However, I agree with the article that people will run big LLMs on their laptop N years down the line. Especially if hardware outgrows best-in-class LLM model requirements. If a phone could run a 512GB LLM model fast, you would want it.
The amount of compute in the world is doubling over 2 years because of the ongoing investment in AI (!!)
In some scenario where new investment stops flowing and some AI companies go bankrupt all that compute will be looking for a market.
Inference providers are already profitable so with cheaper hardware it will mean even cheaper AI systems.
> The amount of compute in the world is doubling over 2 years because of the ongoing investment in AI (!!)
All going into the hands of a small group of people that will soon need to pay the piper.
That said, VC backed tech companies almost universally pull the rug once the money stops coming in. And historically those didn't have the trillions of dollars in future obligations that the current compute hardware oligopoly has. I can't see any universe where they don't start charging more, especially now that they've begun to make computers unaffordable for normal people.
And even past the bottom dollar cost, AI provides so many fun, new, unique ways for them to rug pull users. Maybe they start forcing users to smaller/quantized models. Maybe they start giving even the paying users ads. Maybe they start inserting propaganda/ads directly into the training data to make it more subtle. Maybe they just switch out models randomly or based on instantaneous hardware demand, giving users something even more unstable than LLMs already are. Maybe they'll charge based on semantic context (I see you're asking for help with your 2015 Ford Focus. Please subscribe to our 'Mechanic+' plan for $5/month or $25 for 24 hours). Maybe they charge more for API access. Maybe they'll charge to not train on your interactions.
I'll pass, thanks.
> All going into the hands of a small group of people that will soon need to pay the piper.
It's not very small! On the inference side there are many competitive providers as well as the option of hiring GPU servers yourself.
> And historically those didn't have the trillions of dollars in future obligations that the current compute hardware oligopoly has. I can't see any universe where they don't start charging more, especially now that they've begun to make computers unaffordable for normal people.
I can't say how strongly I disagree with this - it's just not how competition works, or how the current market is structured.
Take gpt-oss-120B as an example. It's not frontier level quality but it's not far off and certainly gives a strong redline that open source models will never get less intelligent than.
There is a competitive market in hosting providers, and you can see the pricing here: https://artificialanalysis.ai/models/gpt-oss-120b/providers?...
In what world is there a way in which all the providers (who are want revenue!) raise prices above the premium price Cerebas is charging for their very high speed inference?
There's already Google, profitable serving at the low-end at around half the price of Cerebas (but then you have to deal with Google billing!)
The fact that Azure/Amazon are all pricing exactly the same as 8(!) other providers as well as the same price https://www.voltagepark.com/blog/how-to-deploy-gpt-oss-on-a-... gives for running your own server shows how the economics work on NVidia hardware. There's no subsidy going on there.
This is on hardware that is already deployed. That isn't suddenly going to get more expensive unless demand increases... in which case the new hardware coming online over the next 24 months is a good investment, not a bad one!
No one is leaving an H100 cluster not running because the power costs too much - this is why remnants markets like Vast.ai exist.
But the operational costs are much lower than some people in this thread seem to think.
You can find a safe margin for the price by looking at aggregators.
https://gpus.io/gpus/h100 is showing $1.83/hour lowest price, around $2.85 average.
That easily pays running costs - a H100 server with cooling etc is around $0.10/hour to keep running
And a massive overbuild pushes prices down not up!
That surprises me, do you remember where you learned that?
Here's a few good ones:
https://github.com/deepseek-ai/open-infra-index/blob/main/20... (suggests Deepseek is making 80% raw margin on inference)
https://www.snellman.net/blog/archive/2025-06-02-llms-are-ch...
https://martinalderson.com/posts/are-openai-and-anthropic-re... (there's a HN discussion of this where it was pointed out this overestimates the costs)
https://www.tensoreconomics.com/p/llm-inference-economics-fr... (long, but the TL;DR is that serving Lllama 3.3 70B costs around $0.28/million tokens input, $0.95 output at high utilization. These are close to what we see in the market: https://artificialanalysis.ai/models/llama-3-3-instruct-70b/... )
which is funded by the dumping
when the bubble pops: these DCs are turned off and left to rot, and your capacity drops by a factor of 8192
What dumping do you mean?
Are you implying NVidia is selling H200s below cost?
If not then you might be interested to see that Deepseek has released there inference costs here: https://github.com/deepseek-ai/open-infra-index/blob/main/20...
If they are losing money it's because they have a free app they are subsidizing, not because the API is underpriced.
The situation might be a lot different than people selling ex-crypto mining GPUs to gamers. There might be a lot of effective scrap that is no longer usable when it is no longer part of a some companies technological fever dream.
I don't see why consumer hardware won't evolve to run more LLMs locally. It is a nice goal to strive for, which consumer hardware makers have been missing for a decade now. It is definitely achievable, especially if you just care about inference.
Uber is economical, too; but folks prefer to own cars, sometimes multiple.
And how there's market for all kinds of vanity cars, fast sportscars, expensive supercars... I imagine PCs & Laptops will have such a market, too: In probably less than a decade, may be a £20k laptop running a 671b+ LLM locally will be the norm among pros.
One time I took an Uber to work because my car broke down and was in the shop and the Uber driver (somewhat pointedly) made a comment that I must be really rich to commute to work via Uber because Ubers are so expensive
An Uber car can provide several.
When LLM use approaches this number, running one locally would be, yes. What you and other commentator seem to miss is, "Uber" is a stand-in for Cloud-based LLMs: Someone else builds and owns those servers, runs the LLMs, pays the electricity bills... while its users find it "economical" to rent it.
(btw, taxis are considered economical in parts of the world where owning cars is a luxury)
so stop it