Show HN: Sunk Cost – How long until a local LLM rig pays for itself? (sunkcost.ai)
I kept hearing "just buy a Mac and run models locally, it pays for itself" and wanted to check. Sunk Cost takes a machine, a model and how many tokens you use a day, and works out how long the hardware takes to pay back against renting the same model by the token.
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!
78 comments
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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.
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.
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.
If you don't consume many of tokens, it will likely never pay for itself. If you do, though, it will have trade-offs, but you'll probably save money in the end.
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.
AFAIK, a limiter for iOS engineers (and AI agents) for concurrent feature development is the xcode environment and hardware limits. BE engineers can easily have 3 agents working on 3 different microservices (or gitwork trees), but iOS devs can basically only manage one version of the code at a time, due to externalized state (like derived data and bundle ids).
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.
And Google's original goal was to organize the world's information.
How did that turn out?
When Claude is used in a professional setting, any or all of:
Pretty much anything on a machine which uses Anthropic/OpenAI native tools is a candidate to be compromised really.0 - https://en.wikipedia.org/wiki/Personal_data
1 - https://en.wikipedia.org/wiki/Health_Insurance_Portability_a...
I understand people’s hesitation but the business agreements are different. The business risks of misusing HIPAA data is not only being banned from a massive enterprise market (last I checked, there were about 1.2m jobs related to claims billing and adjudication) and significant legal repercussions.
Other companies like AWS also handle HIPAA data. Are we afraid they’re stealing it? I don’t believe it, nor that Anthropic is training on HIPAA data.
OpenAI… I will never trust them.
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.
Some of us get a weird thrill that we can actually do this. Mind-boggling time we live in.
Some how, _instability_ has gained valuable currency, so now we all act like the constant change of whatever is actually good for us. FOMO is just like breathing guys. That anxiety induced by tech culture constantly churning is healthy.
In reality, these people churn for their own self worth and nothing else.
"Offloading thought" sounds a lot better than "outsourcing thought", but the latter is what we're really doing. Offloading implies you thought it first and then gave it to the LLM, but we're only giving it the minimun so it can do most of the work in our place,
Qwen3.8-flash-next Deepseek4-0731-flash Glm5.3
The latest unsloth llama.cpp has a lot of nice features that runs them faster than before.
I’ll have to double check which one runs how fast, but it’s generally 20-40 t/s. (And infil is fast but not sure how that’s counted)
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 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.
>> Is your comparison against a similar size model? Or shouldn't you be comparing it against the cost of a hosted model matching the one you’re using locally?
> You should be comparing the value you get
But the GP commenter specifically compared the cost of solutions such as Claude Code against a 32 GB model.
If they are going to compare cost, they should compare to the cost of a hosted ~32 GB model.
Or if privacy trumps everything for them, then just say that and don't bother comparing costs of incredibly disparate solutions, as Claude Code costing $100+ a month was a red herring if they're happy with 32 GB model output - they could have compared to a far cheaper option that matched their local model's quality.
I still have to use GHCP at work, and I self-host at home, and aside from the fact self-hosting also forces you to tinker, optimize, etc. - there's not a huge difference in my end result in end user results. I spent quite a bit of time trying to optimize llamacpp and compare 35b to 27b, etc. I don't compare models that much at work.
I guess the other part of it is I didn't really know much about cheaper cloud models, but I was attracted to the idea of no longer renting against Claude code, etc. I figured if I could run something functionaly similar from my bedroom on a normal outlet, then all this talk about data centers needing to be built everywhere in the news cycle is obviously just plain stupidity and hype.
It appears I'm using about 20.4/7.6 million in/out tokens a month, or on open router, about $20/month.
That puts $1350 at a 5-6 year break even (thanks to cheap electricity), I guess. Beyond that, running on localhost as a nice feature of 0 no latency when doing rapid tool calling
I don't think I'm the only persona that did the math.
For each usage level, it lists the quickest pay-back in each capability class, with each model on its quickest machine and one click into the calculator to change the assumptions. Short version: at 1M tokens/day the best Sonnet-class option is Qwen3.8 27B on a Mac mini M6, 8.3 years. It only drops under a year if you're running agents at around 20M tokens/day.
At 7 tokens/s (Mac mini) you max at 600k/day so you couldn’t hit those higher amounts like 4M where it says 2 year payback?
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 .
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.