Obviously there are other reasons to buy your own hardware aside from just saving money on llms but this is just looking at it from a raw cost saving perspective.
If you have any ideas on how I can make this more helpful lmk!
Obviously there are other reasons to buy your own hardware aside from just saving money on llms but this is just looking at it from a raw cost saving perspective.
If you have any ideas on how I can make this more helpful lmk!
Mac mini can also build iOS applications. I think if you’re a mobile dev, you can have concurrent builds for your agents instead of everyone waiting on a single machine to finish.
Idk about the quality of this setup but just pasting it here as an example. https://explainx.ai/blog/heretic-llm-abliteration-guide-2026
When does the average person actually need to do that?
I expect this is only going to get worse. "Censorship" isn't just going to be about who you vote for and which political party the model will say nice things about and which it is more likely to say bad things about. It's going to become about whether the hoi polloi are allowed to have effective AIs at all. Like the 1990s internet, AI has outrun a lot of power structures but that is not going to continue indefinitely.
I paid $1350 and threw an R9700 in an existing machine. That's a 4 month pay off or so.
Plus, I can feed it sensitive data all day and not be worried where it's going.
The premium is not having your million dollar prize and career stolen by billionaires.
I sell those to business who need local air gapped requirments and I make a lot more money!
I can run the alliterated models where none of the service prvoider even dare to provide.
THose benefits outweights a few K.
And show me an api provider that allows me to run 10x agents concurrently for 5 days straights .
Any of them on a Max/Pro plan as long as you are smart about model selection? That's my main objection to local inference, I'd need a whole rack of GPUs to do as many things in parallel that I can do for $400 a month. I do plan on setting up some local inference hardware, but...RAM and GPU prices alone are $$$$
The big benefit of constant generation is you can do a lot more research, coding sub-agents, experiments, etc in parallel when you're not "at work". You end up getting a lot more work done than if you only sit there babysitting sessions.
Having said that, it will never pay for itself. A simpler more absolute math is, if I buy a Mac and use it to sell tokens on OpenRouter, will I make a profit? And the answer is no.
In May of this year, I was running qwen3.6:35b-a3b on my MacBook (bought in 2024). Obviously not as fast as, say, running a model on Cerebras, but a year ago it wasn't really feasible to have a local model running on my 2024 laptop with vision support. (Concretely, I was passing apartment diagram pictures to Qwen and making it compare different apartments for which ones would feel the most spacious while optimizing for initial moving costs and other factors.)
This was back in May and I wouldn't be surprised if there have been significant improvements since then.
Overall, I think it's fair to compare a workflow like "use llama.cpp locally to upload some pictures and ask questions" to "open the ChatGPT app, upload pictures from your phone, and ask questions". Sure, you can't run a model like GPT-5.4 locally, but the model is mostly an implementation detail here. What a user will care about is: "when I go with the llama.cpp option, am I getting useful information from my conversations?"
The thing with "sending tokens to someone else's computer" is that we, as users, have no way to audit or verify that a third-party provider, even if they advertise themselves as privacy-focused, will adhere to their own terms. This is similar to the issue of privacy-focused VPN providers that claim to not log user activity (and then end up leaking user activity in data breaches). You can get proof of ~P, but rarely proof of P, and often times the proof of ~P is due to police raids, data breaches, etc., not something of the provider's volition.
What you can possibly audit, is probably data sovereignty. For instance, I would not be surprised if Mistral's customers actually demand concrete evidence that their data is held within the European Union. But that is a distinct issue from training on input tokens.
You are missing out a lot.
Try that with Hermes or Opencode or Deekseek Harness , even Qwen 3.8 27b works really well for that kind of that.
I just ask it to install windows as a vm on my linux and install vs Community 2019 on it , and then build a legacy vb 2019 project on it. and sleep
When i wake up :
It installs Qemu , setup a vm , inside vm download and install windows 10 on its own , clicking next next next as needed , typing in things , writing powershell , python scripts , that run automatically after install by baking into CD that includes ssh server , reboot , it logins into ssh , trigger pythons script that continue installation of vs 2019 community , which includes a driver that click the installation steps , installs nuget , install all depedencies and then build the project into exe after i woke up.
That is with 100% pure local AI .
What part of my brain is contained here? Sure, the conversations have back and forth (some have dozens of exchanges), but, like, that's not the secret to me. I don't think it can replicate me, and even if it could… okay?
Are you worried they're going to target ads? That the government will steal something? What?
Claude Code has information about my home server, but google or DDG would also have the broad strokes (torrents). I don't know. Maybe others are working on more sensitive things at home.
The proof to the Navier-Stokes problem.
Its the same point used against privacy. What's so secret you are doing that you need privacy. I think in the end, its about privacy and not trusting these model companies with your data. Facebook manipulated people behaviors with all the data they had, no reason AI companies wont someday decide to do that same, and they have far more intimate knowledge.
When it comes to coding, I also don't like the idea of them taking my money and potentially at same time potentially using as dataset generator.
I also needed a new device anyway - and having this much system memory to run virtual machines has been amazing.
Am paying subscriptions as well tho lol.
Local isn’t strictly about NOT lab. It’s rapidly becoming apples (though not just macs) to oranges to compare the to.
Which is why the premise is silly. To be underwater it would need to be a real comparison. It’s not, and the claude fartifact doesn’t make it so.
So I can certainly understand why someone would want the guardrails gone.
Case in point, last week I was poking Opus 5 into writing me some RPi-pico firmware for driving a small e-paper screen. Font was built in right into C code as hex constants. Space being tight, I asked if there is some clever compression that could be applied. Claude thought for good 10 minutes, then guardrail kicked in telling me that was "cyber", and refused to continue.
hyperhello•46m ago
gruez•37m ago
-- Warren Buffett
taraindara•24m ago
rlindsey123•24m ago
tyre•18m ago
I don't know when we'll have an open equivalent to Fable, let alone whatever (insane) hardware you'd need to run it locally.
cle•7m ago
They're willing to sell it at a loss b/c users are generating training data for them which they hope to use to commoditize their intelligence, skills, and expertise.
epistasis•7m ago
Local inference makes sense for speciality needs, or very small models. But if your model is bug enough to span GPUs its excessively wasteful to hoard those GPUs for yourself without piggybacking hundreds of other conversations on top of all that memory bandwidth and matrix multiplies.