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.
Companies have wasted more money on dumber things so spending isn't a good measure.
And what about the countless other AI companies? Anthropic has one of the top models for coding so that's like saying there ins't a problem pre dot com bubble because Amazon is doing fine.
The real effects of AI is measured in rising profit of the customers of those AI companies otherwise you're looking at the shovel sellers
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.
And I'm still convinced we're not paying real prices anywhere. Everyone is still trying to get market share so the prices are going to go up when this all needs to sustain itself. At that point, which use cases become too expensive and does that shrink it's applicability ?
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.
I don’t think that’s true. I think both my mother and my mother-in-law would start to complain pretty quickly if they got pushed back to 4o. Change may have felt gradual, but I think that’s more a function of growing confidence in what they can expect the machine to do.
I also think “ask how long to boil an egg” is missing a lot here. Both use ChatGPT in place of Google for all sorts of shit these days, including plenty of stuff they shouldn’t (like: “will the city be doing garbage collection tomorrow?”). Both are pretty sharp women but neither is remotely technical.
I did. The old one is smarter.
(The newer ones are more verbose, though. If that impresses you, then you probably think members of parliament are geniuses.)
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.
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.
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.
It is, however, actually funny how bad e.g. the amazon chatbot (Rufus) is on amazon.com. When asked where a particular CC charge comes from, it does all sorts of SQL queries into my account, but it can't be bothered to give me the link to the actual charges (the page exists and solves the problem trivially).
So, maybe, the callcenter troubles will take some time to materialize.
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.
I wouldn’t be that dismissive. Some have managed to make impressive things with them (although nothing close to an actual movie, even a short).
https://www.youtube.com/watch?v=ET7Y1nNMXmA
A bit older: https://www.youtube.com/watch?v=8OOpYvxKhtY
Compared to two years ago: https://www.youtube.com/watch?v=LHeCTfQOQcs
The acquisitions do. Remember Groq?
Most M&As arent done by value investors.
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?
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.