I am of the opinion that Nvidia's hit the wall with their current architecture in the same way that Intel has historically with its various architectures - their current generation's power and cooling requirements are requiring the construction of entirely new datacenters with different architectures, which is going to blow out the economics on inference (GPU + datacenter + power plant + nuclear fusion research division + lobbying for datacenter land + water rights + ...).
The story with Intel around these times was usually that AMD or Cyrix or ARM or Apple or someone else would come around with a new architecture that was a clear generation jump past Intel's, and most importantly seemed to break the thermal and power ceilings of the Intel generation (at which point Intel typically fired their chip design group, hired everyone from AMD or whoever, and came out with Core or whatever). Nvidia effectively has no competition, or hasn't had any - nobody's actually broken the CUDA moat, so neither Intel nor AMD nor anyone else is really competing for the datacenter space, so they haven't faced any actual competitive pressure against things like power draws in the multi-kilowatt range for the Blackwells.
The reason this matters is that LLMs are incredibly nifty often useful tools that are not AGI and also seem to be hitting a scaling wall, and the only way to make the economics of, eg, a Blackwell-powered datacenter make sense is to assume that the entire economy is going to be running on it, as opposed to some useful tools and some improved interfaces. Otherwise, the investment numbers just don't make sense - the gap between what we see on the ground of how LLMs are used and the real but limited value add they can provide and the actual full cost of providing that service with a brand new single-purpose "AI datacenter" is just too great.
So this is a press release, but any time I see something that looks like an actual new hardware architecture for inference, and especially one that doesn't require building a new building or solving nuclear fusion, I'll take it as a good sign. I like LLMs, I've gotten a lot of value out of them, but nothing about the industry's finances add up right now.
> I am of the opinion that Nvidia's hit the wall with their current architecture
Based on what?
Their measured performance on things people care about keep going up, and their software stack keeps getting better and unlocking more performance on existing hardware
> but nothing about the industry's finances add up right now
Is that based just on the HN "it is lots of money so it can't possibly make sense" wisdom? Because the released numbers seem to indicate that inference providers and Anthropic are doing pretty well, and that OpenAI is really only losing money on inference because of the free ChatGPT usage.
Further, I'm sure most people heard the mention of an unnamed enterprise paying Anthropic $5000/month per developer on inference(!!) If a company if that cost insensitive is there any reason why Anthropic would bother to subsidize them?
> Their measured performance on things people care about keep going up, and their software stack keeps getting better and unlocking more performance on existing hardware
I'm more concerned about fully-loaded dollars per token - including datacenter and power costs - rather than "does the chip go faster." If Nvidia couldn't make the chip go faster, there wouldn't be any debate, the question right now is "what is the cost of those improvements." I don't have the answer to that number, but the numbers going around for the costs of new datacenters doesn't give me a lot of optimism.
> Is that based just on the HN "it is lots of money so it can't possibly make sense" wisdom?
As far as revenue, the released numbers from nearly anyone in this space are questionable - they're not public companies, we don't actually get to see inside the box. Torture the numbers right and they'll tell you anything you want to hear. What we _do_ get to see is, eg, Anthropic raising billions of dollars every ~3 months or so over the course of 2025. Maybe they're just that ambitious, but that's the kind of thing that makes me nervous.
> OpenAI has $1.15T in spend commitments over the next 10 years
Yes, but those aren't contracted commitments, and we know some of them are equity swaps. For example "Microsoft ($250B Azure commitment)" from the footnote is an unknown amount of actual cash.
And I think it's fair to point out the other information in your link "OpenAI projects a 48% gross profit margin in 2025, improving to 70% by 2029."
> Is that based just on the HN "it is lots of money so it can't possibly make sense" wisdom?
I mean the amount of money invested across just a handful of AI companies is currently staggering and their respective revenues are no where near where they need to be. That’s a valid reason to be skeptical. How many times have we seen speculative investment of this magnitude? It’s shifting entire municipal and state economies in the US.
OpenAI alone is currently projected to burn over $100 billion by what? 2028 or 2029? Forgot what I read the other day. Tens of billions a year. That is a hell of a gamble by investors.
The flip side is that these companies seem to be capacity constrained (although that is hard to confirm). If you assume the labs are capacity constrained, which seems plausible, then building more capacity could pay off by allowing labs to serve more customers and increase revenue per customer.
This means the bigger questions are whether you believe the labs are compute constrained, and whether you believe more capacity would allow them to drive actual revenue. I think there is a decent chance of this being true, and under this reality the investments make more sense. I can especially believe this as we see higher-cost products like Claude Code grow rapidly with much higher token usage per user.
This all hinges on demand materialising when capacity increases, and margins being good enough on that demand to get a good ROI. But that seems like an easier bet for investors to grapple with than trying to compare future investment in capacity with today's revenue, which doesn't capture the whole picture.
I am not someone who would ever be ever be considered an expert on factories/manufacturing of any kind, but my (insanely basic) understanding is that typically a “factory” making whatever widgets or doodads is outputting at a profit or has a clear path to profitability in order to pay off a loan/investment. They have debt, but they’re moving towards the black in a concrete, relatively predictable way - no one speculates on a factory anywhere near the degree they do with AI companies currently. If said factory’s output is maxed and they’re still not making money, then it’s a losing investment and they wouldn’t expand.
Basically, it strikes me as not really apples to apples.
Consensus seems to be that the labs are profitable on inference. They are only losing money on training and free users.
The competition requiring them to spend that money on training and free users does complicate things. But when you just look at it from an inference perspective, looking at these data centres like token factories makes sense. I would definitely pay more to get faster inference of Opus 4.5, for example.
This is also not wholly dissimilar to other industries where companies spend heavily on R&D while running profitable manufacturing. Pharma semiconductors, and hardware companies like Samsung or Apple all do this. The unusual part with AI labs is the ratio and the uncertainty, but that's a difference of degree, not kind.
Correct. Core came from Pentium M, which actually came from the Israeli team who took the Pentium 3 architecture, and coupled this with the best bits from the Pentium 4
> The reason this matters is that LLMs are incredibly nifty often useful tools that are not AGI and also seem to be hitting a scaling wall
I don't know who needs to hear this, but the real break through in AI that we have had is not LLMs, but generative AI. LLM is but one specific case. Furthermore, we have hit absolutely no walls. Go download a model from Jan 2024, another from Jan 2025 and one from this year and compare. The difference is exponential in how well they have gotten.
There is a lot of talking past each other when discussing LLM performance. The average person whose typical use case is asking ChatGPT how long they need to boil an egg for hasn't seen improvements for 18 months. Meanwhile if you're super into something like local models for example the tangible improvements are without exaggeration happening almost monthly.
GP was talking about commercially hosted LLMs running in datacenters, not free Chinese models.
Local is definitely still improving. That’s another reason the megacenter model (NVDA’s big line up forever plan) is either a financial catastrophe about to happen, or the biggest bailout ever.
5.2 is great if you ask it engineering questions, or questions an engineer might ask. It is extremely mid, and actually worse than the o3/o4 era models if you start asking it trivia like if the I-80 tunnel on the bay bridge (yerba buena island) is the largest bore in the world. Don't even get me started on whatever model is wired up to the voice chat button.
But yes it will write you a flawless, physics accurate flight simulator in rust on the first try. I've proven that. I guess what I'm trying to say is Anthropic was eating their lunch at coding, and OpenAI rose to the challenge, but if you're not doing engineering tasks their current models are arguably worse than older ones.
In 2001, there were something like 50+ OC-768 hardware startups.
At the time, something like 5 OC-768 links could carry all the traffic in the world. Even exponential doubling every 12 months wasn't going to get enough customers to warrant all the funding that had poured into those startups.
When your business model bumps into "All the <X> in the world," you're in trouble.
Remember that without real competition, Nvidia has little incentive to release something 16x faster when they could release something 2x faster 4 times.
You’re right but Nvidia enjoys an important advantage Intel had always used to mask their sloppy design work: the supply chain. You simply can’t source HBMs at scale because Nvidia bought everything, TSMC N3 is likewise fully booked and between Apple and Nvidia their 18A is probably already far gone and if you want to connect your artisanal inference hardware together then congratulations, Nvidia is the leader here too and you WILL buy their switches.
As for the business side, I’ve yet to hear of a transformative business outcome due to LLMs (it will come, but not there yet). It’s only the guys selling the shovels that are making money.
This entire market runs on sovereign funds and cyclical investing. It’s crazy.
If Intel's original 10nm process and Cannon Lake had launched within Intel's original timeframe of 2016/17, it would have been class leading.
Instead, they couldn't get 10nm to work and launched one low-power SKU in 2018 that had almost half the die disabled, and stuck to 14nm from 2014-2021.
They were valued at $6.9B just three months before Nvidia bought them for $20B, triple the valuation. That figure seems to have been pulled out of thin air.
Speaking generally: It makes sense for a acquisition price to be at a premium to valuation, between the dynamics where you have to convince leadership its better to be bought than to keep growing, and the expected risk posed by them as competition.
Based on conversations I've had with some people managing GPU's at scale in the datacenters, inference is an after thought. There is a gold rush for training right now, and that's where these massive clusters are being used.
LLM's are probably a small fraction of the overall GPU compute in use right now. I suspect in the next 5 years we'll have full Hollywood movies being completely generated (at least the specialfx) entirely by AI.
Hollywood studios are breathing their last gasps now. Anyone will be able to use AI to create blockbuster type movies, Hollywood's moat around that is rapidly draining.
> nothing about the industry's finances add up right now
Nothing about the industry’s finances, or about Anthropic and OpenAI’s finances?
I look at the list of providers on OpenRouter for open models, and I don’t believe all of them are losing money. FWIW Anthropic claims (iirc) that they don’t lose money on inference. So I don’t think the industry or the model of selling inference is what’s in trouble there.
I am much more skeptical of Anthropic and OpenAI’s business model of spending gigantic sums on generating proprietary models. Latest Claude and GPT are very very good, but not better enough than the competition to justify the cash spend. It feels unlikely that anyone is gonna “winner takes all” the market at this point. I don’t see how Anthropic or OpenAI’s business model survive as independent entities, or how current owners don’t take a gigantic haircut, other than by Sam Altman managing to do something insane like reverse acquiring Oracle.
EDIT: also feels like Musk has shown how shallow the moat is. With enough cash and access to exceptional engineers, you can magic a frontier model out of the ether, however much of a douche you are.
What can it actually run? The fact their benchmark plot refers to Llama 3.1 8b signals to me that it's hand implemented for that model and likely can't run newer / larger models. Why else would you benchmark such an outdated model? Show me a benchmark for gpt-oss-120b or something similar to that.
Many are aware, just can’t offload it onto their hardware.
The 8B models are easier to run on an RTX to compare it to local inference. What llama does on an RTX 5080 at 40t/s, Furiosa should do at 40,000t/s or whatever… it’s an easy way to have a flat comparison across all the different hardware llama.cpp runs on.
I think you are comparing latency with throughput. You can't take the inverse of latency to get throughput because concurrency is unknown. But then, RNGD result is probably with concurrency=1.
I thought they were saying it was more efficient, as in tokens per watt. I didn’t see a direct comparison on that metric but maybe I didn’t look well enough.
It still kind of makes the point that you are stuck with a very limited range of models that they are hand implementing. But at least it's a model I would actually use. Give me that in a box I can put in a standard data center with normal power supply and I'm definitely interested.
The fact that so many people are focusing solely on massive LLM models is an oversight by people that narrowly focusing on a tiny (but very lucrative) subdomain of AI applications.
Got excited, then I saw it was for inference. yawns
Seems like it would obviously be in TSMCs interest to give preferential taping to nvidia competitors, they benefit from having a less consolidated customer base bidding up their prices.
really weird graph where they're comparing to 3x H100 PCI-E which is a config I don't think anyone is using.
they're trying to compare at iso-power? I just want to see their box vs a box of 8 h100s b/c that's what people would buy instead, and they can divide tokens and watts if that's the pitch.
Yeah they are defining a "rack" as 15kW, though 3x H100 PCIe is only a bit over 1kW. So they are assuming GPUs are <10% of rack power usage which sounds suspiciously low.
The positioning makes sense, but I’m still somewhat skeptical.
Targeting power, cooling, and TCO limits for inference is real, especially in air-cooled data centers.
But the benchmarks shown are narrow, and it’s unclear how well this generalizes across models and mixed production workloads. GPUs are inefficient here, but their flexibility still matters.
Is this from 2024? It mentions "With global data center demand at 60 GW in 2024"
Also, there is no mention of the latest-gen NVDA chips: 5 RNGD servers generate tokens at 3.5x the rate of a single H100 SXM at 15 kW. This is reduced to 1.5x if you instead use 3 H100 PCIe servers as the benchmark.
How usable is this in practice for the average non AI organization? Are you locked into a niche ecosystem that limits the options of what models you can serve?
Yes, but in principle it isn't that different from running on Trainium or Inferentia (it's a matter of degree), and plenty of non-AI organizations adopted Trainium/Inferentia.
The story with Intel around these times was usually that AMD or Cyrix or ARM or Apple or someone else would come around with a new architecture that was a clear generation jump past Intel's, and most importantly seemed to break the thermal and power ceilings of the Intel generation (at which point Intel typically fired their chip design group, hired everyone from AMD or whoever, and came out with Core or whatever). Nvidia effectively has no competition, or hasn't had any - nobody's actually broken the CUDA moat, so neither Intel nor AMD nor anyone else is really competing for the datacenter space, so they haven't faced any actual competitive pressure against things like power draws in the multi-kilowatt range for the Blackwells.
The reason this matters is that LLMs are incredibly nifty often useful tools that are not AGI and also seem to be hitting a scaling wall, and the only way to make the economics of, eg, a Blackwell-powered datacenter make sense is to assume that the entire economy is going to be running on it, as opposed to some useful tools and some improved interfaces. Otherwise, the investment numbers just don't make sense - the gap between what we see on the ground of how LLMs are used and the real but limited value add they can provide and the actual full cost of providing that service with a brand new single-purpose "AI datacenter" is just too great.
So this is a press release, but any time I see something that looks like an actual new hardware architecture for inference, and especially one that doesn't require building a new building or solving nuclear fusion, I'll take it as a good sign. I like LLMs, I've gotten a lot of value out of them, but nothing about the industry's finances add up right now.
Based on what?
Their measured performance on things people care about keep going up, and their software stack keeps getting better and unlocking more performance on existing hardware
Inference tests: https://inferencemax.semianalysis.com/
Training tests: https://www.lightly.ai/blog/nvidia-b200-vs-h100
https://newsletter.semianalysis.com/p/mi300x-vs-h100-vs-h200... (only H100, but vs AMD)
> but nothing about the industry's finances add up right now
Is that based just on the HN "it is lots of money so it can't possibly make sense" wisdom? Because the released numbers seem to indicate that inference providers and Anthropic are doing pretty well, and that OpenAI is really only losing money on inference because of the free ChatGPT usage.
Further, I'm sure most people heard the mention of an unnamed enterprise paying Anthropic $5000/month per developer on inference(!!) If a company if that cost insensitive is there any reason why Anthropic would bother to subsidize them?
I'm more concerned about fully-loaded dollars per token - including datacenter and power costs - rather than "does the chip go faster." If Nvidia couldn't make the chip go faster, there wouldn't be any debate, the question right now is "what is the cost of those improvements." I don't have the answer to that number, but the numbers going around for the costs of new datacenters doesn't give me a lot of optimism.
> Is that based just on the HN "it is lots of money so it can't possibly make sense" wisdom?
OpenAI has $1.15T in spend commitments over the next 10 years: https://tomtunguz.com/openai-hardware-spending-2025-2035/
As far as revenue, the released numbers from nearly anyone in this space are questionable - they're not public companies, we don't actually get to see inside the box. Torture the numbers right and they'll tell you anything you want to hear. What we _do_ get to see is, eg, Anthropic raising billions of dollars every ~3 months or so over the course of 2025. Maybe they're just that ambitious, but that's the kind of thing that makes me nervous.
Yes, but those aren't contracted commitments, and we know some of them are equity swaps. For example "Microsoft ($250B Azure commitment)" from the footnote is an unknown amount of actual cash.
And I think it's fair to point out the other information in your link "OpenAI projects a 48% gross profit margin in 2025, improving to 70% by 2029."
OpenAI can project whatever they want, they're not public.
I mean the amount of money invested across just a handful of AI companies is currently staggering and their respective revenues are no where near where they need to be. That’s a valid reason to be skeptical. How many times have we seen speculative investment of this magnitude? It’s shifting entire municipal and state economies in the US.
OpenAI alone is currently projected to burn over $100 billion by what? 2028 or 2029? Forgot what I read the other day. Tens of billions a year. That is a hell of a gamble by investors.
This means the bigger questions are whether you believe the labs are compute constrained, and whether you believe more capacity would allow them to drive actual revenue. I think there is a decent chance of this being true, and under this reality the investments make more sense. I can especially believe this as we see higher-cost products like Claude Code grow rapidly with much higher token usage per user.
This all hinges on demand materialising when capacity increases, and margins being good enough on that demand to get a good ROI. But that seems like an easier bet for investors to grapple with than trying to compare future investment in capacity with today's revenue, which doesn't capture the whole picture.
Basically, it strikes me as not really apples to apples.
The competition requiring them to spend that money on training and free users does complicate things. But when you just look at it from an inference perspective, looking at these data centres like token factories makes sense. I would definitely pay more to get faster inference of Opus 4.5, for example.
This is also not wholly dissimilar to other industries where companies spend heavily on R&D while running profitable manufacturing. Pharma semiconductors, and hardware companies like Samsung or Apple all do this. The unusual part with AI labs is the ratio and the uncertainty, but that's a difference of degree, not kind.
Didn't the Core architecture come from the Intel Pentium M Israeli team? https://en.wikipedia.org/wiki/Intel_Core_(microarchitecture)...
I don't know who needs to hear this, but the real break through in AI that we have had is not LLMs, but generative AI. LLM is but one specific case. Furthermore, we have hit absolutely no walls. Go download a model from Jan 2024, another from Jan 2025 and one from this year and compare. The difference is exponential in how well they have gotten.
Is this the second most abused english word (after 'literally')?
> a model from Jan 2024, another from Jan 2025 and one from this year
You literally can't tell the difference is 'exponential', quadratic, or whatever from three data points.
Plus it's not my experience at all. Since Deepseek I haven't found models that one can run on consumer hardware get much better.
GP was talking about commercially hosted LLMs running in datacenters, not free Chinese models.
Local is definitely still improving. That’s another reason the megacenter model (NVDA’s big line up forever plan) is either a financial catastrophe about to happen, or the biggest bailout ever.
But yes it will write you a flawless, physics accurate flight simulator in rust on the first try. I've proven that. I guess what I'm trying to say is Anthropic was eating their lunch at coding, and OpenAI rose to the challenge, but if you're not doing engineering tasks their current models are arguably worse than older ones.
In 2001, there were something like 50+ OC-768 hardware startups.
At the time, something like 5 OC-768 links could carry all the traffic in the world. Even exponential doubling every 12 months wasn't going to get enough customers to warrant all the funding that had poured into those startups.
When your business model bumps into "All the <X> in the world," you're in trouble.
As for the business side, I’ve yet to hear of a transformative business outcome due to LLMs (it will come, but not there yet). It’s only the guys selling the shovels that are making money.
This entire market runs on sovereign funds and cyclical investing. It’s crazy.
Not likely since TSMC has a new process with big gains.
> The story with Intel
Was that their fab couldn’t keep up not designs.
Instead, they couldn't get 10nm to work and launched one low-power SKU in 2018 that had almost half the die disabled, and stuck to 14nm from 2014-2021.
The acquisitions do. Remember Groq?
Most M&As arent done by value investors.
LLM's are probably a small fraction of the overall GPU compute in use right now. I suspect in the next 5 years we'll have full Hollywood movies being completely generated (at least the specialfx) entirely by AI.
Nothing about the industry’s finances, or about Anthropic and OpenAI’s finances?
I look at the list of providers on OpenRouter for open models, and I don’t believe all of them are losing money. FWIW Anthropic claims (iirc) that they don’t lose money on inference. So I don’t think the industry or the model of selling inference is what’s in trouble there.
I am much more skeptical of Anthropic and OpenAI’s business model of spending gigantic sums on generating proprietary models. Latest Claude and GPT are very very good, but not better enough than the competition to justify the cash spend. It feels unlikely that anyone is gonna “winner takes all” the market at this point. I don’t see how Anthropic or OpenAI’s business model survive as independent entities, or how current owners don’t take a gigantic haircut, other than by Sam Altman managing to do something insane like reverse acquiring Oracle.
EDIT: also feels like Musk has shown how shallow the moat is. With enough cash and access to exceptional engineers, you can magic a frontier model out of the ether, however much of a douche you are.
I think Llama 3 focus mostly reflects demand. It may be hard to believe, but many people aren't even aware gpt-oss exists.
The 8B models are easier to run on an RTX to compare it to local inference. What llama does on an RTX 5080 at 40t/s, Furiosa should do at 40,000t/s or whatever… it’s an easy way to have a flat comparison across all the different hardware llama.cpp runs on.
That's 86 token/second/chip
By comparison, a H100 will do 2390 token/second/GPU
Am I comparing the wrong things somehow?
[1] https://inferencemax.semianalysis.com/
It still kind of makes the point that you are stuck with a very limited range of models that they are hand implementing. But at least it's a model I would actually use. Give me that in a box I can put in a standard data center with normal power supply and I'm definitely interested.
But I want to know the cost :-)
Seems like it would obviously be in TSMCs interest to give preferential taping to nvidia competitors, they benefit from having a less consolidated customer base bidding up their prices.
they're trying to compare at iso-power? I just want to see their box vs a box of 8 h100s b/c that's what people would buy instead, and they can divide tokens and watts if that's the pitch.
Yeah they are defining a "rack" as 15kW, though 3x H100 PCIe is only a bit over 1kW. So they are assuming GPUs are <10% of rack power usage which sounds suspiciously low.
Targeting power, cooling, and TCO limits for inference is real, especially in air-cooled data centers.
But the benchmarks shown are narrow, and it’s unclear how well this generalizes across models and mixed production workloads. GPUs are inefficient here, but their flexibility still matters.
Also, there is no mention of the latest-gen NVDA chips: 5 RNGD servers generate tokens at 3.5x the rate of a single H100 SXM at 15 kW. This is reduced to 1.5x if you instead use 3 H100 PCIe servers as the benchmark.
Maybe they are cheap.
People forget this is also a place of discussion and the comment section is usually peak value as opposed to the article itself.