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Comment by rkangel | Hacker News Reader

It is absolutely not worth buying hardware to run models for purely (long term) cost reasons. For open weights models the economies of scale means the cloud beats local significantly and your payback time is like 10 years.

However there are other reasons (e.g. privacy) that might make it worth running locally for some people.

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I think the biggest reason is to own the stack so your model can't be changed out from under you, but maybe I care about that too much.

You do, there's like 20 providers for any model on openrouter. You can also just spin bedrock or gcp and download the weights for later if you're worried. It's never going to make cost sense when the token rate is so low with how expensive ram is

What if the internet goes away?

Starlink? It's never gone anymore

> I think the biggest reason is to own the stack so your model can't be changed out from under you,

The concern would be future regulations that prohibit you from buying a hosted version of the model. Even that could be bypassed with a VPN to another country but it's more work to go through the payments.

As long as there is demand for a model, it will be hosted by multiple providers.

What if the model is hopelessly obsolete, and thus no demand, but I want that specific model? Owning the weights and hardware is not just solving for one problem. It eliminates all the classes of problems that occur outside of your building, if you have a solar and battery setup.

Also, on a more practical basis, what if the way it's served is bad. Maybe I want my specific KV setup, or ultra low quant for entertaining garbage at 200 tk/s

Also, this makes me wonder if, by using a bicycle generator, and a local model at sufficiently low power consumption, you could directly claim to have produced the text in a really physical way. "Yes, I generated the electrons that made that text work by my own efforts".

> What if the model is hopelessly obsolete, and thus no demand, but I want that specific model?

You can still find a lot of old and completely outdated models on OpenRouter. The providers can scale serving of models up and down as demand arrives, so models don't generally disappear. They're just kept in the mix and the clouds will allocate hardware to it if someone is willing to pay.

In the odd case that it disappears completely, buying the hardware 2 years from now is probably going to be a better deal. That wasn't true if you selectively check the time period before hardware got expensive, but as new hardware comes out we're going to start seeing Strix Halo and old Apple hardware hit the market as people upgrade. It's already happening.

There is a certain personality type that cannot tolerate any uncertainty and must lock everything in right now against all future possibilities. If you fit that description then there's nothing anyone can say to discourage you from buying your own hardware, but for everyone else I do not recommend buying hardware to self-host LLMs just to save money. I self-host and run a lot of tokens through my setup (non-coding work) but I'm not really saving money.

> There is a certain personality type that cannot tolerate any uncertainty and must lock everything in right now against all future possibilities.

I thought HN banned personal attacks. I'm in this sentence and I don't like it. /s

I just buy the good apple hardware because it's good, and it also happens to run local models. It's not as good for the dollar, don't get me wrong, but I'm not going to develop iOS without a mac, that's even more questionable than buying a strix or whatever.

Welcome to production software, where you really want to pin all uncertainties and dependencies, and roll back in case a major problem occurs.

Nobody is treating production software like that today. It's always downloading half the internet on every build.

The only reason I'm considering picking one up is I think we're not that far away from compute limitations in consumer hardware.

I live in a place where using VPN is illegal and akin to "terrorism" because why would you want to hide what you are doing. Only bad guys hide. So if you use VPN, you are a bad guy.

https://srinagar.nic.in/notice/immediate-suspension-of-virtu...

Phones are randomly searched on the streets and if VPN is found, arrested

https://www.medianama.com/2026/01/223-jammu-kashmir-vpn-ban-...

https://timesofindia.indiatimes.com/india/after-vpn-ban-in-k...

“Out of the 15 individuals identified, five were minors who were counselled and advised in the presence of their guardians, with emphasis on awareness, lawful digital conduct, and the consequences of violating lawful orders,” he added.

I think that's overly pessimistic. Here's [1] a video of somebody running it on a ~$6000 rig and getting around 14T/s for complex prompts (about double that for simpler prompts). Payback time is going to depend on your electric cost/consumption. In most domains cloud providers end up charging a significant premium rather than a offering a scale enabled discount, relative to local at retail costs. That will almost certainly end up being the case with LLMs as well, if it isn't already.

Furthermore we continue to follow the path that image gen neural networks took. In that domain hardware requirements reached a peak and then started sharply declining to where we are today where a plain old video card can rapidly generate images that took a supercomputer not that long ago. So it's reasonable to assume that performance of such a system could potentially even increase over time.

[1] - https://www.youtube.com/watch?v=ZWS2JVN2iBI

With roughly 2.7 million seconds per month, times 14 tokens per second, you are getting 38.5 million tokens a month at most.

That’s less than 164USD worth of GLM5.3 tokens on the inference market. So that 6000 USD rig will take 3 years to break even - and only if it runs continuously. And this is being generous, as it’s not even taking quantisation into account.

I think if you steel-man what I'm saying, what you're saying falls apart. 14 tokens per second was rare. It only dropped that low in one scenario where he had it single shot an entire game (flappy bird clone) from scratch, with different assets, all self created, and so on. It ended up resulting in the LLM doing stuff like plotting out a some odd 100 item long to-do list, requerying it repeatedly, and so on. And it succeeded.

Also as the video mentions, the guy wasn't very familiar with what he was doing, and so there are almost certainly various optimizations on the config side he could work out, especially as he was using a 5 GPU system, which default configs are probably not well optimized for.

But I think we've rapidly moving along the same path as image gen stuff. Local generation has gone from purely theoretic, to requiring supercomputers to run relatively incapable models, to where we are today - where with a fairly basic high end setup, he's comfortably running a frontier level model. There's definitely an argument for going local that's only growing stronger by the day.

I agree it’s probably not representative token speed. But I do believe the overall observation holds: The monetary value of local inference is bound by the wall clock.

I agree that there are many other reasons than cost alone.

I think the “killer app” is doing inference without sending the data to China or the US. At home it’s overkill but imagine you are an EU consultancy with a lot of client data to work on, or a company/institution with a lot of sensitive data, buying the hardware to make sure the data stays private is a huge benefit. So is that you “own” the model. Its capabilities, price or access don’t change at someone else’s whim.

Some of that is that EU providers need to up their game here.

Needing an EU native option is really the one and only reasonably objection I've heard against using LLMs from the cloud, the rest is tin-foil hat level unless you're actually intending to meddle with the inference or fine tuning or something beyond just querying.

> That’s less than 164USD worth of GLM5.3 tokens on the inference market.

I can cherry pick stats too.

The other day I heard mention of someone paying $200/mo for Claude Code.

At those rates my local LM setup pays for itself in a single year.

Does it make sense running 1-bit models for agentic tasks?

I'm actively uninspired to write high quality code when using Anthropic/OpenAI models given the high chance I'm a customer as well as used as dataset generation tool for them.

But currently cloud does beat costs of hardware ownership, particularly with ridiculously high RAM/GPU/SSD costs....again due to these same companies.

> It is absolutely not worth buying hardware to run models for purely (long term) cost reasons

This is especially true when it's trivial to have the LLM itself write you a script/tool that can rent a GPU node for you (via API calls to providers) and then download and set up an open weight model for you.

I mean, I think it depends. At home 3 of us we use AI for multiple reasons, from coding apps to asking general questions, and if we would have to pay equivalent subscriptions that would be ~1k a year on AI + submitting all your data to external services. I payed around ~8k on 2 DGX Sparks that, at the moment, serves perfectly fine as a ChatGPT/Claude replacement at home (DS4 Flash peaking at ~170 tokens per sec with 6 concurrent sequences), and even once the technology is obsolete for inference in a few years, I will still have 2 pretty powerful machines for whatever I need + some pretty fast NVME Storage. I don't think its a terribly bad idea.

And compliance.

It seems absurdly naive to rely on "oh, the cloud AI of the future will definitely be as open and priced the same way it is right now."

And not "Hey, these companies have a history of giving you something nice now, and rugpulling you either in quality or price later."

Your "absolutely" seems silly.

I think the privacy argument that keeps coming up is overrepresented. Certainly ZDR is enough for an absolute majority of use cases? I see so much talk about local inference but I doubt most of it has privacy as a valid argument (not arguing it doesn't exist). It's fun to do things locally though. I've tried it as well but cloud is just faster and cheaper.

These companies have displayed zero respect for everyone's intellectual property getting these models trained.

I think not giving them your complete trust is reasonable! I'm not saying zero trust, and ZDR is fine for most things but I understand the people who don't want to stream their whole codebase out token by token.

Then use other providers hosting open models. Companies and individuals already put their whole code base on the cloud. I'm genuinely interested in privacy-oriented use cases where ZDR is not enough.

I'm not that worried about the codebase itself. I'm worried about the fact coding agents poke around the terminal and system so much that there is almost a certainty that some of your other personal data ends up in the context somewhere which is getting logged in to a training dataset by random hosting providers.

ZDR is built on trust. Given that end-to-end encryption fundamentally doesn't work with LLMs, as they need the content to be unencrypted to operate on it[1], you have no way to prove that once your plaintext data is on somebody else's server they aren't doing whatever the hell they please with it. All you have to rely on is their pinky promise that they won't do anything with it. Trust is a valid option, much of our society runs on trust, but you can eliminate the need for trust whatsoever by running on your own hardware.

[1] Yes, I'm aware of experiments to operate on encrypted prompts, but these are only research attempts, not something that could actually be used with frontier models in production.

Privacy isn’t only, I don’t want anyone to have access to my data. It could also be, I don’t want anyone to know my use case because it’s niche and highly profitable.

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