Jlagreen

And what about the extra energy consumption of the RTX 5070 TI vs. a iGPU? If you go GPU cloud then you can save on energy as well in your PC. Less energy means also less noise by the way.

To get an idea, if you go gaming via cloud then fast internet + office PC or Laptop is enough. So you save way more than the GPU only in a proper comparison.

This is why I play consoles only. I can play games for years without ever changing HW and save tons of money compared to my PC gaming times.

How many customers do you think AMD and AVGO have in AI?

Nvidia having 4 customers at 60% is good news because it was 90% 2 years ago.

There is one answer to this question which is called options. Selling short term OTM CCs with half of premium being used to buy ITM Calls (1/3 protection of the CC), OTM Calls (protection against gap up) and longterm OTM Puts (earn money when Nvidia nose dives).

All Tech companies including OpenAI have initiatives for their own chips. Nobody talks about Intel.

Tech companies have the following options:

1. Buy Nvdia high performance stack with stable and fast support and deploy your SW developers to quickly get started

2. Buy AMD whatever stack and deploy your SW developers to make a lot of ground work

3. Develop and deploy your own chips and use your SW developers to make your SW from scratch but exactly as you need it

The only reason they might go with No. 2 is if AMD gives them the HW more or less for free but maybe even then will mix 2 + 3 if they don't want 1. And AMD deal is showing exactly this, AMD has to give free quity to get a customer for their HW.

What people don't get, who in their right mind would switch from one vendor lock-in to another with the difference on investing SW development into the 2nd???? The resources spend on AMD will bind customers to AMD. It doesn't matter if RoCm is open source as long as it runs only on AMD. If RoCm would run on any AI chip (including Nvidia) then we would have a case of an interesting switch but then the question comes up, why buy AMD if RoCm doesn't require it?

On huge GPU clusters running inferencing the utilization of GPUs is key.

Imagine you have 1 million GPUs and you have 99% utilization of theoretical performance in the system with inferencing. That would mean 10k of GPUs are basically idle and draw power. You could now try to identify which ones are idle but you won't find them because utilization is a dynamic process so while all GPUs are under load not all are running 100% performance beause of interconnects and networking not providing data fast enough so your whole network becomes a bottleneck.

So what you need is a very smart routing process of computation requirements on the whole cluster. This is pure SW issue and not HW issue. This is the SW Nvidia has been working on for years and where AMD is years behing.

This is also why Jensen is absolutely right to say that competitors can offer their chips for free because Nvidia's key in TCO performance is the idea of one giant GPU so SW and networking allowing for highest utilization of a data center. You can't build a GPU the size of 1 million GPUs so you have to think of the utilization problem of a network of GPUs.

In the real world utilization rates are way below 100% so every % better of utilization is way more worth than the price of single GPUs. The idea here is that the company providing 2-3x higher utilization can easily ask for like 5x higher pricing per chip and will still deliver a better TCO.

AMD was desperate enough to sell 10% of their company to get 1 customer.

The issue here is now, that every large customer of AMD will now probably ask for equity. AMD has put itself into a pit hole with that deal.

If I were Hyperscaler CEO, I would basically ask for the a similiar deal as OpenAI or no business. Sorry Lisa Su but as a CEO giving equity to a customer is an absolute red flag because it starts a negative spirale you can't stop.

It seems that no matter the discount, OpenAI wasn't ready to make deal without equity. This tells you exactly how AMD is seen in the AI world.

OpenAI will take the compute for free and help AMD to rise stock value but it won't help AMD one bit because if AMD remains in the current position then OpenAI and Hyperscalers can get great deals with equity from AMD. The incentive isn't now to improve AMD to be competitive but to squeeze everything out of a company being desperate enough to give equity to customers.

And AMD will feel this. Nvidia will remain dominant because of ecosystem and supply. AMD can't easily replace Nvidia in supply chain and Nvidia is already strongly entrenched in many AI compute operations. And on the other side Hyperscalers are focused on their own chips (even OpenAI LOL) so they will tell AMD "Give us equity or no deal". This deal might be really the worst AMD deal yet because AMD is telling the world "here, you can get free AI compute from us financed by our equity". And while it might push AMD share price the very share price will drop 80-90% like any other one in case of an AI bubble pop.

My cost base of Nvidia is $1 so for me to lose is that Nvidia gets back to way before COVID valuation.

Nvidia is more worth than $1 per share on gaming alone.

And you have that right.

And now imagine what will happen when OpenAI makes deals with Nvidia and AMD. Do you think Hyperscalers will just watch?

I expect Musk to make a $1 trillion deal soon. I guess, that's why he wants to get the $1 trillion from Tesla.

And do you think Meta, Amazon, Microsoft and Google will stand by while Altman and Musk are buying future supply from Nvidia and AMD?

I love that. As an investor in Nvidia, I hope that these future promises will push the stock 4-5x quickly in Cisco fashion because then I can sell and retire in my 40s with a huge pile of money watching the bubble explosion on some beach on an island :)

So, it's basically trying to catch up again: https://www.nvidia.com/en-us/solutions/quantum-computing/

Or some interesting news here: https://nvidianews.nvidia.com/news/nvidia-powers-worlds-larg...

At least, they aren't waiting until CUDA quantum becomes as large as CUDA for GPUs.

Nvidia already has an ecosystem with 2 milliond developers in robotics. Nvidia's CUDA for robotics started 11 years ago.

And just like with CUDA no one else has currently the resources to go there because at the moment you have to invest heavily without any return.

The console market is low margin because they seem to find someone ready to take low margin (e.g. AMD). Nvidia was in console market before but left it due to low margin. Nvidia only sells old low development chip with probably good margin to Nintendo. The chips in the Switch 2 are using node from 2020 and are super cheap in manufacturing and Nvidia had low efforts in developing them.

AMD however has to design new special APUs for Xbox and PS. Why do they do that? They could just decide to step away from the tender but they won't because they seem to be desperate for any business. Jensen was like that 20 years ago but he has learned that some business you simply step away from.

and what is China's market for High Tech chips outside of China?

Will you buy an AI GPU coming from China on which you will train your sensitive data?

People, really create some facts without checking some simple basic things. Huwai was banned for modems in the Western world but sure we will buy their AI GPUs LOL.

And at the same time how can it be that Chinese companies want the far inferior H20 or soon B20 instead of anything coming from China itself??? Nvidia has warned us that they can't provide the demand for H20 chips due to supply but yes sure Chinese chips will kill Nvidia lol.

Nvidia would probably not fail because they work already very closely with TSMC and have teams sitting there directly.

Also Nvidia could even afford to burn 100s of billions of dollars since they are becoming the most profitable company in the world with probably passing Apple this year.

BUT then Nvidia would compete with TSMC and that is a problem because Nvidia is building up a cash and supply moat. Why should Nvidia build TSMC competition if they can simply buy 95% of the packaging supply at TSMC so competitors like AMD and others struggle to get supply? By booking everything at TSMC, Nvidia can easily keep their market share even if competitors improve their products.

This is a huge differences to many industries where most companies have their own production. Imagine there would be only 1 car manufacturer and all other car companies would only design. And now 1 car company would book 100% of supply from the manufacturer. What would the other car design companies do? They would get out of business even if they design better cars.

Nvidia has done this before and that was in the 90s. By speeding up design and releasing new products and more products 2-3x faster than any competitor. The result was that from 90 competitors in 1993, there was only 1 left in 2003 for Nvidia in gaming GPUs.

And Nvidia is doing that again by speeding up their roadmap cadence and as well as booking all supply. Nvidia is crashing competition not only with a great product but by removing their competitors' option to place their product in the market.

Nvidia had $130b revenue last year, this year (2025) it will breach $200b with >90% of it being AI related.

Estimates for next year are $300b. Sources are rumors about the supply of Blackwell and Rubin GPUs for 2025 and 2026 with estimated ASPs.

Nvidia is selling everything they produce so insiders at supply chains can easily tell how much revenue Nvidia will roughly make.

DC GPUs from Nvidia are sold at $30-40k per piece. You might want to rethink your calculations.

Nvidia is going to sell >5 million Blackwells this year and will do $200b in revenue with that alone.

Nvidia has a high net profit margin of >50%. If Nvidia would make $4 trillion in revenue then they would have >$2 trillion in net profit. Then the market cap would easily be 5-10x higher than today because otherwise Nvidia would be the cheapest stock in history of all time.

Market cap is also a very bad indicator as it doesn't really tell how much money was really invested into the stock. Market cap is just a product of shares * prices. For example, I bought Nvidia shares in 2016 for a certain amount. These shares are >100x more valuable today but I didn't put any extra money into them. So 99% of "my" market cap was simply created by traders pushing up the stock price.

If tomorrow, the majority of Nvidia stock holders decide to sell and all stocks are sold then I guarantee you that never ever will $4 trillion be traded because if there is a strong sell move then the stock price will drop like a rock and the last sellers will get a fraction of money as they have based on todays market cap. We might be lucky to see $500b of trading volume.

AWS for Amazon was a logical step because who is AWS largest customer? Amazon of course.

If you build large eCommerce like Amazon then of course you need huge IT infrastructure for it. Amazon was kind of forced to build the IT infrastructure and in that process Bezos saw another business opportunity because not every smaller company can easily build large IT infrastructure but many companies need it. And just as AWS built "SW services" for Amazon eCommerce it also became the foundation of the cloud business.

The worlds spends several trillions per year on public and private R&D. The AI frenzy could go on for a decade without making any money simple by R&D spend world wide.

That's what people don't get. We're still primarily in AI research mode. The race in LLM training isn't about making money, it's about a R&D race and whoever gets a better product faster than competition.

Therefore Nvidia won't fail, because we're far far away from any AI production mode since the computing for that would need decades to install. Imagine how much compute power you would need for 24/7 assistant inferencing in real time for every person on earth. We're are just scratching the surface. For Nvidia to fail at this time would be the same as that the world would stop on AI research lol.

Imagine, you have an industry where 80% of all companies' / private investors R&D money becomes revenue of a single company. That's basically Nvidia's position in a nutshell.

Basically, Nvidia could do $1 trillion revenue on world wide R&D budgets alone, no need for their customers to make money yet.

Except for ATI, Nvidia drove all of them out of business. There is a reason why Nvidia dominates all GPU markets.

That's because Nvidia is offering a full ecosystem stack with HW, SW and networking clusters.

And that gives customers the most flexibility. Nvidia dominates training and is highly competitive in inferencing. At the same time, SW improvements speed up single node and networking performance. H100 released 3 years ago is today several times faster than it was on release with constant SW updates.

Customers who buy Nvidia for training today can use the older GPUs from Nvidia for inferencing later. And Nvidia supports even V100 still in SW updates and speed improvements. And since all is based on the same SW ecosystem, it allows for more seamless operations for customers. You can mix different Nvidia GPU clusters but you can't easily mix Nvidia solutions with other solutions.

That is also why Nvidia has always been dominant and that's flexibility. NVFP4 is a good example of what they do to stay ahead. And it is even supported by Hopper so any old customer can use Nvidia's new format to further improve model training performance. Suddenly old Hopper clusters become more valuable with some SW releases by Nvidia.

Nvidia has a track record which no competitor can match. Going with Nvidia is no mistake today while going with any competitor is a risky bet. If you spend billions, you think twice about making bets.

Exactly, and Nvidia doesn't have to talk to the press, right? Or send them anything for free?

The issue is that the press wants to show how a 8GB new card is slower than a 12GB older card in putting both in higher resolution comparison.

The press doesn't care about thinking or reasoning about product placement and instead wants to always uncover some scandal.

Nvidia clearly has communicated if you want the card for free then these are the conditions. You say yes or no. Nvidia HASN'T forbidden GN and others to buy the card and test independently, right?

Jensen opens every large meeting with the sentence "we will be out of business in 30 days, if you don't get things right". This is Nvidia's mantra for 3 decades and remains so even today.

There is a reason why Nvidia dominates and that's because when you're paranoid and desperate is when you innovate the most.

Actually, Nvidia has already built the ecosystem. Now, they are refining and adapting it to the fast research in AI.

Others talk about chips when Nvidia thought about interconnects 8 years ago. Today, competitors try to catch up on this while Nvidia talks about One Giant GPU.

The next step will be scale up and then scale out.

Nvidia is always ahead because what the article fails to see is that where CSPs are today is where Nvidia was in the last decade. Nvidia has a working ecosystem for everyone which they can now fine tune with actual customers.

And yet, Nvidia innovates in gaming with SW 10x more than anyone. Strange side business it is which is also worth billions by the way.

Did you know that Nvidia has a gaming cloud running which might become the largest in the world at some time?

In 10-20 years, Nvidia might make more revenue from gaming cloud than they do today with gaming HW.

Because Nvidia focuses on being a partner in each industry you mention.

See it that way, if you have an OS/SW for all the industries you mention then who is your competitor? Not the participants in that industries. Nvidia can partner with any automotive company but won't compete with any of them as long as they don't build cars. But imagine the potential of every self driving car being build using Nvidia AI?

Think about the potential of every robot build using Nvidia AI?

Think about the potential of any AI Service using Nvidia AI?

See, Nvidia isn't directly competing in the enduser market but instead focuses on the B2B. Nvidia can also create many different revenue streams from 1 customer.

For example an automotive customer: - Nvidia HW in car for AI - Nvidia data center on-prem/cloud for DriveSim in car - Nvidia Omniverse for car design and manufacturing simulation - Nvidia Isaac for robotics/logistics in manufacturing plant - Nvidia Cosmos+GR00T for robots inside the plant - Nvidia edge devices inside any robot in the plant - Nvidia NeMo data center on-prem/cloud for AI models / LLMs for internal use

And what will be the advantage? Nvidia can actually make it more and more seamless to operate between all Nvidia solutions. For example, you can do an update to your robots in Cosmos, simulate it in Omniverse and with 1 click update your Nvidia driven real robots. The alternative is that you have 3 solutions from 3 different vendors with no interface between them.

People have no idea, what Nvidia is actually creating. Nvidia has more SW engineers and even Nvidia employees call Nvidia an AI SW company. They publish so many libs and lots of other SW stuff that it's sometimes hard to keep up. Just look at all the RTX goodies for gamers which Nvidia is developing. And they are all free, well except that you need Nvidia HW for it. The same model, Nvidia will apply to ALL industries in the world. And here they discuss about CSPs being an issue for Nvidia while Jensen focuses to build a Mega Corp. which potential TAM is in every industry in the world :)

One of the most important contributors to QC is Nvidia because they try to help with GPUs in creating SW for QC and simulations. Nvidia has cuda quantum for years now.

Yes, but the cloud customers "who finance TPUs" have NO INTEREST in TPUs and in Nvidia GPUs instead.

How does Google pay for TPUs internally? By Google Search and Google Cloud of course. Google Search uses TPUs, Google Cloud however has way more non-TPUs instances.

What people forget, nobody wants to switch from CUDA dependency to SW dependency on Google/AWS/Azure. CUDA at least allows me to use it in consumer, in pro HW, in cloud and AND in on-prem data center.

I'm really looking forward to Fortune 500 companies sending all their internal company data to Google to structure it to train custom AI models. Yeah, that will never happen. What happens instead is that Fortune 500 companies will build up AI expertise to build their own custom AI model and they will think hard if they want the training AI compute internally or on a cloud. Nvidia has a huge business of building data centers on-premises which people totally oversee. NO CSP will ever compete there because it's against their primary business model. A Reliance India contract from 2023 alone is a delivery of 2 million GPUs in a few years. That's probably more than Nvidia's last year's total revenue and that is 1 large corp in India only.

Yes, we can see how Big Tech and Apple's huge margins get compressed all the time, they never expand. They are going to zero soon it seems :(

You can't say that in general because it also depends on the moat.

Apple has 75% profitshare in the smartphone market not because they have the best smartphone but because they sell iOS. This is why Apple can charge much higher margins on iPhones then any other smartphone competitor. Competitors use Android and are basically exchangable so their HW is commodity more or less and margins are much lower.

The same will happen with Nvidia. Nvidia will offer complete data center solutions and many other SW/HW solutions for AI and accelerated computing and will charge high margins for that. HW sellers like AMD will sell only chips which will compete with commodity ASICs.

What are you talking about?

Nvidia has been a high margin and great performing business for the last decade. Nvidia had better gross margins than apple by selling gaming GPUs only 10 years ago and that's in a market where you can easily exchange the card in a PCIe slot.

From 2015 till 2022, Nvidia had several years with 50-60% revenue growth. People only look at the recent 2 years and think that Nvidia was "OK" before but I'm invested since 2016 because Nvidia started the growth turbo back in 2014/2015.

Jensen decided decades ago that Nvidia is premium and he positions the company in that position. What many don't get, Apple has only 25% unit share but 75% profit share. So Apple basically concentrates the profit of the Smartphone business. Nvidia will do the same. They had better gross margins a decade ago than AMD has today. AMD might gain unit share but will never gain Nvidia's margins because AMD is a market follower and will never be able to set pricing unlike Nvidia with premium solutions.

Jensen also made CUDA possible. Intel on the other hand killed such projects and also their first GPU project. Intel also didn't invest in OpenAI and so on. Jensen is by far the best CEO and fortunately he doesn't get crazy like Musk does.

The way I see it is that Nvidia might reacht $1 trillion in revenue before AMD reaches $100 billion in revenue. So the upside in revenue growth is higher for Nvidia.

People think that because a company has grown very large very quickly that it can't grow as much anymore. But on the other hand, there is clear evidence that Nvidia continues to dominate AMD's offerings despite the latter having a competitive product now. So the metric for Nvidia isn't Nvidia vs. AMD but the growth aspect of AI market overall.

Ah, I'm sorry you're right a misunderstood your comment.

I agree and Nvidia positions itself for exactly that. See how fast DeepSeek will come to NIM. People are already wondering how well DeepSeek will run on Digits.

Nvidia also offers distilled open models or specific own open models so is indirectly competing in that space as well. But Nvidia isn't in the LLM generator business but in the business of "infrastructure for LLM generators"

Everyone is waiting for GPT5 or another big bang. And because it takes so much people start to think that there is a wall. And there is a wall but that wall could be also compute. Blackwell will show if there is a compute wall because simply put, if a training run with large parameter set on GPT5 takes like 4 months then Blackwell might reduce that to under 1 month with the same amount of GPUs. Getting more GPUs can get that down even more. Imagine the speed up in AI frontier model research if your training times come down 4-5x from new GPU generation and another 2x from getting twice as many.

The nice part with Nvidia is also that the old GPUs don't become obsolete, OpenAI can continue using them for inferencing or even try to use combined architecture training as long as they don't go FP4.

I wouldn't be surprised that at the end of 2025 we will see things which will make DeepSeek and GPT4 as oldschool stuff simply because of the massive compute which Blackwell will deliver this year.