HN Explorer

Show HN: Sunk Cost – How long until a local LLM rig pays for itself? (sunkcost.ai)

1 points by rlindsey123 ↗ HN
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!

60 comments

[ 1.5 ms ] story [ 27.8 ms ] thread
hyperhello
I doubt it will ever be for the foreseeable future. The AI companies have astonishing amounts of compute and they’re effectively dumping it on the market.
gruez
"If they are selling it for less than it cost to make, buy as much as you can."

-- Warren Buffett

taraindara
Only caveat is you’re buying time. Not a physical good. It’s only worth what you’re able to get out of it in that time.
tyre
For their current models, served directly from their infrastructure, they are profitable after training (which all present models are.)

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
It's not a simple transaction like that.

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
More than that, running hundreds of conversation streams at once is essentially the same cost as running a single conversation. And then you add on the secondary benefit of having the GPUs running nearly all the time rather than mostly idle...

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.

jrflo
43 years to break even on Qwen 3.8 at 25% the speed of the API, lol. I like the idea of local models for really small tasks like automation/toolcalling, but it will probably never make sense for coding. I tried them and it was just excruciating compared to what you get for $100 a month from a subscription.
rlindsey123
Yeah it's surprising how long it would take to get back on those local models!
itake
I have a home server running vibed applications. VPS host would cost $25/mo or $300/yr.

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.

rlindsey123
What models are you running on it? I'm also an iOS dev but I find I need more frontier models to get good quality code from it.
itake
I only use frontier models to vibe code iOS apps, as I'm not an iOS developer. I haven't tried the local models post qwen coder 3.5 release for all the reasons.

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).

ProjectArcturis
Local LLMs are not really about saving money, they're about autonomy. Choose the exact model you want, fine-tune it if you want, and no one can take it away from you.
mcone
The idea that you need a new machine is pretty ridiculous. I bought a used HP Omen with a 3090 last month for $2k. 57t/s with Qwen 3.8.
rlindsey123
I've not heard of others running HP with it. Hows much RAM do you have?
usernomdeguerre
Agreed, I was also annoyed that the only params on the site were mac products. I run qwen 3.8 on a 12 year old asus and a 3090, 50tok/s. It's not even the only guest running on the box. For my usage profile (not running it 24/7) it's actually less expensive per-month than claude subscriptions.
redox99
I'm so happy for the two used 3090s I bought for $500 each after Ethereum mining ended. I even saw them for like $430 at some point lol.
txrx0000
It pays off instantly, because OpenAI/Anthropic can no longer see what I'm doing and that's worth a lot of money to me. If I am offloading some of my thought processes to a machine, I want to own that machine. Otherwise, it's like if someone else owns a part of my mind and has a backdoor into my mind.
tyre
I'm curious what people are sending to Claude that is so secret. Claude knows about my interior decorating, questions about light bulbs, curiosity about what the Galactic Empire was even trying to do, unpacking SCOTUS decisions, shoe trees, Fed inflation history, etc.

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.

cle
Anthropic's goal is to commoditize intelligence. People who use their brains / intelligence for competitive advantage might not want to contribute training data for that goal.
AdieuToLogic
> Anthropic's goal is to commoditize intelligence.

And Google's original goal was to organize the world's information.

How did that turn out?

AdieuToLogic
> I'm curious what people are sending to Claude that is so secret.

When Claude is used in a professional setting, any or all of:

  Proprietary intellectual property (a.k.a. system code)
  PII[0] of the employee, customers, or both
  HIPAA[1] data known to a system
  Internal communications not meant to be publicized
  Sensitive data, such as SSH keys and the like
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...

tyre
Anthropic will sign BAAs. They are HIPAA compliant (we used them.)

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.

juiceland
Are you willing to bet that your lack of imagination for exploitation is precisely that of several multibillion dollar companies?
jrecyclebin
This was my thought as well. I have a local model monitoring my finances and personal wiki - things I wouldn't want Claude to touch - and the Qwen 3.5 9b handles it all just perfectly.

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.

throwaway894345
I’m very sympathetic to this point of view but I also can’t remotely afford the hardware required to get in the ballpark of Fable.
ChickeNES
For me, I'm glad they train on my stuff if it improves the model. Hell, I've been using tons of muse-spark-1.3-contributor for this very reason (and because it's a decent model for a bargain basement price)
shadowpho
I like this calculator but it’s really wrong at least for dgx spark. I have one and I get 4x the tokens/s .
rlindsey123
Aw very interesting! This is great feedback - what model are you running? I'm keen to do more crowdsourced data as time goes on.
chasd00
Not a fair comparison really. If you can run a model locally then you can somewhat train out the guardrails, censorship, and brand-safety. That has value a subscription does not.

Idk about the quality of this setup but just pasting it here as an example. https://explainx.ai/blog/heretic-llm-abliteration-guide-2026

ChickeNES
> If you can run a model locally then you can somewhat train out the guardrails, censorship, and brand-safety.

When does the average person actually need to do that?

xnx
That can also be done with neoclouds.
ThunderSizzle
Claude Code is $100+ or else be constantly throttled. My usage on GHCP was gonna be $300+ a month.

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.

no-name-here
An R9700 has 32 GB RAM. 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?
dwb
You should be comparing the value you get. If you get as much value from a local model as a hosted one, the size difference doesn’t matter.
bix6
Fun feature: can you show some sort of list of the best combos? Eg shortest payoff time for best capability in various situations.
rlindsey123
Good idea, pretty crude but it's up: https://sunkcost.ai/best/

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.

bix6
That was fast!

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?

QwenGlazer9000
Yeah no it does not pay for itself just comparing to cloud. Not at these prices at least, people far richer than you or I buy these things wholesale, no scalper, bought a significant amount at cheaper prices, and are wired up the ass with VC money.

The premium is not having your million dollar prize and career stolen by billionaires.

[deleted]
(comment deleted)
[deleted]
(comment deleted)
monksy
I wish you could put different setups on here. I have a couple of A6000s on an AM5.
bpbp-mango
can you add RTX cards too please? 5090 and 6000
gfody
should throw in a tt-quietbox
serial_dev
In the “The small print that isn't small” you describe all the disadvantages of running your models locally, but none of the advantages (just check the rest of the comment section for inspiration on that).
01HNNWZ0MV43FF
I was just gonna throw a beefy Ryzen into an ATX chassis. I don't want to pay Mac prices
harhargange
Also, i also use my gpu for rendering and learning and playing games.
Zetaphor
This tells me that the max throughput for the models I'm running on my hardware is lower than it actually is. Please allow us to tweak all the variables instead of locking me in to whatever rate you found by searching
rlindsey123
Thanks for all the feedback. You can now enter your own measured tok/s for any machine and model.
v3ss0n
Besides from privacy: I already making twice now.you own the hardware and the price had doubled since i bought. Almost tripled. You missed the opportunity and i have 4 of those awesome machines. Cry on.

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 .

0xbadcafebee
It pays for itself very quickly if you do 24/7 generation. Use an AI agent that orchestrates other agents working on many things at once constantly. If speed is a factor, you'd not buy a Macbook, you'd buy dual 24GB RTX 3090s. About the same price, but at least 6x as fast as a M5 Max.

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.

dumberquestions
Yeah I'm surprised no one pointed this out, if something like persistent agents gets more popular/useful, the local option pays for itself surprisingly quickly.
redox99
The math is wrong, the tok/s is at least 2x that, at least with MTP and Q8 KV which you should always use. And the default tokens a day is ridiculously low at least for coding.

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.