidia keeps its earnings and our best frontier models get a hundred times better.

Not really. The training methodology opens up whole new mechanisms that'll make it much easier to train non-language models, which have been very much neglected. Think robot multi-modal models; visual / video question answering; audio processing, etc.

I don't think it's fair to say NVDA is meme stock, having reported 35B revenue last quarter.

Nvidia's annual revenue in 2024 was $60B. In comparison, Apple made $391B. Microsoft made $245B. Amazon made $575B. Google made $278B. And Nvidia is worth more than all of them. You'd have to go very far down the list to find a company with a comparable ratio of revenue or income to market cap as Nvidia.

Nvidia's revenue growth rate was 94% and income growth rate was 109% for the Oct 2024 quarter. This compares to Apple's 6% and -35%.

Nvidia is growing profits faster than income.

Nvidia's net profit margin is 55% (vs Apple 15%) and they have an operating income of $21B vs Apple's $29.5

These are some pretty impressive financial results - those growth rates are the reason people are bullish on it.

Yes revenue has grown xx% in the last quarter and year, but the stock is valued as if it will keep growing at that rate for years to come and no one will challenge them. That is the definition of a bubble.

How sound is the investment thesis when a bunch of online discussions about a technical paper on a new model can cause a 20% overnight selloff? Does Apple drop 20% when Samsung announces a new phone?

> the stock is valued as if it will keep growing at that rate for years to come and no one will challenge them.

If it were valued that way, the P/E would be over 100.

Feel free to say Nvidia is overvalued, but you have to get the financials right.

People do not understand. If you want to make money in the stock market, find growing companies. Pricing of the growing companies is different from others. Since it is not clear when the growth will end, there is a high probability that there will be extreme things in pricing. Since they are market leadership and can lead the price. Don't compare growing companies with others. That's a big fallacy. Their price always overshooted. I don't have any investments in Nvidia, but reality is that. This is why economists always talk about growth.

One might argue that very high margins could be a bad sign. If you assume that Apple is efficient at being Apple, then there is not a whole lot of room for someone else to undercut them at similar cost of goods sold. But there is a lot of room to undercut Nvidia with similar COGS — Nvidia is doing well because it’s difficult to compete for various reasons, not that it’s expensive to compete.

That's the thing. Nvidia's future growth has been potentially kneecapped by R1's leaps in efficiency.

I don't see it, instead of 100 GPUs running the AIs we have today, we'll have 100 GPUs running the AI of the future. NVIDIA wins either way. It won't be 50 GPUs running the AI of today.

All other things being equal, less demand means lower profits. Even if demand still outstrips supply, it's still less demand expected than a month ago.

If the demand is for reaching AGI then we'll still need all the GPUs NVIDIA can sell, we'll just get there faster thanks to DeepSeek.

Bearish argument: "How can we adapt you to run on 8,000,000 NVidia GPUs instead of 80,000,000?"

That probably won't be the first question we ask AGI if/when we ever get there, but it will be near the top of the list.

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.

Blackwell will increase training efficiency in large clusters a lot compared to Hopper and yet it's already sold out because even that won't be enough.

to be fair, there's no way these rates will be sustained for a decade.

What does "to be fair" mean in this context? There's nothing fair or even an alternative point of view. Even the most bullish NVidia investor would agree with this statement.

No one expects this growth to be sustained for a decade. Companies aren't prices based on hypothetical growth rates in 10 years time.

P/E ratio is better indicator. Price/Earnings. NVidia: 46, Microsoft: 35, Apple: 34, Amazon: 50.

As you see NVidia doesn't stand out much, it's even lower than Amazon.

You are sharing numbers after the drop. The p/e was 60 yesterday.

anyway it's not dramatic. vs 50 for Amazon. $147 was close to historical max for NVidia. Not fair either. last month in was less than $140 average, just estimate.

All of them are overvalued compared to historical average ratios.

And NVDA’s P/E benefits from very recent huge spending that may not continue.

They have higher growth rates than average.

Look at their PEG ratios.

Stock market valuations are not about current revenue. That’s just a fundamental disconnect from how the financial markets work.

In theory it’s more about forward profits per share, taking into account growth over many years. And Nvidia is growing faster than any company with that much revenue.

Obviously the future is hard to predict, which leaves a lot of wiggle room.

But I say in theory, because in practice it’s more about global liquidity. It has a lot to do with passive investing being so dominant and money flows.

Money printer goes brrr and stonks go up.

That is not the only thing that matters, but it seems to be the main thing.

If it were really about future profits most of these companies would long since be uninvestable. The valuations are too high to expect a positive ROI.

I'd say it's a meme stock and based on meme revenue. Much of the 35B comes from the fact that companies believe Nvidia make the best chips, and that they have to have the best chips or they'll be out of the game.

DeepSeek supposedly nullifies that last part.

Didn't DeepSeek train on Nvidia hardware though?

I can't see how DeepSeek hurts Nvidia, if Nvidia is what enables DeepSeek.

that's not entirely relevant.

the simplest way to present the counter argument is:

- suppose you could train the best model with a single H100 for an hour. would that hurt or harm nvidia?

- suppose you could serve 1000x users with a 1/1000 the amount of gpus. would that hurt or harm nvidia?

the question is how big you think the market size is, and how fast you get to saturation. once things are saturated efficiency just results in less demand.

Supposedly DeepSeek trained on Nvidia hardware that is not current generation. This suggests that you don't need the current generation to make the best model, which a) makes it harder for Nvidia to sell each generation if it's more like traditional compute (how's Intel's share price today?), and b) opens the door to more competition, because if you can get an AMD chip that's 80% as good for 70% of the price, that's worth it.

I'm skipping over some details of course, but the current Nvidia valuation, or rather the valuation a few days ago, was based on them being the only company capable of producing chips that can train the best models. That wasn't true for those in the know before, but is now very much more clearly not true.

Would now be a good time to buy into NVDA if you are long on it?

True but with that revenue number it would mean that before today it was valued at ~100x revenue. That’s pretty bubbly.

Thats 100x quarterly revenue, or 25x annual revenue.

I think less of that and more of real risks - Nvidia legitimately has the earnings right now. The question is how sustainable that is, when most of it is coming from 5 or so customers that are both motivated and capable of taking back those 90% margins for themselves

They don't have anything close to the earnings to justify the price they have reached.

They are getting a lot of money, but their stock price is in a completely different universe. Not even that $500G deal people announced, if spent exclusively on their products could justify their current price. (Nah, notice that just the change on their valuation is already larger than that deal.)

Their forward PE is fairly reasonable: Nvidia 27, Apple 31, Amazon 38, Microsoft 33.

Regarding their earnings at the moment, I know it doesn't mean everything, but a ~50 P/E is still fairly high, although not insane. I think Ciscos was over 200 during the dotcom bubble. I think your question about the 5 major customers is really interesting, and we will continue to see those companies peck at custom silicon until they can maybe bridge the gap from just running inference to training as well.

Correct, Nvidia has been on this bubble-like tragectory since before the stock was split last year. I would argue that today's drop is a precursor to a much larger crash to come.

Nah, this is not about Nvidia being a bubble. This is about people forgetting that software will keep eating the world and Nvidia is a hardware company no matter how many times people say it's a software company and talk about Cuda. Yes, CUDA is their moat, but they are not a software company. See my post on reddit from 10 months ago about this happening.

https://www.reddit.com/r/LocalLLaMA/comments/1c0je6h/comment...

"The biggest threat to NVIDIA is not AMD, Intel or Google's TPU. It's software. Sofware eats the world!"

"That's what software is going to do. A new architecture/algorithm that allows us current performance with 50% of the hardware, would change everything. What would that mean? If Nvidia had it in the books to sell N hardware, all of a sudden the demand won't exist since N compute can be realized with the new software and existing hardware. Hardware that might not have been attractive like AMD, Intel or even older hardware would become attractive. They would have to cut their price so much, the violent exodus from their stocks will be shocking. Lots of people are going to get rich via Nvidia, lots are going to get poor after the fact. It's not going to be because of hardware, but software."

A lot of people are saying that I'm wrong on other hardware like AMD or Intel, but this article by Stratechery agrees, all other hardware vendors are possibly relevant again. I didn't talk about Apple because I was focused on the server side, Apple has already won the consumer side and is so far ahead and waiting for the tech to catch up to it.

The biggest threat to Nvidia is still more software optimization.

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 smartphone vendors combined.

And this is exactly where Nvidia's goal is. The AI compute market will grow, Nvidia will lose unit market share but Nvidia will retain their profit market share. Simple as that.

And by the way, Nvidia is way ahead in SW compared to alternatives. Most here have the DIY glasses on. But enterprises and businesses have different lenses. For those not being Tech they need secure and working solution with enterprise grades. Nvidia is among the few to offer this with Enterprise AI solutions (NeMo, NIMs, etc.). Nvidia's SW moat isn't CUDA, CUDA is an API for performance and stability. Nvidia's SW moat is in the frameworks for applications for many differnt industries and of course ALL Nvidia SW will require Nvidia HW.

A company using Nvidia enterprise SW solutions and consultancy will never use anything except Nvidia HW. Nvidia has a program with >10k AI startups being supported with free consulting and HW support. Nvidia is basically grooming their next generation customers by themselves.

You have no idea, many think Nvidia is only selling some chips and that's where they are wrong. Nvidia is a brand, an ecosystem and they will continue to grow from there. See gaming, much more standards and commodity in SW than AI SW. There is no CUDA, you can swap a Nvidia card with AMD card within a minute. So let me know, how come for 2 decades that Nvidia has continously 80-95% market share?

Nvida has a P/E of 47. While it may be a bit high for a semiconductor company, it's definitely not a meme stock figure.

The forward PE is half that based on real numbers of future orders reported in company reports.

Yes and no, going from 47 to 50 would buy a few of the most popular meme stocks so there simply aren't enough people to make it a true meme stock with that market cap.

I'm sorry, but this is just so, so wrong. Nvidia is an insane company. You can make the argument that the entire sector is frothy/bubbly; I'm more likely to believe that. But, here's some select financials about NVDA:

NVDA Net income, Quarter ending in ~Oct2024: $19B. AMD? $771M. INTC? -$16.6B. QCOM? $3B. AAPL? $14B.

Revenue growth, YoY? +93%. AMD? +17%. INTC? -6%. QCOM? +18%. AAPL? +6%.

Margin? 55%. AMD? 11%. INTC? -125%. QCOM? 28%. AAPL? 15%.

P/E Ratio? 46. AMD? 103. INTC? N/A, unprofitable. QCOM? 19. AAPL? 34. NFLX? 54. GME? 151.

Their P/E Ratio doesn't even classify them as all that overvalued. Think about that. Price to earnings, they are cheaper than Netflix, Gamestop, they're about the same level as WALMART, you know, that Retailer everyone hates that has practically no AI play, yeah their P/E is 40.

Nvidia is an insane company. Insane. We've had three of the largest country-economies on the planet announce public/private funding to the tune of 12 figures, maybe totaling 13 figures when its all said and done, and NVDA is the ONLY company on the PLANET that sells what they want to buy. There is no second player. Oh yeah, Google will rent you some TPUs, haha yeah sure bud. China wants to build AI data centers, and their top tech firms are going to the black market smuggling GPUs across the ocean like bricks of cocaine rather than rely on domestic manufacturers, because not even other AMERICAN manufacturers can catch up.

Sure, a 10x drop in cost of intelligence is initially perceived as a hit to the company. But, here's the funny thing about, let's say, CPUs: The Intel Northwood Pentium 4 was released in 2001; with its 130nm process architecture, it sipped a cool 61 watts of power. With today's 3nm process architecture, we've built (drumroll please) the Intel Core Ultra 5 255, which consumes 65 watts of power. Sad trombone? Of course not; its a billion times more performant. We could have directed improvements in process architecture toward reducing power draw (and certainly, we did, for some kinds of chips). But, the VAST, VAST, VAST majority of allocation of these process improvements was in performance.

The story here is not "intelligence is 10x cheaper, so we'll need 10x fewer GPUs". The story is: "Intelligence is 10x cheaper, people are going to want 10x more intelligence."

Nvidia's forward PE before this is 27, based off of real orders. Unless orders are being canceled, the stock price should be significantly higher

This is a cookie cutter comment that appears to have been copy pasted from a thread about Gamestop or something. DeepSeek R1 allegedly being almost 50x more compute efficient isn't just a "vague rumor". You do this community a disservice by commenting before understanding what investors are thinking at the current moment.

Has anyone verified DeepSeek's claims about R1? They have literally published one single paper and it has been out for a week. Nothing about what they did changed Nvidia's fundamentals. In fact there was no additional news over the weekend or today morning. The entire market movement is because of a single statement by DeepSeek's CEO from over a week ago. People sold because other people sold. This is exactly how a panic selloff happens.

They have not verified the claims but those claims are not a "vague rumor". Expectations of discounted cash flows, which is primarily what drives large cap stock prices, operates on probability, not strange notions of "we must be absolutely certain that something is true".

A credible lab making a credible claim to massive efficiency improvements is a credible threat to Nvidia's future earnings. Hence the stock got sold. It's not more complicated than that.

Not a true verification but I have tried the Deepseek R1 7b model running locally, it runs on my 6gb laptop GPU and the results are impressive.

Its obviously constrained by this hardware and this model size as it does some strange things sometimes and it is slow (30 secs to respond) but I've got it to do some impressive things that GPT4 struggles with or fails on.

Also of note I asked it about Taiwan and it parroted the official CCP line about Taiwan being part of China, without even the usual delay while it generated the result.

The weights are public. We can't verify their claims about the amount of compute used for training, but we can trivially verify the claims about inference cost and benchmark performance. On both those counts, DeepSeek have been entirely honest.

Benchmark performance - better models are actually great for Nvidia's bottom line, since the company is relying on the advancement of AI as a whole.

Inference cost - DeepSeek is charging less than OpenAI to use its public API, but that isn't an indicator of anything since it doesn't reflect the actual cost of operation. It's pretty much a guarantee that both companies are losing money. Looking at DeepSeek's published models the inference cost is in the same ballpark as Llama and the rest.

Which leaves training, and that's what all the speculation is about. The CEO said that the model cost $5.5M and that's what the entire world is clinging on. We have literally no other info and no way to verify it (for now, until efforts to replicate it start to show results).

>Inference cost - DeepSeek is charging less than OpenAI to use its public API, but that isn't an indicator of anything since it doesn't reflect the actual cost of operation.

Again, the weights are public. You can run the full-fat version of R1 on your own hardware, or a cloud provider of your choice. The inference costs match what DeepSeek are claiming, for reasons that are entirely obvious based on the architecture. Either the incumbents are secretly making enormous margins on inference, or they're vastly less efficient; in the first case they're in trouble, in the second case they're in real trouble.

R1's inference costs are in the same ballpark as Llama 3 and every other similar model in its class. People are just reading and repeating "it is cheap!!" ad nauseam without any actual data to back it up.

I wonder where they got 50… llama405 cost like 60M, which puts deepseek at closer to 10x…

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.

Deepseek isn’t a distilled model (and neither is llama405), both are pre trained foundation models.

Deepseek has distilled deepseek R1 into a couple of smaller open source models, but neither R1 or v3 are distilled themselves.

Hard to argue Nvidia is a meme stock and that Tesla is not a bigger meme stock.

If meme stocks were imploding, why is Tesla fine?

This is about DeepSeek.

What does any of this have to do with Tesla? Even if Tesla is a bigger bubble, not all bubbles have to pop at the same time.

The market can stay irrational longer than you can stay solvent.

Tesla is playing the political game with Trump. They're riding that wave. Musk always find some new reason for people to believe the stock.

Whenever the internet tells you to buy, it's a huge warning that a pump and dump is occurring.

Exactly. Everything is massive kindle for a wildfire and it comes to show how tiny of a spark it takes. Yet - people keep adding more kindle.

No the reality of AI models fundamentally changed

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