AI Is Catapulting Nvidia Toward the $1 Trillion Club(wsj.com)
wsj.com
AI Is Catapulting Nvidia Toward the $1 Trillion Club
https://www.wsj.com/articles/how-ai-is-catapulting-nvidia-toward-the-1-trillion-club-14f42380
249 comments
https://archive.is/4XeAa
I'm getting very strong 1998 .COM vibes from AI.
Replace Internet with AI in the following quote from the New York Times, November 11, 1996[0]:
"For many people, AI could replace the functions of a broker, whose stock in trade is information, advice and execution of transactions, all of which may be cheap and easy to find on line. AI also is prompting some business executives to wonder whether they really need a high-priced Wall Street investment bank to find backers for their companies when they may now be able to reach so many potential investors directly over the Net. And ultimately, AI's ability to bring buyers and sellers together directly may change the very nature of American financial markets."
It's a cautionary tale. Obviously, the Internet did live up to the hype. Just after it wiped out millions of retail investors...
[0]https://www.nytimes.com/1996/11/11/business/slow-transition-...
Replace Internet with AI in the following quote from the New York Times, November 11, 1996[0]:
"For many people, AI could replace the functions of a broker, whose stock in trade is information, advice and execution of transactions, all of which may be cheap and easy to find on line. AI also is prompting some business executives to wonder whether they really need a high-priced Wall Street investment bank to find backers for their companies when they may now be able to reach so many potential investors directly over the Net. And ultimately, AI's ability to bring buyers and sellers together directly may change the very nature of American financial markets."
It's a cautionary tale. Obviously, the Internet did live up to the hype. Just after it wiped out millions of retail investors...
[0]https://www.nytimes.com/1996/11/11/business/slow-transition-...
It is fairly different though in scope. NVidia clearly is making tons of money from AI. Probably after OpenAI they are the company most directly impacted by AI.
The .com boom of the late 90s was different. Companies who had very little to do with the internet were adding ".com" to their name. I was a penny stock trader and that was one of the fastest ways companies would increase value -- add ".com" and issue a press release about how they plan to be "internet enabled" or "create a web presence".
Today most companies aren't getting a bump by talking about AI. You don't see Spotify changing their name to Spotify.AI. Companies are dabbling in offering AI, e.g., SnapChat, but they aren't shifting their whole focus to AI.
Now there is an industry of small companies building on AI, and I think that's healthy. A handful will find something of value. Going back to the early .com days -- I remember companies doing video/movie streaming over the web and voice chats. None of those early companies, AFAIK, still exist. But the market for this technology is bigger than its ever been.
The .com boom of the late 90s was different. Companies who had very little to do with the internet were adding ".com" to their name. I was a penny stock trader and that was one of the fastest ways companies would increase value -- add ".com" and issue a press release about how they plan to be "internet enabled" or "create a web presence".
Today most companies aren't getting a bump by talking about AI. You don't see Spotify changing their name to Spotify.AI. Companies are dabbling in offering AI, e.g., SnapChat, but they aren't shifting their whole focus to AI.
Now there is an industry of small companies building on AI, and I think that's healthy. A handful will find something of value. Going back to the early .com days -- I remember companies doing video/movie streaming over the web and voice chats. None of those early companies, AFAIK, still exist. But the market for this technology is bigger than its ever been.
You seem to misunderstand the dot com bubble. Sure, there were companies like Pets.com and companies adding an 'e' to the beginning of their names. But there were also companies like Cisco and Sun Microsystems. Companies making profits selling real goods needed by the growing internet. Go look up those companies and their stock charts. Also, if you think random companies aren't mentioning AI to boost their stock you haven't been paying much attention.
Cisco peaked at $77.00 in March 2000. It's currently $50.00. In the interim there have been no splits.
Intel peaked at $73.94 in September 2000. It's currently $28.99. In the interim there have been no splits.
NVidia has split 5 times (cumulative 48x) since 2000. It closed 2000 around $2.92. It is currently $389.93. Totally gain 6400x. If you ignore the last 12 months, NVidia's last peak was $315 in 2021, for a total gain of 5178x. -ish.
Intel peaked at $73.94 in September 2000. It's currently $28.99. In the interim there have been no splits.
NVidia has split 5 times (cumulative 48x) since 2000. It closed 2000 around $2.92. It is currently $389.93. Totally gain 6400x. If you ignore the last 12 months, NVidia's last peak was $315 in 2021, for a total gain of 5178x. -ish.
I imagine we might be looking at different points in each company’s lifecycle? Nvidia was founded in the 90s. If we look at intel’s stock over the same range relative to the start of the company, what happens then? Feels like this comparison is not that relevant to the dot com bubble and whether AI is similar.
Looks like you’re double-counting the splits.
No, they just did a couple unusual splits: https://www.stocksplithistory.com/nvidia/
The $2.92 price you mentioned as closing the year 2000 is almost certainly split-adjusted.
Why wouldn't you just reference market cap instead of share price..... Ugh
Ask GPT-4, maybe it will say that this is not that unreasonable. Artificial intelligence is might be once-in-a-civilization-lifetime event.
I'm concerned that if AI really lives up to the hype that retail investors are thinking, it's more of a civilization ending event than some sort of ascension to heaven.
Meanwhile the hedge funds and institutional investors are just trying to ride the momentum while it lasts, which could be for a while.
Meanwhile the hedge funds and institutional investors are just trying to ride the momentum while it lasts, which could be for a while.
"On a long enough timeline, the survival rate for everyone drops to zero."
I think you misunderstood my point. My thesis was that these two eras were different in scope (my first sentence). I was pointing out how the .com booms impact was so much larger than the current AI boom, in terms of financial impact. I wasn't trying to say the .com boom was smaller or more well-reasoned. In fact quite the opposite. I don't think we've seen comparable spikes to the .com boom yet, and you seem to agree.
If we're going to see an AI spike and bust, we're just at the beginning of it.
Nvidia is pricing on actual revenue growth (~16%?) and projected growth (~20%). Since 2016 they've been killing it.
AI will turn into a bubble when unrelated companies begin being priced like that, without historical or current revenue growth to back up their projections, simply by virtue of being AI-associated.
Nvidia is pricing on actual revenue growth (~16%?) and projected growth (~20%). Since 2016 they've been killing it.
AI will turn into a bubble when unrelated companies begin being priced like that, without historical or current revenue growth to back up their projections, simply by virtue of being AI-associated.
Somehow other companies with that growth don't get priced at 30 times sales.
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I would be comparing how someone like Intel did during dot.com instead of Pets.com etc. Of course it far from being the same and Intel did struggle in the early 00’s but they still ended up dominating their market which had significant growth in the 20 years after dot.com.
Did Intel ever ‘grow’ into their massively overvalued valuation? No.. their stock never even reached it’s September, 2000 peak yet.
There is a chance that AMD, Intel, maybe Google etc. catch up with Nvidia in a year or two and data center GPUs become a commodity (clearly the entry bar should be lower than what it was for x86 CPUs back in) and what happens then?
Did Intel ever ‘grow’ into their massively overvalued valuation? No.. their stock never even reached it’s September, 2000 peak yet.
There is a chance that AMD, Intel, maybe Google etc. catch up with Nvidia in a year or two and data center GPUs become a commodity (clearly the entry bar should be lower than what it was for x86 CPUs back in) and what happens then?
> There is a chance that AMD, Intel, maybe Google etc. catch up with Nvidia in a year or two and data center GPUs become a commodity (clearly the entry bar should be lower than what it was for x86 CPUs back in) and what happens then?
Realistically, there is next to zero chance Intel (especially given the Arc catastrophe and foundry capabilities) or AMD (laundry list of reasons) catchup within 2 years.
Safe bet Google's TPUv5 will be competitive with the H100, as the v4 was with the A100, but their offering clearly hasn't impacted market share thus far and there is no indication Google intends to make their chips available outside of GCP.
With that said I also agree the current valuation seems too high, but I highly doubt there is a serious near-term competitor. I think it is more likely that current growth projections are too aggressive and demand will subside before they grow into their valuation, especially as the space evolves with open source foundation models and techniques come out (like LoRA/PEFT) that substantially reduce demand for the latest chips.
Realistically, there is next to zero chance Intel (especially given the Arc catastrophe and foundry capabilities) or AMD (laundry list of reasons) catchup within 2 years.
Safe bet Google's TPUv5 will be competitive with the H100, as the v4 was with the A100, but their offering clearly hasn't impacted market share thus far and there is no indication Google intends to make their chips available outside of GCP.
With that said I also agree the current valuation seems too high, but I highly doubt there is a serious near-term competitor. I think it is more likely that current growth projections are too aggressive and demand will subside before they grow into their valuation, especially as the space evolves with open source foundation models and techniques come out (like LoRA/PEFT) that substantially reduce demand for the latest chips.
> there is no indication Google intends to make their chips available outside of GCP.
1. You can buy mini versions of their chips through Coral (coral.ai). But yea, they’d never sell them externally as long as there exists a higher-margin advantage to selling software on top of them, and chips have supply constraints.
2. Google can sell VMs with the tensor chips attached, like GPUs. Most organizations with budgets that’d impact things will be using the cloud. If Apple/MSFT/AWS/Goog/Meta start serious building their own chips, NVidia could be left out of the top end.
1. You can buy mini versions of their chips through Coral (coral.ai). But yea, they’d never sell them externally as long as there exists a higher-margin advantage to selling software on top of them, and chips have supply constraints.
2. Google can sell VMs with the tensor chips attached, like GPUs. Most organizations with budgets that’d impact things will be using the cloud. If Apple/MSFT/AWS/Goog/Meta start serious building their own chips, NVidia could be left out of the top end.
> Google can sell VMs with the tensor chips attached, like GPUs.
They have already been doing this for quite a while now and even when offered free via TRC barely anyone uses TPUs. There is nothing to suggest that Google as an organization is shifting focus to be the HPC cloud provider for the world.
As it stands TPU cloud access really seems ancillary to their own internal needs.
> If Apple/MSFT/AWS/Goog/Meta start serious building their own chips, NVidia could be left out of the top end.
That's a big "if", especially within two years, given that this chip design/manufacturing isn't really a core business interest for any of those companies (other than Google which has massive internal need and potentially Apple who have never indicated interest in being a cloud provider).
They certainly could compete with Nvidia for the top-end, but it would be really hard and how much would the vertical integration actually benefit their bottom line? A 2048 GPU SuperPOD is what, like 30M?
There's also the risk that the not-always-friendly DoJ gets anti-trusty if a cloud provider has a massive advantage and is locking the HW in their walled garden.
They have already been doing this for quite a while now and even when offered free via TRC barely anyone uses TPUs. There is nothing to suggest that Google as an organization is shifting focus to be the HPC cloud provider for the world.
As it stands TPU cloud access really seems ancillary to their own internal needs.
> If Apple/MSFT/AWS/Goog/Meta start serious building their own chips, NVidia could be left out of the top end.
That's a big "if", especially within two years, given that this chip design/manufacturing isn't really a core business interest for any of those companies (other than Google which has massive internal need and potentially Apple who have never indicated interest in being a cloud provider).
They certainly could compete with Nvidia for the top-end, but it would be really hard and how much would the vertical integration actually benefit their bottom line? A 2048 GPU SuperPOD is what, like 30M?
There's also the risk that the not-always-friendly DoJ gets anti-trusty if a cloud provider has a massive advantage and is locking the HW in their walled garden.
> barely anyone uses TPUs
What are you basing that on? I'm not aware of GCP having released any numbers on their usage.
What are you basing that on? I'm not aware of GCP having released any numbers on their usage.
Anecdotal data warning but for context my research is in medical informatics and I've quite extensively followed publications on transformers dating back to the early BERT variants (including non-medical).
I'm making that statement as my experience (easily several hundreds of publications read or reviewed over 3 years) is that it is very uncommon to see TPU's mentioned or TRC acknowledged in any non-Google transformer paper (especially major publications) dating back to the early BERT family of models despite the fact that Google is very generous with research credits (they'll give out preemptible v3-32s and v3-64s for 14 days with little question, presumably upgraded now as I haven't asked for credits in a while).
Fully acknowledge this isn't quality evidence to back my claim and I'm happy to be proven wrong but I'm very confident a literature review would support this as when I tried to use TPUs myself I couldn't find much.
This doesn't account for industry use, there is probably a non-insignificant amount of enterprise customers still using AutoML (I can think of a few at least) which I believe uses the TPU cloud but I would be surprised if many use TPU nodes directly outside of Jax shops like cohere and anyone still using TF.
PyTorch XLA has just breaks too much otherwise and when I last tried to use it in January of this year there was still quite a significant throughput reduction on TPUs. Additionally when using nodes there is a steeper learning curve on the ops side (VM, storage, Stackdriver logging) that make working with them harder than spinning up a A100x8 which is relatively cheap, cheaper than the GCP learning curve for sure.
I'm making that statement as my experience (easily several hundreds of publications read or reviewed over 3 years) is that it is very uncommon to see TPU's mentioned or TRC acknowledged in any non-Google transformer paper (especially major publications) dating back to the early BERT family of models despite the fact that Google is very generous with research credits (they'll give out preemptible v3-32s and v3-64s for 14 days with little question, presumably upgraded now as I haven't asked for credits in a while).
Fully acknowledge this isn't quality evidence to back my claim and I'm happy to be proven wrong but I'm very confident a literature review would support this as when I tried to use TPUs myself I couldn't find much.
This doesn't account for industry use, there is probably a non-insignificant amount of enterprise customers still using AutoML (I can think of a few at least) which I believe uses the TPU cloud but I would be surprised if many use TPU nodes directly outside of Jax shops like cohere and anyone still using TF.
PyTorch XLA has just breaks too much otherwise and when I last tried to use it in January of this year there was still quite a significant throughput reduction on TPUs. Additionally when using nodes there is a steeper learning curve on the ops side (VM, storage, Stackdriver logging) that make working with them harder than spinning up a A100x8 which is relatively cheap, cheaper than the GCP learning curve for sure.
> Anecdotal data warning but for context my research is in medical informatics
Isn't Medical Informatics inherently biased against the cloud? That's my uninformed guess as an outsider.
Isn't Medical Informatics inherently biased against the cloud? That's my uninformed guess as an outsider.
It seems like one of these two things must be true:
A) Nvidia's TAM is not really what the stock is priced foe
B) Google will try to enter this market and compete
Either way NVDA looks perilously pricey, not that that is very predictive of anything (see TSLA).
A) Nvidia's TAM is not really what the stock is priced foe
B) Google will try to enter this market and compete
Either way NVDA looks perilously pricey, not that that is very predictive of anything (see TSLA).
So what, are they pathological layers?
https://youtu.be/XVcKLetqf3U
The Intel® Data Center GPU Max Series outperforms Nvidia H100 PCIe card by an average of 30% on diverse workloads1, while independent software vendor Ansys shows a 50% speedup for the Max Series GPU over H100 on AI-accelerated HPC applications.2 The Xeon Max Series CPU, the only x86 processor with high bandwidth memory, exhibits a 65% improvement over AMD’s Genoa processor on the High Performance Conjugate Gradients (HPCG) benchmark1, using less power. High memory bandwidth has been noted as among the most desired features for HPC customers.3 4th Gen Intel Xeon Scalable processors – the most widely used in HPC – deliver a 50% average speedup over AMD’s Milan4, and energy company BP’s newest 4th Gen Xeon HPC cluster provides an 8x increase in performance over its previous-generation processors with improved energy efficiency.2 The Gaudi2 deep learning accelerator performs competitively on deep learning training and inference, with up to 2.4x faster performance than Nvidia A100.
https://www.intel.com/content/www/us/en/newsroom/news/intel-...
https://youtu.be/XVcKLetqf3U
The Intel® Data Center GPU Max Series outperforms Nvidia H100 PCIe card by an average of 30% on diverse workloads1, while independent software vendor Ansys shows a 50% speedup for the Max Series GPU over H100 on AI-accelerated HPC applications.2 The Xeon Max Series CPU, the only x86 processor with high bandwidth memory, exhibits a 65% improvement over AMD’s Genoa processor on the High Performance Conjugate Gradients (HPCG) benchmark1, using less power. High memory bandwidth has been noted as among the most desired features for HPC customers.3 4th Gen Intel Xeon Scalable processors – the most widely used in HPC – deliver a 50% average speedup over AMD’s Milan4, and energy company BP’s newest 4th Gen Xeon HPC cluster provides an 8x increase in performance over its previous-generation processors with improved energy efficiency.2 The Gaudi2 deep learning accelerator performs competitively on deep learning training and inference, with up to 2.4x faster performance than Nvidia A100.
https://www.intel.com/content/www/us/en/newsroom/news/intel-...
> next to zero chance Intel (especially given the Arc catastrophe and foundry capabilities)
Arc is manufactured using TSMC N6.
Intel originally wanted to use Intel 4 but it wasn’t ready yet. Maybe the next batch of GPUs assuming Meteor Lake and their other CPUs don’t consume all the Intel 4 capacity.
Also Arc hardware-wise is fine for what it is and the process node it’s using - N6 isn’t a leading edge node to my knowledge. Drivers are unfortunately something that’s going to take time to fix up - there is no way around this.
Arc is manufactured using TSMC N6.
Intel originally wanted to use Intel 4 but it wasn’t ready yet. Maybe the next batch of GPUs assuming Meteor Lake and their other CPUs don’t consume all the Intel 4 capacity.
Also Arc hardware-wise is fine for what it is and the process node it’s using - N6 isn’t a leading edge node to my knowledge. Drivers are unfortunately something that’s going to take time to fix up - there is no way around this.
Agree but Intel has yet to show they can successfully make a high end GPU, and they're heavily invested in Arc at the moment.
Given Intel 4 is launching at the end of the year I would expect their focus will be on catching up wth AMD on CPUs and the next-gen Arc GPUs. Assuming everything goes well with their yields and they have extra foundry time (which they won't be using as part of IFS) will they have the institutional energy/capital/will to open a new software+hardware battle in a market the entrenched Nvidia will fight to the death for?
It seems extremely unlikely to me within 1-2 years.
Given Intel 4 is launching at the end of the year I would expect their focus will be on catching up wth AMD on CPUs and the next-gen Arc GPUs. Assuming everything goes well with their yields and they have extra foundry time (which they won't be using as part of IFS) will they have the institutional energy/capital/will to open a new software+hardware battle in a market the entrenched Nvidia will fight to the death for?
It seems extremely unlikely to me within 1-2 years.
I wouldn’t discount AMD just yet. They closed quite a big gap in the server cpu market against Intel, most probably due to better leadership and management. I wouldn’t be surprised if they are able to pull that trick a second time with GPUs. 2 years isn’t short but it isn’t that long either.
People have said AAPL was overvalued perennially as long as I remember yet their market performance seems to ignore these opinions.
On the other hand, a big part of it also comes down to the tool chain, and NVIDIA owns CUDA. Until OpenCL or other gpu platforms catch up, it seems like NVIDIA can continue to corner the gpu market at large.
People have said AAPL was overvalued perennially as long as I remember yet their market performance seems to ignore these opinions.
On the other hand, a big part of it also comes down to the tool chain, and NVIDIA owns CUDA. Until OpenCL or other gpu platforms catch up, it seems like NVIDIA can continue to corner the gpu market at large.
Nvidia seems like a tougher competitor to oust than Intel.
> People have said AAPL was overvalued perennially
Yes but they were saying this when AAPL's p/e ratio was in the low teens and now it's near 30. it was never near the insanity that is NVDA. I will grant that there's a lot of uncertainty about the future, but there's immense optimism baked in right now. It will be hard to live up to.
> People have said AAPL was overvalued perennially
Yes but they were saying this when AAPL's p/e ratio was in the low teens and now it's near 30. it was never near the insanity that is NVDA. I will grant that there's a lot of uncertainty about the future, but there's immense optimism baked in right now. It will be hard to live up to.
Arc has already caught up to Nvidia. The latest Nvidia GPUs are a disaster (the 4060ti is being universally mocked for its very pathetic performance), they're intentionally royally screwing their customers.
The A750 and A770 are tremendous GPUs and compete very well with anything Nvidia has in those brackets (and Intel is willing to hammer Nvidia on price, as witnessed by the latest price cuts on the A750). Drivers have rapidly improved in the past few quarters. It's likely given how Nvidia has chosen to aggressively mistreat its customers that Intel will surpass them on value proposition with Battlemage.
The A750 and A770 are tremendous GPUs and compete very well with anything Nvidia has in those brackets (and Intel is willing to hammer Nvidia on price, as witnessed by the latest price cuts on the A750). Drivers have rapidly improved in the past few quarters. It's likely given how Nvidia has chosen to aggressively mistreat its customers that Intel will surpass them on value proposition with Battlemage.
You’re talking about consumer grade graphics, not AI processing, and you’re talking about cheap, not performant.
There is no significant competition to the NVIDIA A100 and H100 for machine learning.
There is no significant competition to the NVIDIA A100 and H100 for machine learning.
Now that I think it's right of them to do, but all consumer Nvidia products are overpriced to hell and have been for a long time, now.
The reason is because they can get away with it, because there's so much demand for their product. Were Nvidia to see AMD release a 4090 equivalent at half the price they need only reduce their own ridiculous prices and take less of a profit margin.
The reason is because they can get away with it, because there's so much demand for their product. Were Nvidia to see AMD release a 4090 equivalent at half the price they need only reduce their own ridiculous prices and take less of a profit margin.
> anything Nvidia has in those brackets
This being the operative part of the statement. If we're talking top-end GPUs it's not even close.
> Intel is willing to hammer Nvidia on price
They also have no choice, Intel's spend on Arc has been tremendous (which is what I mean by catastrophe, everything I've read suggests this will be a huge loss for Intel). I doubt they have much taste for another loss-leader in datacenter-level GPUs right now, if they even have the manufacturing capacity.
This being the operative part of the statement. If we're talking top-end GPUs it's not even close.
> Intel is willing to hammer Nvidia on price
They also have no choice, Intel's spend on Arc has been tremendous (which is what I mean by catastrophe, everything I've read suggests this will be a huge loss for Intel). I doubt they have much taste for another loss-leader in datacenter-level GPUs right now, if they even have the manufacturing capacity.
the 4060ti is an entry board, it's designed to be cheap not fast. I believe this pattern was also true for 3060 and 2060.
>and what happens then?
Most likely, all their prices go up...
I mean, your first instinct is to say, "but how could all their prices so up, they'll steal value from each other", but that's not necessarily true. If AI starts solving useful problems, and especially if it starts requiring multi-modality to do so, I would expect the total GPU processing demand to increase by 10,000-100,000X that we have now.
Now, you're going to say "What's going to pay for this massive influx of GPU power by corporations". And my reply would be "Corporations not having to pay for your health insurance any longer".
Most likely, all their prices go up...
I mean, your first instinct is to say, "but how could all their prices so up, they'll steal value from each other", but that's not necessarily true. If AI starts solving useful problems, and especially if it starts requiring multi-modality to do so, I would expect the total GPU processing demand to increase by 10,000-100,000X that we have now.
Now, you're going to say "What's going to pay for this massive influx of GPU power by corporations". And my reply would be "Corporations not having to pay for your health insurance any longer".
I'm not sure AMD will catch up to Nvidia. Obviously there are a lot of traders betting on that right now, given that AMD has started to rally in response to Nvidia. However after all this time NV still commands like 80% share of the gaming GPU market despite AMD often (not always) releasing competitive cards. Gaming GPUs are already a commodity - why hasn't AMD caught up there?
I mean, maybe it's not a fair comparison but I don't see why the datacenter/GPGPU market won't end up the same way. Nvidia is notorious for trying to lock in users with proprietary tech too, though people don't seem to mind.
I mean, maybe it's not a fair comparison but I don't see why the datacenter/GPGPU market won't end up the same way. Nvidia is notorious for trying to lock in users with proprietary tech too, though people don't seem to mind.
> Did Intel ever ‘grow’ into their massively overvalued valuation? No.. their stock never even reached it’s September, 2000 peak yet.
If you take dividends into account it did break even a few years ago, at least in nominal terms.
Cisco and Sun Microsystems may be even better comparables though.
If you take dividends into account it did break even a few years ago, at least in nominal terms.
Cisco and Sun Microsystems may be even better comparables though.
Exactly. In the current situation the "dotcom boom" would be any companies who are currently forcing themselves to use AI for the sake of using AI so that they can say "we use AI to [task that can be done without AI]".
Nvidia is different in that they're the ones selling the hardware, AI isn't going anywhere imo, the spike Nvidia is seeing atm may subside a little but I doubt it, as minor players give up, stronger players will still need more hardware anyway.
Tbh imagine being Nvidia: * Known for dominating in the gaming market, consumers buy plenty of Nvidia cards and always will do * Workstation cards have always been used for CAD/rendering digital media and always will be * Nvidia hardware used in plenty of supercomputers * Crypto craze hit and Nvidia sold a bajillion cards for that, I imagine 2nd-hand mining cards have impacted the consumer arm of their biz but probably not too much, I've seen people avoid buying crypto cards unless they're offered at a very low price * Nvidia has sold cards to people doing AI for a long time, but now the AI boom has started and they're making bank
Basically they've enjoyed the crypto boom and are now enjoying the AI boom, but even if AI boom declines to 0 (it won't) they can still fall back on their workstation/consumer hardware.
Reason I don't think the AI boom will end is that besides companies smashing AI in for no reason, actual applications of it are incredibly useful. I still remember friends being amazed that they could search their Google Photos by "dog" or "cat" (which as furries it's hilarious that it comes up with fursuiters).
Nvidia is different in that they're the ones selling the hardware, AI isn't going anywhere imo, the spike Nvidia is seeing atm may subside a little but I doubt it, as minor players give up, stronger players will still need more hardware anyway.
Tbh imagine being Nvidia: * Known for dominating in the gaming market, consumers buy plenty of Nvidia cards and always will do * Workstation cards have always been used for CAD/rendering digital media and always will be * Nvidia hardware used in plenty of supercomputers * Crypto craze hit and Nvidia sold a bajillion cards for that, I imagine 2nd-hand mining cards have impacted the consumer arm of their biz but probably not too much, I've seen people avoid buying crypto cards unless they're offered at a very low price * Nvidia has sold cards to people doing AI for a long time, but now the AI boom has started and they're making bank
Basically they've enjoyed the crypto boom and are now enjoying the AI boom, but even if AI boom declines to 0 (it won't) they can still fall back on their workstation/consumer hardware.
Reason I don't think the AI boom will end is that besides companies smashing AI in for no reason, actual applications of it are incredibly useful. I still remember friends being amazed that they could search their Google Photos by "dog" or "cat" (which as furries it's hilarious that it comes up with fursuiters).
Are there numbers available on current ML applications GPU sales volume, is it really a big share of NV revenue? Dedicated ML hardware like TPUs would seem to be the logical perf/$ competitor longer term, they're so far proprietary but so are NV sw and hw after all.
I got this quote from the BBC:
"Figures show its [NVidia] AI business generated around $15bn (£12bn) in revenue last year, up about 40% from the previous year and overtaking gaming as its largest source of income"
Oddly, in updates to the article they rewrote a lot of it, and that line is missing, but you can still see it if you search for it.
"Figures show its [NVidia] AI business generated around $15bn (£12bn) in revenue last year, up about 40% from the previous year and overtaking gaming as its largest source of income"
Oddly, in updates to the article they rewrote a lot of it, and that line is missing, but you can still see it if you search for it.
Maybe someone temporarily mistook the main non gaming aka datacenter side for being all ml.
SpotifAI
I did the same s/ai/internet thing yesterday when I asked Bard to give me analyst ratings for cisco stock before the dot-com crash:
"The highest analyst price target for Cisco stock before the dot-com crash was $125 per share. This target was set by Merrill Lynch analyst Henry Blodget in April 2000, just as the dot-com bubble was beginning to burst. Blodget's target was based on his belief that Cisco was well-positioned to benefit from the continued growth of the Internet."
I was looking to compare with analyst targets set for NVDA yesterday. Analysts now are saying the exact thing about Nvidia being able to capture the continued growth of AI:
"JPMorgan set its price target to $500 Wednesday, double its previous estimate and among the highest out of the big banks. Analyst Harlan Sur said this is the “first massive wave of demand in generative AI,” with more gains to follow. He reiterated his overweight rating on the stock."
The ironic bit of course is that my own research here is powered by Bard which probably used an NVDA gpu to train it. But even those dot-com analyst calls were probably emailed around on equipment sold by Cisco.
If I were holding that stock right now, regardless of how right these analysts end up being over the next year or so. I would sell today
"The highest analyst price target for Cisco stock before the dot-com crash was $125 per share. This target was set by Merrill Lynch analyst Henry Blodget in April 2000, just as the dot-com bubble was beginning to burst. Blodget's target was based on his belief that Cisco was well-positioned to benefit from the continued growth of the Internet."
I was looking to compare with analyst targets set for NVDA yesterday. Analysts now are saying the exact thing about Nvidia being able to capture the continued growth of AI:
"JPMorgan set its price target to $500 Wednesday, double its previous estimate and among the highest out of the big banks. Analyst Harlan Sur said this is the “first massive wave of demand in generative AI,” with more gains to follow. He reiterated his overweight rating on the stock."
The ironic bit of course is that my own research here is powered by Bard which probably used an NVDA gpu to train it. But even those dot-com analyst calls were probably emailed around on equipment sold by Cisco.
If I were holding that stock right now, regardless of how right these analysts end up being over the next year or so. I would sell today
> The ironic bit of course is that my own research here is powered by Bard which probably used an NVDA gpu to train it
Google uses in-house TPUs for Bard.
https://www.hpcwire.com/2023/04/10/google-ai-supercomputer-s...
Google uses in-house TPUs for Bard.
https://www.hpcwire.com/2023/04/10/google-ai-supercomputer-s...
I don't think this is just another bubble about to burst. I mean, the bubble bears have been talking about the imminently bursting bubble since 2016. The past couple years are what that burst bubble looks like. Hype-driven companies going out of business, disappearing unicorns, pullback on VC, massive layoffs, bank implosions, tons of tech stocks pulled back by 70-90%, consequences on the likes of Theranos, SBF, etc.
The current AI wave is 95% hype (ultimately useless/broken crap invoking LLM APIs or AI art app du jour) but some of the companies are clearly useful (transcription, summarization, categorization, code generation, next-gen search engine, etc.) and will disrupt traditional services and scale large.
And AI infra companies (AI hardware, AI software on top of hardware, and generic AI model SaaS) will make tons of money as those app companies scale.
The current AI wave is 95% hype (ultimately useless/broken crap invoking LLM APIs or AI art app du jour) but some of the companies are clearly useful (transcription, summarization, categorization, code generation, next-gen search engine, etc.) and will disrupt traditional services and scale large.
And AI infra companies (AI hardware, AI software on top of hardware, and generic AI model SaaS) will make tons of money as those app companies scale.
What ElevenLabs is doing with synthesised voices is absolutely amazing. Not quite fully realistic yet, but they're the best I've ever heard.
In addition 2minute papers viewers have seen that AI generated media is coming fast, soon we'll go from Unity/Unreal having an AI "assistant" that can generate models "make a chair for two characters in the same style as this single person chair" to "based on the current information you know about this game world, generate a new zone for the player that includes x, y, z challenges, resources. Create models, textures, animations for all of this" etc. And this is only implications for making games, let along all the other stuff we could get it to do.
The video on automatic animations (https://www.youtube.com/watch?v=wAbLsRymXe4 and others) is super cool, once refined it's going to be possible to have a system that can: generate a character model, texture it, automatically animate it for that particular character (young, old, how many limbs) and adjust as needed "right foot becomes injured, so limp" generated voices and unique dialogue set within the realm of the overall game world. I think main plots will still be controlled by game makers, but interaction with rando npcs/side-quests could be totally organic.
In addition 2minute papers viewers have seen that AI generated media is coming fast, soon we'll go from Unity/Unreal having an AI "assistant" that can generate models "make a chair for two characters in the same style as this single person chair" to "based on the current information you know about this game world, generate a new zone for the player that includes x, y, z challenges, resources. Create models, textures, animations for all of this" etc. And this is only implications for making games, let along all the other stuff we could get it to do.
The video on automatic animations (https://www.youtube.com/watch?v=wAbLsRymXe4 and others) is super cool, once refined it's going to be possible to have a system that can: generate a character model, texture it, automatically animate it for that particular character (young, old, how many limbs) and adjust as needed "right foot becomes injured, so limp" generated voices and unique dialogue set within the realm of the overall game world. I think main plots will still be controlled by game makers, but interaction with rando npcs/side-quests could be totally organic.
That is incredible, thanks for the link! It kind of reminds me of the invention of the music synthesizer. Suddenly you could create any sound, fluidly and interactively.
You are correct that the overall economic backdrop is quite different from the late 90s.
Nonetheless, the AI news cycle is continuous (like .COM was) and the attribution of NVDA's +25% romp to the prospects of AI grabs the attention of retail investors, who tuned in to see AVGO +20% and the likes of MSFT, TSLA, NFLX and GOOG add 5% in 2 days. The longer that goes on, the more we'll see investors looking for reasons that companies will benefit from AI and want to buy in, then, companies that don't have a strong AI story will need to get on the train and start buying all the AI startups that have materialized over the last couple of years. Then, we start seeting AI IPOs with increasingly sketchy histories. (sorry, .COM PTSD kicking in...)
All this could happen in a weak market. In fact, strong returns in AI during a weak overall market will simply call more attention to it.
Nonetheless, the AI news cycle is continuous (like .COM was) and the attribution of NVDA's +25% romp to the prospects of AI grabs the attention of retail investors, who tuned in to see AVGO +20% and the likes of MSFT, TSLA, NFLX and GOOG add 5% in 2 days. The longer that goes on, the more we'll see investors looking for reasons that companies will benefit from AI and want to buy in, then, companies that don't have a strong AI story will need to get on the train and start buying all the AI startups that have materialized over the last couple of years. Then, we start seeting AI IPOs with increasingly sketchy histories. (sorry, .COM PTSD kicking in...)
All this could happen in a weak market. In fact, strong returns in AI during a weak overall market will simply call more attention to it.
‘At 10 times revenues, to give you a 10-year payback, I have to pay you 100% of revenues for 10 straight years in dividends. That assumes I can get that by my shareholders. That assumes I have zero cost of goods sold, which is very hard for a computer company. That assumes zero expenses, which is really hard with 39,000 employees. That assumes I pay no taxes, which is very hard. And that assumes you pay no taxes on your dividends, which is kind of illegal. And that assumes with zero R&D for the next 10 years, I can maintain the current revenue run rate. Now, having done that, would any of you like to buy my stock at $64? Do you realize how ridiculous those basic assumptions are? You don’t need any transparency. You don’t need any footnotes. What were you thinking?’— Scott McNealy, Business Week, 2002
https://www.bloomberg.com/news/articles/2002-03-31/a-talk-wi...
https://www.bloomberg.com/news/articles/2002-03-31/a-talk-wi...
I mean it's completely wrong though as you still have shares in the company after getting paid.
A very good rule of thumb is: if someone's mentions dividends when discussing valuation they are clueless. It doesn't always work (paying high dividends has implications ranging from clueless management to political pressure on the company) but it's a very good rule that the argument is nonsense.
A very good rule of thumb is: if someone's mentions dividends when discussing valuation they are clueless. It doesn't always work (paying high dividends has implications ranging from clueless management to political pressure on the company) but it's a very good rule that the argument is nonsense.
This ignores inflation and other factors in the macro environment. But ultimately, any argument that a stock is mispriced is definitionally wrong, because the price of a stock is what someone is willing to pay for it. It's a cliche but it's also an incontrovertible fact, even if people like to ignore it because it invalidates all their arguments.
>because the price of a stock is what someone is willing to pay for it.
It's only true assuming fully efficient markets, which even academic economists studying markets don't do.
The fact someone is willing to pay $100 for one share doesn't mean every share is worth $100.
The fair value of a stock should always depend on the expected cash flow you can receive by holding the stock for perpetuity. Nobody can predict the future, so nobody really knows what the fair value is.
But, if you had 1 trillion dollars and still wouldn't want to pay 1 trillion to acquire an entire company, because you feel you very likely can't make that 1 trillion back, then it's fair to say the company is not worth 1 trillion to you.
The fact someone is willing to pay $100 for one share doesn't mean every share is worth $100.
The fair value of a stock should always depend on the expected cash flow you can receive by holding the stock for perpetuity. Nobody can predict the future, so nobody really knows what the fair value is.
But, if you had 1 trillion dollars and still wouldn't want to pay 1 trillion to acquire an entire company, because you feel you very likely can't make that 1 trillion back, then it's fair to say the company is not worth 1 trillion to you.
> …2 years ago we were selling at 10 times revenues when we were at $64. At 10 times revenues, to give you a 10-year payback, I have to pay you 100% of revenues for 10 straight years in dividends. That assumes I can get that by my shareholders. That assumes I have zero cost of goods sold, which is very hard for a computer company. That assumes zero expenses, which is really hard with 39,000 employees. That assumes I pay no taxes, which is very hard. And that assumes you pay no taxes on your dividends, which is kind of illegal. And that assumes with zero R&D for the next 10 years, I can maintain the current revenue run rate. Now, having done that, would any of you like to buy my stock at $64? Do you realize how ridiculous those basic assumptions are? You don’t need any transparency. You don’t need any footnotes. What were you thinking?
Sun Microsystems CEO Scott McNealy in 2002 (source https://smeadcap.com/missives/the-mcnealy-problem/#:~:text=A....)
Sun Microsystems CEO Scott McNealy in 2002 (source https://smeadcap.com/missives/the-mcnealy-problem/#:~:text=A....)
Whether are not AI will follow the same destiny as the dot com bubble, doesn't really matter: In contrast to fancy AI startups, Nvidia is already making money (in fact it is highly profitable). They are basically adhering to the principle "During a goldrush, sell shovels."
As someone who has persistently laughed off the "it's different this time" idiocy from "revolutionary" technology, and as someone who has called 10 out of the last 4 bubbles, I would like to say that it really is different this time.
We're on the precipice of obviating 80% of white collar work, and 99% of Graeber's Bullshit Jobs.
We're on the precipice of obviating 80% of white collar work, and 99% of Graeber's Bullshit Jobs.
I agree with you, especially on un-regulated white-collar work (e.g. no one with magic letters after their name is in danger just yet).
But give it a few years and I'm really curious how regulatory and licensing bodies react because they have almost always moved uniformly in whichever direction is necessary to suppress wages. There are few exceptions to this (e.g. physicians). The output benefits of worker + AI could potentially lead to some professional services becoming dirt cheap, while others become ludicrously expensive.
I'm also curious what this means for immigration. For the West, the primary justification to siphon the world's talent fundamentally vanishes. That's talent that potentially stays put and develops in non-Western countries. For countries where the entire country is a demographic ponzi using immigrants to prevent collapse, it's potentially an existential problem.
But give it a few years and I'm really curious how regulatory and licensing bodies react because they have almost always moved uniformly in whichever direction is necessary to suppress wages. There are few exceptions to this (e.g. physicians). The output benefits of worker + AI could potentially lead to some professional services becoming dirt cheap, while others become ludicrously expensive.
I'm also curious what this means for immigration. For the West, the primary justification to siphon the world's talent fundamentally vanishes. That's talent that potentially stays put and develops in non-Western countries. For countries where the entire country is a demographic ponzi using immigrants to prevent collapse, it's potentially an existential problem.
Immigration is just a really simple way to increase GDP
Could you please detail why do you think Machine Learning will obviate jobs that are already useless?
Easy, managers will have to do what they usually told their useless subordinates to do. “Write a report on this thing that no one will ever read anyway”. Then even if the higher ups actually read it, they will ask for another LLM to provide a summary. There’s no shortage of useless jobs out there. Cashiers for one.
We won't. This is the myth of Homo economicus.
"humans as agents who are consistently rational and narrowly self-interested, and who pursue their subjectively defined ends optimally."
We will figure out new, irrational and suboptiomal ways to make new bullshit jobs.
The AI ethics department will be hiring a ton of people.
"humans as agents who are consistently rational and narrowly self-interested, and who pursue their subjectively defined ends optimally."
We will figure out new, irrational and suboptiomal ways to make new bullshit jobs.
The AI ethics department will be hiring a ton of people.
You're right, but I don't think so. From the moment that 80/99% realizes they're out of work, it's over. That's why you see idiot anti-AI spokespeople showing up, why Altman is invited to Bilderberg, why EU is making AI-laws. They're not against AI, as such, but please do not "awaken" the working class. Keep it for "trusted parties" or the military only. What I am curious about is how NVIDIA will position itself against that background.
Personally, I did really wish this would have been a new-era moment where society would take a step back and evaluate how we are organizing ourselves (and living, even), but I fear that AI comes too late for us, in the sense that we're so rusted and backwards now that we cannot accept it. Or any important change, in fact. It's pretty depressing.
Personally, I did really wish this would have been a new-era moment where society would take a step back and evaluate how we are organizing ourselves (and living, even), but I fear that AI comes too late for us, in the sense that we're so rusted and backwards now that we cannot accept it. Or any important change, in fact. It's pretty depressing.
Massively decreasing pay, while not solving the asset inflation problem... this ain't going to go well at all.
Economic dislocation will lead to rise of angry/insane populists/nationalists (like Trump 2.0) in multiple regions. Already a trend, will get worse. One unfortunate but plausible outcome is catastrophic global conflict.
To avoid this, countries need to plan for and mitigate the social effects of economic dislocation, such as UBI. Unfortunately that ain't gonna happen. Brace yourselves.
To avoid this, countries need to plan for and mitigate the social effects of economic dislocation, such as UBI. Unfortunately that ain't gonna happen. Brace yourselves.
Nah, not there yet. Just in the "Blockchain" realm for now ;-)
This is the only time in my entire life that I accurately predicted the stock market.
About two months ago, I bought three shares of Nvidia stock. I noticed that no one appears to be doing serious AI/ML research with AMD hardware, and I also noticed that Nvidia's stock hadn't spiked yet with the rise of ChatGPT and Stable Diffusion.
For once I was actually right about something in the stock market...About a dozen more accurate predictions and I'll finally make up the money I lost from cryptocurrency.
About two months ago, I bought three shares of Nvidia stock. I noticed that no one appears to be doing serious AI/ML research with AMD hardware, and I also noticed that Nvidia's stock hadn't spiked yet with the rise of ChatGPT and Stable Diffusion.
For once I was actually right about something in the stock market...About a dozen more accurate predictions and I'll finally make up the money I lost from cryptocurrency.
>and I also noticed that Nvidia's stock hadn't spiked yet with the rise of ChatGPT and Stable Diffusion.
I think plenty have noticed, But cant get heads around investing in a company with 150x PE.
I think plenty have noticed, But cant get heads around investing in a company with 150x PE.
This is because you're looking at trailing P/E instead of forward.
NVDA forward P/E is still eye watering, but it's much lower.
NVDA forward P/E is still eye watering, but it's much lower.
Forward P/E aren't that much difference. Even when you are expecting 20%+ Net Income YoY. I dont see it being "much lower".
Ok, Answering my own question. I mean there are three to four threads on Nvidia I wish someone could have pointed it out.
Nvidia next quarter guidance ( Which some media misinterpreted as 2H FY24 ) will be $11B, at least ~50% ahead of current forecast and best market expectation and are TSMC capacity constrained. Revenue expectation of FY2024 will be anywhere between 50% to 80% of FY23. Considering their Revenue will be mostly from Datacenter, Margin is expected to be better, Profits Forward earning in the higher end could double of FY23. Meaning Forward P/E ( Taking Current ~PE 220 from Yahoo, assuming that is correct ) would be ~110. Some sites are putting it Forward P/E as 50, I have no idea where that figure came from. So someone correct me if I am wrong.
But the market currently ( at least as I read it on news and some comments here ) are expecting them to DOUBLE again in FY25.
The Datacenter currently spent about $25B in total on Intel and AMD. Nvidia is looking at around $30B this year.
But again, Nvidia's GPU is only used for training. The actual mass market and volume are still in Inference. I dont see how $30B a year being sustainable in the long term. But I dont doubt this year and next will be a problem, I guess that is what market wants to see anyway.
It is interesting at these sort of Margin and volume. Now the tide has turned and Nvidia will be able to use and afford leading Edge Node on their GPU. Apple will no longer be TSMC's only option although still preferred due to smaller die size of Phone SOC.
It is only a matter of time before Nvidia further grow their market with ARM CPU. Which they haven't put too much effort into it yet. They already have Network Card from Mellanox, and are doing great.
But here is a wild idea, Acquisition. If Nvidia were to spend money to buy, which company would it be? Qualcomm currently only worth $120B. Nvidia will get access to Qualcomm's Patent and their SoC Line. Finally setting foot in Mobile.
Nvidia next quarter guidance ( Which some media misinterpreted as 2H FY24 ) will be $11B, at least ~50% ahead of current forecast and best market expectation and are TSMC capacity constrained. Revenue expectation of FY2024 will be anywhere between 50% to 80% of FY23. Considering their Revenue will be mostly from Datacenter, Margin is expected to be better, Profits Forward earning in the higher end could double of FY23. Meaning Forward P/E ( Taking Current ~PE 220 from Yahoo, assuming that is correct ) would be ~110. Some sites are putting it Forward P/E as 50, I have no idea where that figure came from. So someone correct me if I am wrong.
But the market currently ( at least as I read it on news and some comments here ) are expecting them to DOUBLE again in FY25.
The Datacenter currently spent about $25B in total on Intel and AMD. Nvidia is looking at around $30B this year.
But again, Nvidia's GPU is only used for training. The actual mass market and volume are still in Inference. I dont see how $30B a year being sustainable in the long term. But I dont doubt this year and next will be a problem, I guess that is what market wants to see anyway.
It is interesting at these sort of Margin and volume. Now the tide has turned and Nvidia will be able to use and afford leading Edge Node on their GPU. Apple will no longer be TSMC's only option although still preferred due to smaller die size of Phone SOC.
It is only a matter of time before Nvidia further grow their market with ARM CPU. Which they haven't put too much effort into it yet. They already have Network Card from Mellanox, and are doing great.
But here is a wild idea, Acquisition. If Nvidia were to spend money to buy, which company would it be? Qualcomm currently only worth $120B. Nvidia will get access to Qualcomm's Patent and their SoC Line. Finally setting foot in Mobile.
By my calculations they'd need to keep growing earnings by about 47% every year for the next decade to make their current valuation make sense.
I don't think any company in history has achieved that when it was already at Nvidia's scale. Obviously when a company is much smaller it can achieve greater growth.
I don't think any company in history has achieved that when it was already at Nvidia's scale. Obviously when a company is much smaller it can achieve greater growth.
> Some sites are putting it Forward P/E as 50, I have no idea where that figure came from.
Forward P/E is just current P divided by some estimated future E. You can use your own earnings estimate, or commonly you can use sell side analyst estimates.
Forward P/E is just current P divided by some estimated future E. You can use your own earnings estimate, or commonly you can use sell side analyst estimates.
If they weren't allowed to buy Arm, I wonder whether they'll be allowed to buy Arm's biggest customer.
People are expecting more than 20% net income increase.
Whenever a PE looks expensive people are expecting very large increases in E. They aren't just trying to buy expensive stuff.
Whenever a PE looks expensive people are expecting very large increases in E. They aren't just trying to buy expensive stuff.
Many, many people are not looking at PE at all. They are buying into momentum alone, or just buying because it's in the news a lot.
AI may change the world, but it's currently a bubble figuring out how to inflate.
It's implied in the above is that it's for anyone paying attention to E. Practically everyone understands that for every conceivable reason there is a buyer buying for that reason somewhere.
You may think it's a bubble. It's not obviously a bubble to anyone who understands the current capabilities and how locked in nvidia is with their gpus and cuda. It might end up being expensive in retrospect. It might not.
You may think it's a bubble. It's not obviously a bubble to anyone who understands the current capabilities and how locked in nvidia is with their gpus and cuda. It might end up being expensive in retrospect. It might not.
Average 2026 bank fwd PE is ~50x. I have no idea what you're seeing.
Doesn't make any difference whatsoever.
> About two months ago […] and I also noticed that Nvidia's stock hadn't spiked yet with the rise of ChatGPT and Stable Diffusion.
Two months ago the stock was 60% up since ChatGPT was released and 150% up since October’s low.
Two months ago the stock was 60% up since ChatGPT was released and 150% up since October’s low.
Correct. I bought 1000 shares in early Feb, and it's up over 82%.
You cannot look at the prior price to make a purchase decision. You have to look at the future projected revenues per share, and apply an industry standard multiple.
If you hesitate to buy a stock you like because you feel annoyed that you didn't buy it for cheaper the day/week/month before, you will nerf yourself.
You cannot look at the prior price to make a purchase decision. You have to look at the future projected revenues per share, and apply an industry standard multiple.
If you hesitate to buy a stock you like because you feel annoyed that you didn't buy it for cheaper the day/week/month before, you will nerf yourself.
Note that this was not a commentary on the convenience -or not- of buying the stock two months ago after it had more than doubled in a few months. It was intended to put the "stock hadn't spiked yet with the rise of ChatGPT and Stable Diffusion" quote into context.
Even in early Feb, it was up about 50% from 3 months prior.
So what did your math of:
“You have to look at the future projected revenues per share, and apply an industry standard multiple.”
Work out to?
“You have to look at the future projected revenues per share, and apply an industry standard multiple.”
Work out to?
>I also noticed that Nvidia's stock hadn't spiked yet with the rise of ChatGPT and Stable Diffusion.
The stock is was (and is) extremely expensive. Good luck to all buying this things at 30x sales. It doesn't make any sense.
The stock is was (and is) extremely expensive. Good luck to all buying this things at 30x sales. It doesn't make any sense.
I suppose I could sell now and be happy with my $350 profit...
AMD had a price jump on the same date range. Yes NVIDIA is higher but it was already so in absolute terms.
- NVIDIA: https://www.google.com/finance/quote/NVDA:NASDAQ?sa=X&ved=2a...
- AMD: https://www.google.com/finance/quote/AMD:NASDAQ?sa=X&ved=2ah...
- NVIDIA: https://www.google.com/finance/quote/NVDA:NASDAQ?sa=X&ved=2a...
- AMD: https://www.google.com/finance/quote/AMD:NASDAQ?sa=X&ved=2ah...
And NVDA added more than AMDs entire market cap on that day alone, including the gains AMD had.
Doesnt that mean that amd has room to grow faster than nvidia since nvidia is already high?
Still kicking myself for selling the ~20 shares I bought in high school for a mere 100% gain back when the price was $40.
As others have intimated, you'll never go bankrupt by selling [for a profit] too early. I put $10k into bitcoin when it was $1500, and sold it when it hit $4k. Yeah, I can do the math at $60k and feel bad, but realistically if I had the mentality to hold it from $1500 to $60k I would have waited for it to hit $80k and I'd feel objectively worse today about those paper losses, albeit with a little bit more money in the bank.
At the end of the day doubling your money is exceedingly rare, especially on any single security, no sense feeling bad you didn't 10x it.
At the end of the day doubling your money is exceedingly rare, especially on any single security, no sense feeling bad you didn't 10x it.
You made a profit, no self-kicking allowed. Otherwise Old Man Mikestew is going to tell us stories about how he rode $STOCK all the way up, didn't sell, and rode it all the way back down to where he bought it, and then watched it dip below that. (Please note that "stories" is plural; slow learner, he is.)
Don't be like me: never, never, never, never feel bad about selling shares for a profit. Sell it, go one about your day (IOW, quit looking at $STOCK, you don't own it anymore), take the spouse/SO out for a nice dinner if you made some serious bank.
Which reminds me that now might be a good time to unload that NVDA I've been holding. I'm not completely unteachable.
Don't be like me: never, never, never, never feel bad about selling shares for a profit. Sell it, go one about your day (IOW, quit looking at $STOCK, you don't own it anymore), take the spouse/SO out for a nice dinner if you made some serious bank.
Which reminds me that now might be a good time to unload that NVDA I've been holding. I'm not completely unteachable.
Just don't unload all of them. In case the stock goes bananas for whatever reason.
What I'm really going to do is put a trailing stop[0] on it in case it does continue to go bananas, and the stop can catch it on the way back down in case the banana scenario doesn't happen. :-)
[0] Somewhat oversimplified for discussion purposes.
[0] Somewhat oversimplified for discussion purposes.
Just... don't. Enjoy the profits when you get 'em. :)
This forum is filled with people who sold all sorts of tech stocks way too early (or too late), and people nerding out over things and tossing them and them magically gaining tons of value over time - I'm thinking about all of my super early CCGs that I tossed when cleaning house, the 20 bitcoin I mined for fun way back in 2012 or whenever and then deleted from my laptop (that I then sold on eBay for $100), the 10k of AAPL I bought for like $5 and sold for $10, etc. etc.
Same with all the early job opps and what not too - but we're the sum of our life choices till now and that's OK. :)
This forum is filled with people who sold all sorts of tech stocks way too early (or too late), and people nerding out over things and tossing them and them magically gaining tons of value over time - I'm thinking about all of my super early CCGs that I tossed when cleaning house, the 20 bitcoin I mined for fun way back in 2012 or whenever and then deleted from my laptop (that I then sold on eBay for $100), the 10k of AAPL I bought for like $5 and sold for $10, etc. etc.
Same with all the early job opps and what not too - but we're the sum of our life choices till now and that's OK. :)
I bought 100 shares of AMD for 8$ and sold at 16$ thinking I was a genius
I bought 1000 shares of AMD for $2.50 and sold them at $4 and thought I was pretty cool.
Hey, a win is a win, and profit is profit!
I'm still holding ~600 shares of NVDA from a $1,200 investment back when I thought it was amazing that this company made cards that made Quake 2 look incredible.
300k Investment on $900 dollars.
300k Investment on $900 dollars.
Of course, even after it's massive decline, it was still up big due to the crypto market. I think crypto shows how mountains can be moved and worlds terraformed via distributed processing power, and AI is just another set of problems to be solved. There's likely many applications that we haven't even dreamed up yet (likely in the biotech space)
I’d also make a bet on the underdogs - for instance amd is only a devent software update away from snatching market share away from nvidia. I am surprised they are not hiring devs like crazy right now to beef up their ai gpu capability.
The problem with gamba is that 99% of people quit right before making it big.
https://youtu.be/y28Diszaoo4
https://youtu.be/y28Diszaoo4
But how funny would it be if AI ends up pumping crypto by it being the money it can manage directly and instantly from your computer?
[deleted]
Yep as soon as SD dropped I went bullish on NVDA.
What's funny is that we on HN know there's no magic inside these chips, a sufficiently smart foundry could easily cripple Nvidia overnight... yet where's the nearest VC fund for ASICs??
What's funny is that we on HN know there's no magic inside these chips, a sufficiently smart foundry could easily cripple Nvidia overnight... yet where's the nearest VC fund for ASICs??
Did you say foundry? Nations (including US and Germany) have tried to out smart TSMC, and yet here we are.
If you meant outsmart nvidia, Google’s TPU is already more efficient but a GPU is much more than an efficient design .
If you meant outsmart nvidia, Google’s TPU is already more efficient but a GPU is much more than an efficient design .
This is what kept me from selling my shares when they were down.
I hope you sold your Nvidia and locked in those profits.
Just out of curiosity, what was your crypto hypothesis?
NVDA is pretty hyped at this point. If you wanted to buy it, then fall of last year after it fell 60% was the time.
NVDA has a trailing twelve months (TTM) Price to earnings (P/E) ratio of 175x. Based on the latest quarter and forward guidance they have a forward-looking P/E ratio of 50x - So the market is already expecting (and has priced in) even higher expectations of growth than what the stock is already at.
NVDA is expected to at least double their already great growth (to get to P/E of 25x) according to the market. I have my doubts.
You can compare this to the historical averages of the S&P 500: https://www.multpl.com/s-p-500-pe-ratio
NVDA has a trailing twelve months (TTM) Price to earnings (P/E) ratio of 175x. Based on the latest quarter and forward guidance they have a forward-looking P/E ratio of 50x - So the market is already expecting (and has priced in) even higher expectations of growth than what the stock is already at.
NVDA is expected to at least double their already great growth (to get to P/E of 25x) according to the market. I have my doubts.
You can compare this to the historical averages of the S&P 500: https://www.multpl.com/s-p-500-pe-ratio
>Based on the latest quarter and forward guidance they have a forward-looking P/E ratio of 50x
I may have missed the news. Where did they mention they are going to make 3.5X the profits in their forward guidance or forward looking P/E ?
Assuming consumer revenue stays roughly the same, ( crypto usage being the largest variable ). Data Center sector has to grown at least 6X in revenue.
I may have missed the news. Where did they mention they are going to make 3.5X the profits in their forward guidance or forward looking P/E ?
Assuming consumer revenue stays roughly the same, ( crypto usage being the largest variable ). Data Center sector has to grown at least 6X in revenue.
They don't set the forward P/E - it's literally what the price of the stock the market bid up / actual earnings points to. The market is expecting them to double or triple their income in the coming quarters/years.
The TTM Price/Earnings ratio is even crazier as the market is expecting them to grow revenue 9x from what they made in the last year (to get back to a 20x P/E).
The TTM Price/Earnings ratio is even crazier as the market is expecting them to grow revenue 9x from what they made in the last year (to get back to a 20x P/E).
I know the market is hyped but I just dont see how that is possible. HN please tell me where I am wrong. The only moat Nvidia has is in training. I dont see that disappearing anytime soon. At least not in the next 5 - 8 years. However I also cant see being training only brings 10x revenue on Data Center every year. It is not like older GPU are throw away after use.
I mean PE is accurate, but let's also not forget that hype, and future aspect leads to a PE vastly exceeding that of what the market actually expects.
They expect NVDA to not only dominate GPU market, but have a break through in AI or contribute to it, which would lead to way more money.
Also have to look at the fact, any "AI" portfolio is going to be heavily weighted NVDA stock. And people who may be hedging against a raise in AI or buying into said raise are investing in AI portfolios/ETFs, and thereby a portion of that NVDA.
It's not as simple as how the people above are explaining it.
They expect NVDA to not only dominate GPU market, but have a break through in AI or contribute to it, which would lead to way more money.
Also have to look at the fact, any "AI" portfolio is going to be heavily weighted NVDA stock. And people who may be hedging against a raise in AI or buying into said raise are investing in AI portfolios/ETFs, and thereby a portion of that NVDA.
It's not as simple as how the people above are explaining it.
Please follow this advice people. We are all waiting for the dip
When I spent time playing individual stocks I actually made decent money waiting for big spikes like this, hopping on the bandwagon intraday and just taking 1-2% in the hype train. It's part day trading part picking up pennies in front of a steamroller. The few times I really got burned is getting greedy and holding overnight or over a weekend.
I'm really curious to see where NVDA stands on Tuesday morning.
I'm really curious to see where NVDA stands on Tuesday morning.
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One thing that Nvidia has going for it is the stickiness of CUDA. Developers don't have a preference for GPUs, they have a preference for the programming stacks that are associated with them. Why is Google's Tenserflow not as popular?, probably because everyone has deep experience with CUDA and it would be a pain to migrate.
Microsoft Office rode the same type of paradigm to dominate the desktop app market.
Microsoft Office rode the same type of paradigm to dominate the desktop app market.
Sorry, I have to point out: Tensorflow is not comparable to CUDA. Tensorflow is a (arguably) high level library that links against CUDA to run on NVIDIA GPUs, as does PyTorch (the main competitor).
Comparatively few people have “deep” experience with CUDA (basically Tensorflow/Pytorch maintainers, some of whom are NVIDIA employees, and some working in HPC/supercomputing).
CUDA is indeed sticky, but the reason is probably because CUDA is supported on basically every NVIDIA GPU, whereas AMD’s ROCm was until recently limited to CDNA (datacenter) cards, so you couldn’t run it on your local AMD card. Intel is trying the same strategy with oneAPI, but since no one has managed to see a Habana Gaudi card (let alone a Gaudi2), they’re totally out of the running for now.
Separately, CUDA comes with many necessary extensions like cuSparse, cuDNN, etc. Those exist in other frameworks but there’s no comparison readily available, so no one is going to buy an AMD CDNA card.
AMD and Intel need to publish a public accounting of their incompatibilities with PyTorch (no one cares about Tensorflow anymore), even if the benchmarks show that their cards are worse. If you don’t measure in the public no one will believe your vague claims about how much you’re investing into the AI boom. Certainly I would like to buy an Intel Arc A770 with 16GB of VRAM for $350, but I won’t, because no one will tell me that it works with llama.
Comparatively few people have “deep” experience with CUDA (basically Tensorflow/Pytorch maintainers, some of whom are NVIDIA employees, and some working in HPC/supercomputing).
CUDA is indeed sticky, but the reason is probably because CUDA is supported on basically every NVIDIA GPU, whereas AMD’s ROCm was until recently limited to CDNA (datacenter) cards, so you couldn’t run it on your local AMD card. Intel is trying the same strategy with oneAPI, but since no one has managed to see a Habana Gaudi card (let alone a Gaudi2), they’re totally out of the running for now.
Separately, CUDA comes with many necessary extensions like cuSparse, cuDNN, etc. Those exist in other frameworks but there’s no comparison readily available, so no one is going to buy an AMD CDNA card.
AMD and Intel need to publish a public accounting of their incompatibilities with PyTorch (no one cares about Tensorflow anymore), even if the benchmarks show that their cards are worse. If you don’t measure in the public no one will believe your vague claims about how much you’re investing into the AI boom. Certainly I would like to buy an Intel Arc A770 with 16GB of VRAM for $350, but I won’t, because no one will tell me that it works with llama.
With respect to the incompatabilities with PyTorch and TensorFlow - given that the AMD and Intel GPU drivers are more likely to be open sourced - do you believe the open source community or a third party vendors will step in to close the gap for AMD/Intel?
It would seem a great startup idea with the intent to get acqui-hired by AMD or Intel to get into the details of these incompatibilities and/performance differences.
At worst it seems you could pivot into some sort of passive income AI benchmarking website/YT channel similar to the ones that exist for Gaming GPU benchmarks.
It would seem a great startup idea with the intent to get acqui-hired by AMD or Intel to get into the details of these incompatibilities and/performance differences.
At worst it seems you could pivot into some sort of passive income AI benchmarking website/YT channel similar to the ones that exist for Gaming GPU benchmarks.
Drivers are only the lowest level of the stack. You could (in principle) have a great driver ecosystem and a nonexistent user-level ecosystem. And indeed, the user-level ecosystem on AMD and Intel seems to be suffering.
For example, I recently went looking into Numba for AMD GPUs. The answer was basically, "it doesn't exist". There was a version, it got deprecated (and removed), and the replacement never took off. AMD doesn't appear to be investing in it (as far as anyone can tell from an outsider's perspective). So now I've got a code that won't work on AMD GPUs, even though in principle the abstractions are perfectly suited to this sort of cross-GPU-vendor portability.
NVIDIA is years ahead not just in CUDA, but in terms of all the other libraries built on top. Unless I'm building directly on the lowest levels of abstraction (CUDA/HIP/Kokkos/etc. and BLAS, basically), chances are the things I want will exist for NVIDIA but not for the others. Without a significant and sustained ecosystem push, that's just not going to change quickly.
For example, I recently went looking into Numba for AMD GPUs. The answer was basically, "it doesn't exist". There was a version, it got deprecated (and removed), and the replacement never took off. AMD doesn't appear to be investing in it (as far as anyone can tell from an outsider's perspective). So now I've got a code that won't work on AMD GPUs, even though in principle the abstractions are perfectly suited to this sort of cross-GPU-vendor portability.
NVIDIA is years ahead not just in CUDA, but in terms of all the other libraries built on top. Unless I'm building directly on the lowest levels of abstraction (CUDA/HIP/Kokkos/etc. and BLAS, basically), chances are the things I want will exist for NVIDIA but not for the others. Without a significant and sustained ecosystem push, that's just not going to change quickly.
"NVIDIA is years ahead not just in CUDA, but in terms of all the other libraries built on top."
How big an effort would it take to get those libraries to work with AMD drivers?
How big an effort would it take to get those libraries to work with AMD drivers?
I think this is what George Hotz is doing with tiny corp, but I have to admit I have little hope. Making asynchronous SIMD code fast is very difficult as a base point, let alone without internal view of decisions like “why does this cause a sync” or even “will this unnecessary copy ever get fixed?”. Unfortunately AMD and especially Intel don’t “develop in the open”, so even if the drivers are open sourced, without context it’ll be an uphill battle.
To give some perspective, see @ngimel’s comments and PRs in Github. That’s what AMD and Intel are competing against, along with confidence that optimizing for ML customers will pay off (clearly NVIDIA can justify the investment already).
To give some perspective, see @ngimel’s comments and PRs in Github. That’s what AMD and Intel are competing against, along with confidence that optimizing for ML customers will pay off (clearly NVIDIA can justify the investment already).
This kind of software development is hard and expensive. I do not think that this can enable you to make enough income from benchmark website or YT channel, considering most people are not interested in those low level details.
Theoretically the ARC should work with llama.cpp using OpenCL, but I haven't seen benchmarks or even a confirmation that it works.
This has always been in the back of my mind anytime AMD has some new GPUs with nice features. Gamers will say this will be where AMD will win the war. But I fear the war is already won on the compute that counts, and right now that’s CUDA accel on NVIDIA.
This has been the case for a while because AMD never had the resources to do software well. But their market cap is 10x what it was 5 years ago, so now they do. That still takes time, and having resources isn't a guarantee of competent execution, but it's a lot more likely now than it used to be.
On top of that, Intel is making a serious effort to get into this space and they have a better history of making usable libraries. OpenVINO is already pretty good. It's especially good at having implementations in both Python and not-Python, the latter of which is a huge advantage for open source development because it gets you out of Python dependency hell. There's a reason the thing that caught on is llama.cpp and not llama.py.
On top of that, Intel is making a serious effort to get into this space and they have a better history of making usable libraries. OpenVINO is already pretty good. It's especially good at having implementations in both Python and not-Python, the latter of which is a huge advantage for open source development because it gets you out of Python dependency hell. There's a reason the thing that caught on is llama.cpp and not llama.py.
AMDs problem with software goes well beyond people they can’t stick with anything for any significant length of time and the principal design behind ROCm is doomed to fail as it compiles hardware specific binaries and offers no backward or forward compatibility.
CUDA compiles to hardware agnostic intermediary binaries which can run on any hardware as long as the target feature level is compatible and you can target multiple feature levels with a single binary.
CUDA code compiled 10 years ago still runs just fine, ROCm require recompilation every time the framework is updated and every time a new hardware is released.
CUDA compiles to hardware agnostic intermediary binaries which can run on any hardware as long as the target feature level is compatible and you can target multiple feature levels with a single binary.
CUDA code compiled 10 years ago still runs just fine, ROCm require recompilation every time the framework is updated and every time a new hardware is released.
That's all software. There is nothing but resources between here and a release of ROCm that compiles existing code into a stable intermediate representation, if that's something people care about. (It's not clear if it is for anything with published source code; then it matters a lot more if the new version can compile the old code than if the new hardware can run the old binary, since it's not exactly an ordeal to hit the "compile" button once or even ship something that does that automatically.)
It’s a must, published source code or not it doesn’t help.
First there is no forward compatibility guarantee for compiling and based on current history it always breaks.
Secondly even if the code is available a design that breaks software on other users machine is stupid and anti user.
Plenty of projects could import libraries and then themselves be upstream dependencies for other projects, many of which may not be supported.
CUDA is king because people can and still do run 15 year old compiles CUDA code on a daily basis and they know that what they produced today is guaranteed to work on all current and future hardware.
With ROCm you have no guarantee that it would work on even the hardware from the same generation and you pretty much have a guarantee that the next update will break your stuff.
This was a problem with all AMD compilers for GPGPU and ROCm should’ve tried to solve it from day 1 but it still adopted a poor design and that has nothing to do with how many people are working on it.
First there is no forward compatibility guarantee for compiling and based on current history it always breaks.
Secondly even if the code is available a design that breaks software on other users machine is stupid and anti user.
Plenty of projects could import libraries and then themselves be upstream dependencies for other projects, many of which may not be supported.
CUDA is king because people can and still do run 15 year old compiles CUDA code on a daily basis and they know that what they produced today is guaranteed to work on all current and future hardware.
With ROCm you have no guarantee that it would work on even the hardware from the same generation and you pretty much have a guarantee that the next update will break your stuff.
This was a problem with all AMD compilers for GPGPU and ROCm should’ve tried to solve it from day 1 but it still adopted a poor design and that has nothing to do with how many people are working on it.
> Secondly even if the code is available a design that breaks software on other users machine is stupid and anti user.
Most things work like this. You can't natively run ARM programs on x86 or POWER or vice versa, but in most languages you can recompile the code. If you have libraries then you recompile the libraries. All it takes is distributing the code instead of just a binary. Not distributing the code is stupid and anti-user.
> This was a problem with all AMD compilers for GPGPU and ROCm should’ve tried to solve it from day 1 but it still adopted a poor design and that has nothing to do with how many people are working on it.
It isn't even a design decision. Compilers will commonly emit machine code that checks for hardware features like AVX and branch to different instructions based on whether the machine it's running on supports that. That feature can be added to a compiler at any time.
The compiler is open source, isn't it? You could add it yourself, absent any resource constraints.
Most things work like this. You can't natively run ARM programs on x86 or POWER or vice versa, but in most languages you can recompile the code. If you have libraries then you recompile the libraries. All it takes is distributing the code instead of just a binary. Not distributing the code is stupid and anti-user.
> This was a problem with all AMD compilers for GPGPU and ROCm should’ve tried to solve it from day 1 but it still adopted a poor design and that has nothing to do with how many people are working on it.
It isn't even a design decision. Compilers will commonly emit machine code that checks for hardware features like AVX and branch to different instructions based on whether the machine it's running on supports that. That feature can be added to a compiler at any time.
The compiler is open source, isn't it? You could add it yourself, absent any resource constraints.
No most thing’s definitely don’t work like this. I don’t expect my x86 program to stop working after a software update or not to work on new x86 CPUs that’s just ridiculous.
Also if you expect anyone to compile anything you probably haven’t shipped anything in your life.
ROCm is a pile of rubbish until they throw it out and actually have a model that would guarantee forward and backward compatibility it would remain useless for anyone who actually builds software other people use.
Also if you expect anyone to compile anything you probably haven’t shipped anything in your life.
ROCm is a pile of rubbish until they throw it out and actually have a model that would guarantee forward and backward compatibility it would remain useless for anyone who actually builds software other people use.
> I don’t expect my x86 program to stop working after a software update or not to work on new x86 CPUs that’s just ridiculous.
Your x86 program doesn't work on Apple Silicon without something equivalent to a recompile. Old operating systems very commonly can't run on bare metal new hardware because they don't have drivers for it.
Even the IR isn't actually machine code, it's just a binary format of something that gets compiled into actual machine code right before use.
> Also if you expect anyone to compile anything you probably haven’t shipped anything in your life.
Half the software people run uses JIT compilation of some kind.
Your x86 program doesn't work on Apple Silicon without something equivalent to a recompile. Old operating systems very commonly can't run on bare metal new hardware because they don't have drivers for it.
Even the IR isn't actually machine code, it's just a binary format of something that gets compiled into actual machine code right before use.
> Also if you expect anyone to compile anything you probably haven’t shipped anything in your life.
Half the software people run uses JIT compilation of some kind.
The only real remaining fronts in the war are consoles and smartphones, and NVIDIA just signed a deal to license GeForce IP to mediatek so that nut is being cracked as well, mediatek gives them mass-market access for CUDA tech, DLSS, and other stuff. Nintendo has essentially a mobile console platform and will be doing DLSS too on an Orin NX 8nm chip soon (very cheap) using that same smartphone-level DLSS (probably re-optimized for lower resolutions). Samsung 8nm is exactly Nintendo's kind of cheap, it'll happen.
The "NVIDIA they might leave graphics and just do AI in the future!" that people sometimes do is just such a batshit take because it's graphics that opens the door to all these platforms, and it's graphics that a lot of these accelerators center around. What good is DLSS without a graphics platform? Do you sign the Mediatek deal without a graphics platform? Do you give up workstation graphics and OptiX and raysampling and all these other raytracing techs they've spent billions developing, or do you just choose to do all the work of making Quadros and all this graphics tech but then not do gaming drivers and give up that gaming revenue and all the market access that comes with it? It's faux-intellectualism and ayymd wish-casting at its finest, it makes zero sense when you consider the leverage they get from this R&D spend across multiple fields.
CUDA is unshakeable precisely because NVIDIA is absolutely relentless in getting their foot in the door, then using that market access to build a better mousetrap with software that everyone else is constantly rushing to catch up to. Every segment has some pain points and NVIDIA figures out what they are and where the tech is going and builds something to address that. AMD's approach of trying to surgically tap high-margin segments before they have a platform worth caring about is fundamentally flawed, they're putting the cart before the horse, and that's why they keep spinning their wheels on GPGPU adoption for the last 15 years. And that's what people are clamoring for NVIDIA to do with this idea of "abandon graphics and just do AI" and it's completely batshit.
Intel gets it, at least. OneAPI is focused on being a viable product and they'll move on from there. ROCm is designed for supercomputers where people get paid to optimize for it - it's an embedded product, not a platform. Like you can't even use the binaries you compile on anything except one specific die (not even a generation, "this is binary is for Navi 21, you need the Navi 23 binary"). CUDA is an ecosystem that people reach for because there's tons of tools and libraries and support, and it works seamlessly and you can deliver an actual product that consumers can use. ROCm is something that your boss tells you you're going to be using because it's cheap, you are paying to engineer it from scratch, you'll be targeting your company's one specific hardware config, and it'll be inside a web service so it'll be invisible to end-users anyway. It's an embedded processor inside some other product, not a product itself. That's what you get from the "surgically tap high-margin segments" strategy.
But the Mediatek deal is big news. When we were discussing the ARM acquisition etc people totally scoffed that NVIDIA would ever license GeForce IP. And when that fell through, they went ahead and did it anyway. Because platform access matters, it's the foot in the door. The ARM deal was never about screwing licensees or selling more tegras, that would instantly destroy the value of their $40b acquisition. It was 100% always about getting GeForce as the base-tier graphics IP for ARM and getting that market access to crack one of the few remaining segments where CUDA acceleration (and other NVIDIA technologies) aren't absolutely dominant.
And graphics is the keystone of all of it. Market access, software, acceleration, all of it falls apart without the graphics. They'd just be ROCm 2.0 and nobody wants that, not even AMD wants to be ROCm. AMD is finally starting to see it and move away from it, it would be wildly myopic for NVIDIA to do that and Jensen is not an idiot.
Not entirely a direct response to you but I've seen that sentiment a ton now that AI/enterprise revenue has passed graphics and it drives me nuts. Your comment about "what would it take to get Radeon ahead of CUDA mindshare" kinda nailed it, CUDA literally is winning so hard that people are fantasizing about "haha but what if NVIDIA got tired of winning and went outside to ride bikes and left AMD to exploit graphics in peace" and it's crazy to think that could ever be a corporate strategy. Why would they do that when Jensen has spent the last 25 years building this graphics empire? Complete wish-casting, “so dominant that people can’t even imagine the tech it would take to break their ubiquity” is exactly where Jensen wants to be, and if anything they are still actively pushing to be more ubiquitous. That's why their P/E are insane (probably overhyped even at that, but damn are they good).
If there is a business to be made doing only AI hardware and not a larger platform (and I don’t think there is, at that point you’re a commodity like dozens of other startups) it certainly looks nothing like the way nvidia is set up. These are all interlocking products and segments and software, you can’t cut any one of them away without gutting some other segment. And fundamentally the surgical revenue approach doesn’t work, AMD has continuously showed that for the last 15 years.
Being unwilling to catch a falling knife by cutting prices to the bone doesn’t mean they don’t want to be in graphics. The consumer GPU market is just unavoidably soft right now, almost irregardless of actual value (see: 4070 for $600 with a $100 GC at microcenter still falling flat). Even $500 for a 4070 is probably flirting with being unsustainably low (they need to fund R&D for the next gen out of these margins) but if a de-facto $500 price doesn’t spark people’s interests/produce an increase in sales they’re absolutely not going any lower than that this early in the cycle. They’ll focus on margin on the sales they can actually make, rather than chasing the guy who is holding out for 4070 to be $329. People don't realize it but obstinently refusing to buy at any price (even a good deal) is paradoxically creating an incentive to just ignore them and chase margins.
It doesn’t mean they don’t want to be in that market but they’re not going to cut their own throat, mis-calibrate consumer expectations, etc.
Just as AMD is finding out with the RX 7600 launch - if you over-cut on one generation, the next generation becomes a much harder sell. Which is the same lesson nvidia learned with the 1080 ti and 20-series. AMD is having their 20-series moment right now, they over-cut on the old stuff and the new stuff is struggling to match the value. And the expectations of future cuts is only going to dampen demand further, they’re Osborne Effect’ing themselves with price cuts everyone knows are coming. Nvidia smartened up - if the market is soft and the demand just isn’t there… make less gaming cards and shift to other markets in the meantime. Doesn’t mean they don’t want to be in graphics.
The "NVIDIA they might leave graphics and just do AI in the future!" that people sometimes do is just such a batshit take because it's graphics that opens the door to all these platforms, and it's graphics that a lot of these accelerators center around. What good is DLSS without a graphics platform? Do you sign the Mediatek deal without a graphics platform? Do you give up workstation graphics and OptiX and raysampling and all these other raytracing techs they've spent billions developing, or do you just choose to do all the work of making Quadros and all this graphics tech but then not do gaming drivers and give up that gaming revenue and all the market access that comes with it? It's faux-intellectualism and ayymd wish-casting at its finest, it makes zero sense when you consider the leverage they get from this R&D spend across multiple fields.
CUDA is unshakeable precisely because NVIDIA is absolutely relentless in getting their foot in the door, then using that market access to build a better mousetrap with software that everyone else is constantly rushing to catch up to. Every segment has some pain points and NVIDIA figures out what they are and where the tech is going and builds something to address that. AMD's approach of trying to surgically tap high-margin segments before they have a platform worth caring about is fundamentally flawed, they're putting the cart before the horse, and that's why they keep spinning their wheels on GPGPU adoption for the last 15 years. And that's what people are clamoring for NVIDIA to do with this idea of "abandon graphics and just do AI" and it's completely batshit.
Intel gets it, at least. OneAPI is focused on being a viable product and they'll move on from there. ROCm is designed for supercomputers where people get paid to optimize for it - it's an embedded product, not a platform. Like you can't even use the binaries you compile on anything except one specific die (not even a generation, "this is binary is for Navi 21, you need the Navi 23 binary"). CUDA is an ecosystem that people reach for because there's tons of tools and libraries and support, and it works seamlessly and you can deliver an actual product that consumers can use. ROCm is something that your boss tells you you're going to be using because it's cheap, you are paying to engineer it from scratch, you'll be targeting your company's one specific hardware config, and it'll be inside a web service so it'll be invisible to end-users anyway. It's an embedded processor inside some other product, not a product itself. That's what you get from the "surgically tap high-margin segments" strategy.
But the Mediatek deal is big news. When we were discussing the ARM acquisition etc people totally scoffed that NVIDIA would ever license GeForce IP. And when that fell through, they went ahead and did it anyway. Because platform access matters, it's the foot in the door. The ARM deal was never about screwing licensees or selling more tegras, that would instantly destroy the value of their $40b acquisition. It was 100% always about getting GeForce as the base-tier graphics IP for ARM and getting that market access to crack one of the few remaining segments where CUDA acceleration (and other NVIDIA technologies) aren't absolutely dominant.
And graphics is the keystone of all of it. Market access, software, acceleration, all of it falls apart without the graphics. They'd just be ROCm 2.0 and nobody wants that, not even AMD wants to be ROCm. AMD is finally starting to see it and move away from it, it would be wildly myopic for NVIDIA to do that and Jensen is not an idiot.
Not entirely a direct response to you but I've seen that sentiment a ton now that AI/enterprise revenue has passed graphics and it drives me nuts. Your comment about "what would it take to get Radeon ahead of CUDA mindshare" kinda nailed it, CUDA literally is winning so hard that people are fantasizing about "haha but what if NVIDIA got tired of winning and went outside to ride bikes and left AMD to exploit graphics in peace" and it's crazy to think that could ever be a corporate strategy. Why would they do that when Jensen has spent the last 25 years building this graphics empire? Complete wish-casting, “so dominant that people can’t even imagine the tech it would take to break their ubiquity” is exactly where Jensen wants to be, and if anything they are still actively pushing to be more ubiquitous. That's why their P/E are insane (probably overhyped even at that, but damn are they good).
If there is a business to be made doing only AI hardware and not a larger platform (and I don’t think there is, at that point you’re a commodity like dozens of other startups) it certainly looks nothing like the way nvidia is set up. These are all interlocking products and segments and software, you can’t cut any one of them away without gutting some other segment. And fundamentally the surgical revenue approach doesn’t work, AMD has continuously showed that for the last 15 years.
Being unwilling to catch a falling knife by cutting prices to the bone doesn’t mean they don’t want to be in graphics. The consumer GPU market is just unavoidably soft right now, almost irregardless of actual value (see: 4070 for $600 with a $100 GC at microcenter still falling flat). Even $500 for a 4070 is probably flirting with being unsustainably low (they need to fund R&D for the next gen out of these margins) but if a de-facto $500 price doesn’t spark people’s interests/produce an increase in sales they’re absolutely not going any lower than that this early in the cycle. They’ll focus on margin on the sales they can actually make, rather than chasing the guy who is holding out for 4070 to be $329. People don't realize it but obstinently refusing to buy at any price (even a good deal) is paradoxically creating an incentive to just ignore them and chase margins.
It doesn’t mean they don’t want to be in that market but they’re not going to cut their own throat, mis-calibrate consumer expectations, etc.
Just as AMD is finding out with the RX 7600 launch - if you over-cut on one generation, the next generation becomes a much harder sell. Which is the same lesson nvidia learned with the 1080 ti and 20-series. AMD is having their 20-series moment right now, they over-cut on the old stuff and the new stuff is struggling to match the value. And the expectations of future cuts is only going to dampen demand further, they’re Osborne Effect’ing themselves with price cuts everyone knows are coming. Nvidia smartened up - if the market is soft and the demand just isn’t there… make less gaming cards and shift to other markets in the meantime. Doesn’t mean they don’t want to be in graphics.
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Tensorflow is optimized for TPU's which isn't really consumer-grade hardware.
Unrelated question for the HN experts:
My sibling commenter is shadowbanned, but if you look into their comment history, there are occasionally comments that are not dead. How does this happen?
My sibling commenter is shadowbanned, but if you look into their comment history, there are occasionally comments that are not dead. How does this happen?
Somebody clicked on the timestamp of that post and used the "vouch" link to unhide it. I sometimes do that for comments from new accounts that been hidden by some overzealous anti-spam heuristic.
Helpful to know, I've seen a few hidden posts that seem reasonable but didn't know I could do that.
Isn't the coral stick a TPU?
Yes, although availability recently has been pretty bad following the chip shortage, and prices skyrocketed to ~300 dollars. Not sure if situation returning to normal yet. Similar woes to the Raspberry Pi etc.
I needed two for a project and ended up paying a lot more than I wanted for used ones.
For those not familiar, consumer/hobbyist grade TPUs:
https://coral.ai/products/
I needed two for a project and ended up paying a lot more than I wanted for used ones.
For those not familiar, consumer/hobbyist grade TPUs:
https://coral.ai/products/
Google's first TPU was developed a year after Tensorflow. And for that matter, Tensorflow works fine with CUDA, was originally entirely built for CUDA, and it's super weird the way it's being referenced in here.
Tensorflow lost out to Pytorch because the former is grossly complex for the same tasks, with a mountain of dependencies, as is the norm for Google projects. Using it was such a ridiculous pain compared to Pytorch.
And anyone can use a mythical TPU right now on the Google Cloud. It isn't magical, and is kind of junky compared to an H100, for instance. I mean...Google's recent AI supercomputer offerings are built around nvidia hardware.
CUDA keeps winning because everyone else has done a horrendous job competing. AMD, for instance, had the rather horrible ROCm, and then they decided that they would gate their APIs to only their "business" offerings while nvidia was happy letting it work on almost anything.
Tensorflow lost out to Pytorch because the former is grossly complex for the same tasks, with a mountain of dependencies, as is the norm for Google projects. Using it was such a ridiculous pain compared to Pytorch.
And anyone can use a mythical TPU right now on the Google Cloud. It isn't magical, and is kind of junky compared to an H100, for instance. I mean...Google's recent AI supercomputer offerings are built around nvidia hardware.
CUDA keeps winning because everyone else has done a horrendous job competing. AMD, for instance, had the rather horrible ROCm, and then they decided that they would gate their APIs to only their "business" offerings while nvidia was happy letting it work on almost anything.
Best explanation so far. I am surprised OpenCL never gained much traction. Any idea why?
The same reason most of AMD's 'open' initiatives don't gain traction: they throw it out there and hope things will magically work out and that a/the community will embrace it as the standard. It takes more work than that. What AMD historically hasn't done is the real grunge work of addressing the limitations of their products/APIs and continuing to invest in them long term. See how the OpenCL (written by AMD) Cycles renderer for Blender worked out, for example.
Something AMD doesn't seem to understand/accept is that since they are consistently lagging nVidia on both the hardware and software front, nVidia can get away with some things AMD can't. Everyone hates nVidia for it, but unless/until AMD wises up they're going to keep losing.
Something AMD doesn't seem to understand/accept is that since they are consistently lagging nVidia on both the hardware and software front, nVidia can get away with some things AMD can't. Everyone hates nVidia for it, but unless/until AMD wises up they're going to keep losing.
what did you do to get all your posts automatically dead?
frameworks can be agnostic to the underlying library. What are formidable alternatives to cuda ?
The stock price makes absolutely no sense, but the AI hype is real so I won't be shorting.
Just give you a crude metaphor - buying NVDA is like buying a $10 million dollar house to collect $10,000 in rent a year. The price to earnings is bonkers. This valuation only makes sense if somehow Nvidia is using alien technology that couldn't possible by reproduced in the next two decades by any other company.
Just give you a crude metaphor - buying NVDA is like buying a $10 million dollar house to collect $10,000 in rent a year. The price to earnings is bonkers. This valuation only makes sense if somehow Nvidia is using alien technology that couldn't possible by reproduced in the next two decades by any other company.
Your last point is why it feels to me like the better investment right now is everyone and anyone else that will be working very hard to be at where NVDA is at right now. I suppose the obvious answer here is AMD, but surely there's other minor companies too that could see a huge amount of investment.
Agreed, the fundamentals are off and drunk on hype. Where else can investors put their money?
TSMC's P/E is under 20. Disclaimer: I hold
TSMC is cheap because of the risk of invasion, otherwise they would have a pretty insane valuation already.
How is their fab build in AZ going?
Not incredible I hear, but that could just be Morris trying to get more subsidies.
P/E is under 20 is the norm, not the exception. Even companies like meta, apple etc. had pe near 10 for long time.
Yes, but also TSMC is the chip manufacturer that makes NVidia's GPU compute units.
Goog, Meta? They are by far leading in AI research, and they've both developed their own chip. Apple is also going to come out ahead with their Neural chip - imagine chatGPT and stable diffusion becoming part of the iOS SDK
Yes, I can imagine commoditization of such models. Plus they own their chips/silicon and have billions of devices deployed. I think Apple is one to watch because UX is challenge for AI integration. ChatGPT made UX for LLMs user friendly. Apple's design history is superior to its competitors.
I would add MSFT due to their exposure to OpenAI and their very successful previous and upcoming integrations (GH Co-Pilot, Bing revamp, upcoming Excel Co-Pilot etc.)
Meta has their own chip they actually use? IIRC LLaMA was trained on A100s.
Apple is non-viable for LLM workloads.
Apple is non-viable for LLM workloads.
CUDA is a success because 1) it works on all NVIDIA GPUs made since 2006 2) it works on both Windows and Linux.
This may seem like a very low bar to clear, but AMD continues to struggle with it. I don't understand it. They act as if GPU compute was a fad not worth investing in.
This may seem like a very low bar to clear, but AMD continues to struggle with it. I don't understand it. They act as if GPU compute was a fad not worth investing in.
Seriously, knowing very little about such low level stuff, why is this taking so long? George Hotz is starting a company on this premise
The Geohotz post has some good explanations for why this is happening.
https://geohot.github.io//blog/jekyll/update/2023/05/24/the-...
CUDA works, ROCm doesn't work well. Very few people want to run stable diffusion inference, fine tune LLaMA, train a large foundation model on AMD cards.
OpenAI has put in some work on Triton, Modular is working on Mojo, and tiny corp is working on their alternative.
Until some of those alternatives work as well as CUDA, people will mostly choose to buy Nvidia cards.
The monopoly is under attack from multiple angles, but they'll be able to print some good cash in the (potentially long) meantime.
Oh, and still significant supply shortages at many cloud providers. And now Nvidia's making more moves to renting GPUs directly. It'll be interesting to see how long it takes them to be able to have their supply meet demand.
https://geohot.github.io//blog/jekyll/update/2023/05/24/the-...
CUDA works, ROCm doesn't work well. Very few people want to run stable diffusion inference, fine tune LLaMA, train a large foundation model on AMD cards.
OpenAI has put in some work on Triton, Modular is working on Mojo, and tiny corp is working on their alternative.
Until some of those alternatives work as well as CUDA, people will mostly choose to buy Nvidia cards.
The monopoly is under attack from multiple angles, but they'll be able to print some good cash in the (potentially long) meantime.
Oh, and still significant supply shortages at many cloud providers. And now Nvidia's making more moves to renting GPUs directly. It'll be interesting to see how long it takes them to be able to have their supply meet demand.
I'm surprised I didn't see this frontpage HN a few days ago, but a very interesting read.
Edit: Nevermind, found a huge thread from 2 days ago Lol.
Edit: Nevermind, found a huge thread from 2 days ago Lol.
Just use hckrnews.com , it shows frontpage posts from previous days.
I had a meme joke about how AI would come to be by making people mine for crypto but now we're seeing LLMs take the fore-front of AI and causing us to reach for more and more parameters.
It reminds me of when the YOLOv3 model came out and every single upgrade just gave us more and more features and capabilities (the v8 has auto segmentation).
AMD dropped the ball on this, just like Intel when Ryzen dropped, I just don't see a way for them to bring it around.
It reminds me of when the YOLOv3 model came out and every single upgrade just gave us more and more features and capabilities (the v8 has auto segmentation).
AMD dropped the ball on this, just like Intel when Ryzen dropped, I just don't see a way for them to bring it around.
Meanwhile, Nvidia Short Sellers Lose $2.3 Billion in One Day as Stock Soars https://www.bloomberg.com/news/articles/2023-05-25/nvidia-sh...
Shorting into an irrational bull run is a great way to learn an expensive lesson on the difference in power between logic and emotion.
“The market can stay irrational longer than you can stay solvent.” - John Maynard Keynes
Particularly applicable here, couldn't resist myself.
Particularly applicable here, couldn't resist myself.
I think this 20%+ stock move is mostly a combination of:
1) Heavy short option interest going into earnings
2) A large beat announced in after hours
Major market players can take advantage of large earnings surprises by manipulating a stock in after hours. It is possible to trigger very large movements with very little volume because most participants don't have access to after hours trading.
When the market opens the next day the "false" gains should typically be wiped out unless the move is large enough to force the closing of certain positions. In this case, it looks like there was a clamor to escape long puts and short calls.
1) Heavy short option interest going into earnings
2) A large beat announced in after hours
Major market players can take advantage of large earnings surprises by manipulating a stock in after hours. It is possible to trigger very large movements with very little volume because most participants don't have access to after hours trading.
When the market opens the next day the "false" gains should typically be wiped out unless the move is large enough to force the closing of certain positions. In this case, it looks like there was a clamor to escape long puts and short calls.
Skepticism is why momentum works in stocks. People tend to be afraid to buy, or even want to short, stocks that have just risen a lot. That's why it may take a while for a stock to rise to its true price after new information comes out.
The momentum behind NVDA as well as some other tech stocks right now (SMCI, META, NFLX) is frankly stunning. Nary a dip for 6 months. There is so much FOMO in the AI trade that I don't think it crashes back down to earth very soon. Still I'm way too scared to try to get in late.
And in this case the F in FOMO is real. Not just a feeling of missing out, but fear that all your other investments are going to zero as AI replaces entire industries, for example.
The momentum behind NVDA as well as some other tech stocks right now (SMCI, META, NFLX) is frankly stunning. Nary a dip for 6 months. There is so much FOMO in the AI trade that I don't think it crashes back down to earth very soon. Still I'm way too scared to try to get in late.
And in this case the F in FOMO is real. Not just a feeling of missing out, but fear that all your other investments are going to zero as AI replaces entire industries, for example.
Probably more from 50% growth guidance they gave for next quarter and how much that beat expectations.
Yes, the affect of short positions during price jumps is not always discussed / hidden variable.
The super interesting thing about NVDA is that you can bet on:
- Crypto
- AI
- Gaming / Entertainment
- Self driving cars
- VR / Metaverse (whatever that is)
I'm very bullish on the company.
- Crypto
- AI
- Gaming / Entertainment
- Self driving cars
- VR / Metaverse (whatever that is)
I'm very bullish on the company.
Frankly gaming is still their only reliable base. Their AI lead is going to get competed away from every angle within 12-24 months. The AI boom is very much like the Crypto boom.
Gaming might be their worst business from a growth and narrative perspective. Wall street in general doesn't really care about gaming, just see how those companies are priced.
Enterprise compute has been and will continue to be NVidias bread and butter going forward, and they have been betting on this for the past decade. Whether enterprise compute will be for AI, studio graphics, simulation, FSD, etc. those are all more lucrative and imo more interesting from a growth perspective vs their gaming segment. b2b companies have much higher ceilings than b2c.
Enterprise compute has been and will continue to be NVidias bread and butter going forward, and they have been betting on this for the past decade. Whether enterprise compute will be for AI, studio graphics, simulation, FSD, etc. those are all more lucrative and imo more interesting from a growth perspective vs their gaming segment. b2b companies have much higher ceilings than b2c.
What signals to you that other chip makers are capable of competing in learning and inference computation?
We know they'll be motivated, but can they actually compete is the question.
We know they'll be motivated, but can they actually compete is the question.
There is way too much money involved for anyone with the capability to not get into the area. Intel is starving for growth opportunities, investing heavily into the GPU space and very enterprise oriented.
AMD is shitting the bed but might find partners in this space.
Apple is a dark horse and they are very much into the on-device AI abilities.
Amazon, Microsoft, Google can all throw endless amounts of software and hardware at the problem and there have been many advances in the AI space regarding training smaller models that can compete with the big ones.
Open source is going nuts in this space, as well as a ton of academic research. This is likely the biggest dark horse. Major advances are happening weekly.
AMD is shitting the bed but might find partners in this space.
Apple is a dark horse and they are very much into the on-device AI abilities.
Amazon, Microsoft, Google can all throw endless amounts of software and hardware at the problem and there have been many advances in the AI space regarding training smaller models that can compete with the big ones.
Open source is going nuts in this space, as well as a ton of academic research. This is likely the biggest dark horse. Major advances are happening weekly.
Gaming revenue is down. Check their earnings release.
The best part about this if you have that much conviction, you can buy 2025 puts and make a ton of money if you're right. Good luck!
Which hardware/software pairing do you see dethroning NVIDIA that quickly?
Honestly I'd watch for Intel. No chance their GPUs are as good within a couple years, but they could easily be 80% as good for half the price or less. Intel is willing to lose money for a while on this to grow their gpu reputation. If theyre is able to create that product and reliably stock it nvdia will have to cut their prices.
Nvidia effectively has no competition right now due to AMDs software issues. It's hard to see how that can continue with how big their cap is. Someone will be able to create a competitive product.
Nvidia effectively has no competition right now due to AMDs software issues. It's hard to see how that can continue with how big their cap is. Someone will be able to create a competitive product.
Self driving cars?
Don't they have specialized chips?
Don't they have specialized chips?
https://blogs.nvidia.com/blog/category/auto/
PS - you can literally go to https://www.nvidia.com/en-us/ and read the menu to see what they do...
PS - you can literally go to https://www.nvidia.com/en-us/ and read the menu to see what they do...
The result of this optimism in big cap tech companies is that many smaller cap shares in industries such as financial services, insurance or industrial distribution are trading for historically cheap valuations. It appears there is very little investor interest in them. I think it's a wonderful time to be a long-term investor.
Yeah, too bad I owned those before the ai hype sucked all the capital out of the room...
Respect to Jensen, one of the OGs of the valley and a good dude. But LLMs (eventually) running on iphone hardware will crater this run
Not sure how much inference on the edge will impact things unless you think we’ll hit “peak training” in the near future. I would safely wager that most H100 nodes will be used for training rather than inference.
Genuine question to all concerned about PE ratios. Why is PE not subject to 'new normals'? A lot of people seem to reject stocks because they're 'expensive' which to me seems like a relative term. There are a lot more retail investors out there now.
It is helpful to think of it in terms of unit economics. If a company sells a product, it needs to eventually make profit on each unit. (cost < price per unit)
When it comes to owning a share, it eventually needs to make the investor money through dividends or price appreciation. The argument for high PE ratio is price appreciation (growth), but exponential growth is very hard to sustain, so PE ratio has to come down to a certain level in the long term. Also, there is always a risk of a company declining or even folding.
When it comes to owning a share, it eventually needs to make the investor money through dividends or price appreciation. The argument for high PE ratio is price appreciation (growth), but exponential growth is very hard to sustain, so PE ratio has to come down to a certain level in the long term. Also, there is always a risk of a company declining or even folding.
Because if all that is left of the stock market is offloading your hand to a bigger fool then it's way more fun to fly to Vegas and do it at the poker table.
At least you have to actually look in the eyes the guy you are screwing over.
You can't buy stuff because you think other people will also buy, that would mean that you are buying/selling opinions not companies.
At least you have to actually look in the eyes the guy you are screwing over.
You can't buy stuff because you think other people will also buy, that would mean that you are buying/selling opinions not companies.
There are lots of factors that can influence PE but if your long term investing thesis is that retail traders will always love NVDA the most (and won't move on to the next hype train, as they did with TSLA and many others) that seems... unlikely?
If you can predict the next hype cycle or when exactly this one will end, you will make a zillion dollars.
If you are talking about entire markets rather than individual companies then p/e matters because the only examples we have of markets with extreme p/e ratios all ended in disaster.
You should still buy stocks for the long term regardless of PE ratios because you're right, it's not possible to predict what they'll do. Unfortunately when you buy at a high pe you can't anticipate as much return in the future, all else equal.
If you can predict the next hype cycle or when exactly this one will end, you will make a zillion dollars.
If you are talking about entire markets rather than individual companies then p/e matters because the only examples we have of markets with extreme p/e ratios all ended in disaster.
You should still buy stocks for the long term regardless of PE ratios because you're right, it's not possible to predict what they'll do. Unfortunately when you buy at a high pe you can't anticipate as much return in the future, all else equal.
I mean the 5yr licensed that come bundled with H100 just because you technically aren't supposed to use consumer class GPU in a data center... whoever came up with this is definitely following in the footsteps of Adobe's licensing shenanigans
This post from Fullstack Deeplearing analyzed cloud GPUs, seems pertinent to discussions here about NVIDIA, competitors, and determining true value of related AI/chip stocks: https://fullstackdeeplearning.com/cloud-gpus/
- HN post: https://news.ycombinator.com/item?id=36025099
- HN post: https://news.ycombinator.com/item?id=36025099
Good. nvidia deserves what they're getting, imho, because they started early and continued to invest in graphics and then GPUs, with support for both Windows and Linux.
Yet another example of how long term platform investment pays dividends.
And yet another reminder how far behind opencl/AMD is
And yet another reminder how far behind opencl/AMD is
I wonder if https://tenstorrent.com/ will be able to take some market share away
Not any time soon, I believe.
AI hardware is useless without software ecosystem (AMD and Intel could tell a story about that).
Latest marketing materials of Tenstorrent tell stories about great chips and licensing deals, but not a single word about the software side of the things.
Compare that to how much NVidia talks about software on its presentations.
AI hardware is useless without software ecosystem (AMD and Intel could tell a story about that).
Latest marketing materials of Tenstorrent tell stories about great chips and licensing deals, but not a single word about the software side of the things.
Compare that to how much NVidia talks about software on its presentations.
The four commas club.
Going to have to buy a car whose doors open in a new way.
Going to have to buy a car whose doors open in a new way.
What are the companies supplying the components/materials to Nvidia and how are their stocks currently performing?
Not a direct answer, but Nvidia manufactures at TSMC. Yesterday morning Nvidia's stock jumped from its Wednesday evening earnings announcement, and TSM jumped from $90.14 to over $100 yesterday morning.
NVIDIA PE ratio as of May 25, 2023 is 139.44. If you wanted to invest you are too late. Save your money.
TPUs are GPUs basically, so it makes sense. I wouldn't be too quick to call it though.
So NVIDIA will be to AI what Adobe is to media production: An absolute cancer.
Don't hate the neurons? hate the game.
Am I the only one thinking that NVIDIA doesn't really have a moat here?
How many A100 or H100 cards are actually manufactured annually? A few hundred thousand, if that?
Suddenly, there's a big demand. Microsoft mentioned buying something like 25,000 of the H100 cards for GPT-4 and ongoing training. I'm certain they're not paying retail pricing, so that's a few hundred million in revenue for NVIDIA. They're probably the biggest single customer right now, except perhaps for Amazon.
NVIDIA's revenue in 2022 was $27 billion. The additional H100 cards they've sold this year is a fraction of that. Their retail prices have spiked and availability has dropped because supply is inelastic and there aren't any other suppliers with equivalent products... yet.
Fundamentally, a H100 is not that different to a desktop GPU! It's a little bigger, the math units have a different ALU balance, and they use high-bandwidth memory (HBM), but that's it. There's nothing else really special about them. Unlike a CPU, which is extremely complex, a GPU is a relatively simple unit repeated over 10K times. In some sense, it's a copy-paste exercise.
NVIDIA has a tiny moat, because AMD simply didn't bother to go after what was -- until now -- a relatively small market.
That market is going to be huge, but that invites competition! When tens or even hundreds of billions are on the table, you can bet your bottom dollar that AMD, Intel, Google, and even Facebook won't sit idly by and watch NVIDIA walk off with it.
So what moat does NVIDIA have?
CUDA is like assembly language. PyTorch can target other back-ends. Compilers can target other GPU instructions sets. Throw a billion dollars at this, and it suddenly becomes an eminently solvable problem. Just look at Apple's CPU transitions and Amazon rolling out ARM cloud servers.
A card with HBM memory? AMD did that first! They already have the tech.
A GPU + CPU hybrid with unified memory? Both Intel and Apple have either existing or upcoming products. Intel for example just abandoned[1] a HPC CPU design that was a combo of a GPU+CPU surrounded by HBM chips acting as a cache for terabytes of DDR5 memory -- ideal for training or running very large language models!
A GPU with a huge amount of 16-bit, 8-bit, or 4-bit ops/sec? Guess what: this is easier than getting high performance with 64-bit floats! You can literally brute force optimal circuit layouts for 4-bit ALUs. No need to be clever at all. All you need is the ability to manufacture "3nm" chips. TSMC does that, not NVIDIA. Intel and Samsung are catching up, rapidly.
Fundamentally, the programming interface 99% of AI researchers use are high-level languages like Python or maybe C++. Compilers exist. Even with CUDA, diverse instruction sets and capabilities exist.
So.. where's the moat!?
[1] Ooo, I bet they feel real stupid right now for throwing in the towel literally months before the LLM boom started taking off.
How many A100 or H100 cards are actually manufactured annually? A few hundred thousand, if that?
Suddenly, there's a big demand. Microsoft mentioned buying something like 25,000 of the H100 cards for GPT-4 and ongoing training. I'm certain they're not paying retail pricing, so that's a few hundred million in revenue for NVIDIA. They're probably the biggest single customer right now, except perhaps for Amazon.
NVIDIA's revenue in 2022 was $27 billion. The additional H100 cards they've sold this year is a fraction of that. Their retail prices have spiked and availability has dropped because supply is inelastic and there aren't any other suppliers with equivalent products... yet.
Fundamentally, a H100 is not that different to a desktop GPU! It's a little bigger, the math units have a different ALU balance, and they use high-bandwidth memory (HBM), but that's it. There's nothing else really special about them. Unlike a CPU, which is extremely complex, a GPU is a relatively simple unit repeated over 10K times. In some sense, it's a copy-paste exercise.
NVIDIA has a tiny moat, because AMD simply didn't bother to go after what was -- until now -- a relatively small market.
That market is going to be huge, but that invites competition! When tens or even hundreds of billions are on the table, you can bet your bottom dollar that AMD, Intel, Google, and even Facebook won't sit idly by and watch NVIDIA walk off with it.
So what moat does NVIDIA have?
CUDA is like assembly language. PyTorch can target other back-ends. Compilers can target other GPU instructions sets. Throw a billion dollars at this, and it suddenly becomes an eminently solvable problem. Just look at Apple's CPU transitions and Amazon rolling out ARM cloud servers.
A card with HBM memory? AMD did that first! They already have the tech.
A GPU + CPU hybrid with unified memory? Both Intel and Apple have either existing or upcoming products. Intel for example just abandoned[1] a HPC CPU design that was a combo of a GPU+CPU surrounded by HBM chips acting as a cache for terabytes of DDR5 memory -- ideal for training or running very large language models!
A GPU with a huge amount of 16-bit, 8-bit, or 4-bit ops/sec? Guess what: this is easier than getting high performance with 64-bit floats! You can literally brute force optimal circuit layouts for 4-bit ALUs. No need to be clever at all. All you need is the ability to manufacture "3nm" chips. TSMC does that, not NVIDIA. Intel and Samsung are catching up, rapidly.
Fundamentally, the programming interface 99% of AI researchers use are high-level languages like Python or maybe C++. Compilers exist. Even with CUDA, diverse instruction sets and capabilities exist.
So.. where's the moat!?
[1] Ooo, I bet they feel real stupid right now for throwing in the towel literally months before the LLM boom started taking off.
CUDA lock in, and network effect, is the main part I think. Even though other vendors can build CUDA compatibility (like AMD did) the quality is likely to keep trailing NVidia. Plus the datacenter TPU market is not yet really formed, even though they get better perf/$ and better perf/watt.
On the other hand it's cool to see that programming language tech as the keystone but on the other hand it's frustrating and tragic that the whole software stack and dev exp landscape is so crap in GPU/TPU land and the bar is so low that you NV win with a hard to use proprietary C++ based language and preside over a fragmented landscape of divided and conquered competition. Makes you wish the Intel Larrabee etc open platform direction had won out.
On the other hand it's cool to see that programming language tech as the keystone but on the other hand it's frustrating and tragic that the whole software stack and dev exp landscape is so crap in GPU/TPU land and the bar is so low that you NV win with a hard to use proprietary C++ based language and preside over a fragmented landscape of divided and conquered competition. Makes you wish the Intel Larrabee etc open platform direction had won out.
> CUDA lock in
The amount of software written for CUDA pales in comparison to the amount that has been written for Intel x86, yet two large companies migrated off it.
The lock-in with Intel was due to binary distribution (Windows software), and binary ABIs.
Everything to do with large language models is compiled from scratch, using high level languages.
The LLM codes themselves themselves are trivial things, easily replicated in a matter of hours on some other platforms. The hard part is gathering the training data and the compute.
The hard parts are not dependent on CUDA.
Look at it this way: Google developed Transformers and trained multiple LLMs using TPUs, not CUDA GPUs! The reason their LLMs are stupid is not because of the TPUs.
The amount of software written for CUDA pales in comparison to the amount that has been written for Intel x86, yet two large companies migrated off it.
The lock-in with Intel was due to binary distribution (Windows software), and binary ABIs.
Everything to do with large language models is compiled from scratch, using high level languages.
The LLM codes themselves themselves are trivial things, easily replicated in a matter of hours on some other platforms. The hard part is gathering the training data and the compute.
The hard parts are not dependent on CUDA.
Look at it this way: Google developed Transformers and trained multiple LLMs using TPUs, not CUDA GPUs! The reason their LLMs are stupid is not because of the TPUs.
x86 is a binary artifact instruction set, and software is written in higher level languages that can be recompiled. CUDA is a language that can target multiple ISAs. Microsoft and apple had incentive to keep options open to migrate off x86 and give a lot of support to their users doing so, and provide backwards compatibility in form of emulation etc. NVidia does this even better, users don't even notice when they are switching GPU ISAs.
In principle it's easy to recompile CPU side stuff too, but there are 3rd party component ecosystems, other dependency quirks, familiarity with the platform, familiar well known performance properties, sharing the same questions and answers on stackoverflow, etc. The lock-in network effect can be strong even if its individual contributing factors
I agree it's less locked in than eg Intel had it in the heyday. Ultimately we'll find out when the competition eventually shows up with reasons to consider switching.
In principle it's easy to recompile CPU side stuff too, but there are 3rd party component ecosystems, other dependency quirks, familiarity with the platform, familiar well known performance properties, sharing the same questions and answers on stackoverflow, etc. The lock-in network effect can be strong even if its individual contributing factors
I agree it's less locked in than eg Intel had it in the heyday. Ultimately we'll find out when the competition eventually shows up with reasons to consider switching.
I don't understand. Haven't graphics cards basically been obsolete for deep learning since the first TPUs arrived on the scene in ~2016? Lots of companies are offering TPU accelerators now, and it seems like the main thing Nvidia has going for it is momentum. But that doesn't explain this kind of valuation that's hundreds of times greater than their earnings. Personally, it seems a lot like Nvidia is to 2023 what Cisco was to 2000.
This quote from Wednesday’s TinyCorp article seems apropos:
“The current crop of AI chip companies failed. Many of them managed to tape out chips, some of those chips even worked. But not a single one wrote a decent framework to use those chips. They had similar performance/$ to NVIDIA, and way worse software. Of course they failed. Everyone just bought stuff from NVIDIA.”
https://geohot.github.io//blog/jekyll/update/2023/05/24/the-...
“The current crop of AI chip companies failed. Many of them managed to tape out chips, some of those chips even worked. But not a single one wrote a decent framework to use those chips. They had similar performance/$ to NVIDIA, and way worse software. Of course they failed. Everyone just bought stuff from NVIDIA.”
https://geohot.github.io//blog/jekyll/update/2023/05/24/the-...
1xH100 is faster than 8xA100 for a work-in-progress architecture I'm iterating on. Meanwhile the code doesn't work on TPUs right now because it hangs before initialization. (This is with PyTorch for what it's worth) All that to say Nvidia's hardware work and software evangelization has really paid off-- CUDA just works™ and performance continues to increase.
TPUs are good hardware, but TPUs are not available outside of GCP. There's not as much of an incentive for other companies to build software around TPUs like there is with CUDA. The same is likely true of chips like Cerebras' wafer scale accelerators as well.
Nvidia's won a stable lead on their competition that's probably not going to disappear for the next 2-5 years and could compound over that time.
TPUs are good hardware, but TPUs are not available outside of GCP. There's not as much of an incentive for other companies to build software around TPUs like there is with CUDA. The same is likely true of chips like Cerebras' wafer scale accelerators as well.
Nvidia's won a stable lead on their competition that's probably not going to disappear for the next 2-5 years and could compound over that time.
This has been my experience as well with TPUs and A100s. I haven’t used H100s yet (OOM on 1) but I believe the training throughout benchmarks from Nvidia on transformer workloads is 2.5x from A100s.
The effort to make (PyTorch) code run on TPUs is not worth it and my lab would rather rent (discounted) Nvidia GPUs than use free TRC credits we have at the moment. Additionally, at least in Jan 2023 when I last tried this, PyTorch XLA had a significant reduction in throughput so to really take advantage you would probably need to convert to Jax/TF which are used internally at Google and better supported.
The effort to make (PyTorch) code run on TPUs is not worth it and my lab would rather rent (discounted) Nvidia GPUs than use free TRC credits we have at the moment. Additionally, at least in Jan 2023 when I last tried this, PyTorch XLA had a significant reduction in throughput so to really take advantage you would probably need to convert to Jax/TF which are used internally at Google and better supported.
For transformer, v4 chip has 70-100% compute capacity and 40% memory of A100 for pretty much the same price. The only benefit is better networking speed for TPU compared to GPU cluster, allowing very large models to scale better, where for GPU model need to fit in NVlink connected GPUs, which is 320 billion parameters for 8*80 GB A100.
> For transformer, v4 chip has 70-100% compute capacity and 40% memory of A100 for pretty much the same price.
Note there are added costs when using V4 nodes such as the VM, storage and logging which can get $$$.
> where for GPU model need to fit in NVlink connected GPUs
Huh, where is this coming from? You can definitely efficiently scale transformers across multiple servers with parallelism and 1T is entirely feasible if you have the $. Nvidia demonstrated this back in 2021.
Note there are added costs when using V4 nodes such as the VM, storage and logging which can get $$$.
> where for GPU model need to fit in NVlink connected GPUs
Huh, where is this coming from? You can definitely efficiently scale transformers across multiple servers with parallelism and 1T is entirely feasible if you have the $. Nvidia demonstrated this back in 2021.
> Nvidia demonstrated this back in 2021.
Because Nvidia created a supercomputer with A100, with lot of focus for networking. Cloud providers don't give that option.
Because Nvidia created a supercomputer with A100, with lot of focus for networking. Cloud providers don't give that option.
Azure and AWS have both offered high-bandwidth cluster options that allow scaling beyond a single server for several months now.
Pretty sure MosaicML also does this but I haven't used their offering.
https://www.amazon.science/blog/scaling-to-trillion-paramete...
Pretty sure MosaicML also does this but I haven't used their offering.
https://www.amazon.science/blog/scaling-to-trillion-paramete...
The thing that all of these hardware companies don't understand is that it is the software that keeps the boys in the yard. If you don't have something that works as well as CUDA then it doesn't matter how good your hardware is. The only company that seems to understand this is nVidia, and they are the ones eating everyone's lunch. The software side is hard, it is expensive, it takes loads of developer hours and real life years to get right, and it is necessary for success.
I am not an ML expert but as an observer, others have said that why nvidia got right from the beginning, was actually the software support. Stuff like CUDA and good drivers and supporting libraries from over a decade ago? All the libs and researchers and all just use those libs and write software towards it. And as a result it works best on nvidia cards.
As someone closer to this in the industry (embedded ML.. and trying to compete) I agree with the sentiment. Their software is good, I willingly admit. Porting a model to embedded is hard. With NVIDIA, you basically don’t have to port. This has paid dividends for them, pun not intended.
I don’t really see the Nvidia monopoly on ML training stopping anytime soon.
I don’t really see the Nvidia monopoly on ML training stopping anytime soon.
But their valuation is based on forward (future) earnings, using an already obsolete technology.
Correct me if I'm wrong, but isn't OpenAI still using a ton of Nvidia tech behind the scenes? In addition to GPUs doesn't Nvidia also have dedicated ML hardware?
Yes, and all that CUDA software is effectively a moat. ROCm exists but after getting burned badly and repeatedly by OpenCL I'm disinclined to bet on it. At best, my winnings would be avoiding the green tax, at worst, I waste months like I did on OpenCL.
That said, AMD used to be in a dire financial situation, whereas now they can afford to fix their shit and actually give chase. NVIDIA has turned the thumb screws very far and they can probably turn them considerably further before researchers jump, but far enough to justify 150x? I have doubts.
That said, AMD used to be in a dire financial situation, whereas now they can afford to fix their shit and actually give chase. NVIDIA has turned the thumb screws very far and they can probably turn them considerably further before researchers jump, but far enough to justify 150x? I have doubts.
The fact that I hadn't even heard of ROCm until reading your post indicates they got a long way to go to catch up. I've heard of OpenCL but I don't know anyone who actually uses it. I think Apple has something for GPGPU for Metal with performance shaders or compute shaders, but I also don't know of anyone using it for anything, at least not in ML/AI.
It's a little irritating that Nvidia has effectively monopolized the GPGPU market so effectively; a part of me wonders if the best that that AMD could do is just make a CUDA-compatibility layer for AMD cards.
It's a little irritating that Nvidia has effectively monopolized the GPGPU market so effectively; a part of me wonders if the best that that AMD could do is just make a CUDA-compatibility layer for AMD cards.
If you look at the ROCm API you'll see that it's pretty much exactly that, a CUDA compatibility layer, but an identical API means little if the functionality behind it has different quirks and caveats and that's harder to assess. I am rooting for ROCm but I can't justify betting on it myself and I suspect most of the industry is in the same boat. For now.
My question is, is it feasible for AMD to build an ahead of time compiler that transparently translates CUDA instructions into whatever AMD could have, so things Just Work™? They'd be heavily incentivized to do so. Or even put hardware CUDA translators directly into the cards?
Or am I misunderstanding CUDA? I think of it as something like OpenGL/DirectX.
Or am I misunderstanding CUDA? I think of it as something like OpenGL/DirectX.
It is more expensive (in engineering cost) to port all the world's research (and your own one) to the TPU of your choice, than just paying NVIDIA.
Not one provider apart from GCP has TPUs, they don't have them available for consumers to buy & experiment with. No-one experiments
multi-day stuff on the cloud without big pockets or company money especially not PhD students or hobbyists
AWS has their own accelerators, and they're a much better value than their GPU instances.
For at least some applications, the details of the processor architecture are dominated by how much high-throughput RAM you can throw at the problem, and GPUs are by far the cheapest and most accessible way of cramming a bunch of high-throughput RAM into a computer. While it's not exactly a mainstream solution, some people have built AI rigs in 2022/2023 with used Vega cards because they're a cheap way to get HBM.
People underestimate how meme-y both the stock market and the underlying customer market is. I don't think there's anything like the level of TPUs shipping as there are GPUs? If people end up with an "AI accelerator" in their PC, it would be quite likely to have NVIDIA branding.
I've been wondering the same. With crypto we saw the adoption of ASICs pretty quickly, you would think we would see the same with AI.