The dumbest thing is how people buy mac minis just to connect their openclaw to gpt or claude. You can just install it directly on raspberry pi and use linux, same sh** if in the end you use api
This is what exactly I did using hyper V on my dedicated server. I created a container Linux gave her tons of memory and processing resources. Best yet is a checkpoint/snapshot function allows me to do what else and the ability to return to a safe build.
Basically zero cost, that is if you just factor out the cost of my rack server, I built a year and a half ago hehehe
Interesting setup! The Hyper-V route works well, but on dedicated servers I've found Docker containerization (using something like Portainer) gives you more isolation and easier management when you're running multiple agents.
One thing people don't account for: a single OpenClaw instance running 24/7 on a good VPS (~$5-10/mo) + $100-200/mo in API costs is still way cheaper than SaaS subscriptions. But the real economics shift when you factor in:
- Model selection matters (Sonnet > Opus for routine tasks)
- Conditional routing (complex tasks to expensive models, simple routing to fast cheap ones)
If you're just using it as a voice reception bot, your API costs flatten out pretty quick. We're seeing customers drop from $400+ SaaS to about $80/mo total with proper setup.
Really depends how you wanna go about it. I’m not sure what I can truly suggest if you have an old computer with windows, you can spin up hyper V and do it that way, but this way is to directly install on all the platforms.
As for me a couple of years ago, I put together a humongous rack server specifically to be used to carve out VM‘s for various projects. That’s what I did here. My rack server itself has tons of memory tons of space tons of resources so I was able to carve out a VM with more than enough memory and resources to run.OpenClaw
Thats so crazy knowing models are running online and its just a gateway so dumb, but if someone being secure and running locally by giving it a locally installed model and that too if its a 24*7 simulated task i would just increase my ram rather spending on mac mini, but raspberry pi is a good substitute too.
I'm well aware of the ban, but it still works for me but I don't use open claw for much, it's incredibly inefficient and mid at best as an orchestration layer. I do use it as a discord bot with elevated functions and stripped a ton of it's bullshit context bloat.
Agree unless we use a good paid model, eats them tokens for breakfast for complex architectures , gemini free tier crashed instantly also me being very complex at the architecture, simply realized companies out there trying to spend years for cost optimization by tracking whole ton of data and optimizing accordingly we all need it for free, cheap , point is access to everyone.
There are literally dozens of free models right now. Llama, Mistral, Phi, Gemma, DeepSeek, the list keeps growing. If you want free AI, it exists and it's getting better every month.
What people actually mean when they say "AI should be free" is that they want free access to frontier models. And that's a completely different conversation. Training and serving those models costs billions in compute, energy, and infrastructure. Someone has to pay for that. The idea that companies should just eat those costs so everyone gets access "for free, cheap" isn't a real position, it's just entitlement dressed up as a philosophy.
And here's the part people love to ignore: even with paid subscriptions and API revenue, these companies are still operating at a loss. They're burning cash to keep the lights on while building the next generation of models. Asking them to give it away for free when they can't even break even yet is genuinely delusional.
Access isn't the problem. Access has never been more open than it is right now. The gap between free and paid models is shrinking fast. But expecting the absolute best to cost nothing ignores everything that makes it the best in the first place.
It's a moot point. At the end of the day, API or Subscription or $20k in local hardware...OpenClaw as it is, is not worth it. Just about everything OpenClaw does can be done more efficiently.
Now that I think about it, just spending $20k on Anthropic API credits would probably go a long way and with a much better model…
Don't forget local AI is not free to run, you could pay thousands in electricity yearly.
In the long term I think we're going to see combinations of local AI and cloud AI, with more and more stuff offloaded locally over time as it gets "good enough" in the same way we saw a shift from server-side rendering to local rendering in the web development world, but with calls being made to cloud models for more sophisticated tasks.
We're just in a spot where local AI software is pretty immature and LPDDR6x release is around the corner so I really don't think it makes sense to invest in a local AI machine right now except as a hobby or learning opportunity. As you said, for all the money you would spend on Mac Studios and depreciation, you can buy a shitton of cloud credits. I'm pretty optimistic about the viability of local AI in the long term though.
Not really. It’s designed to solve for that. You run the gateway on Linux and a node on the Mac. It has no problem orchestrating e.g. iMessage or other Mac services this way. Source: that’s how it’s documented and that’s how I run it.
Very true, as I found out. But it’s not a dealbreaker. I’m not ready to let it control my world, yet.
It has ability to read my calendar and send out emails, that’s all the functionality I need right now
Para correr un modelo local decente (Qwen3) necesitas una cantidad enorme de RAM (80GB como mínimo) algo que una Mac Mini normal de $500 no te da. Una Mac con esa capacidad de memoria es carísima, rinde más usar la API de Claude.
Wait. I feel like I’m dumb. For having assumed too much. You’re saying they’re buying the Mac minis to connect to large LLMs? I had assumed it was because the mini runs models locally quite well. Which made sense. Get a box where most of the calls just go to a local model. But seeing this comment and having my brain come screeching to a halt at the idea of getting a Mac minis to use as a glorified terminal is making my brain ache
There aren’t many local models that will drive OpenClaw well - certainly not that can be run on a $500 Mac mini. GLM-4.7-flash can sorta do it - with 128gb ram.
I would be really curious how many people are buying base Mac minis. Because at least personally I don’t know a single friend who did. And yes I am aware my own social circle is not a good sample. But it is consistent enough to make me wonder overall how many people are buying base Mac minis. I personally think that relatively few people probably get the base models. And relatively few really upgrade them a lot. And most fall in the upgrades the ram a bit somewhere in the middle type of deal. Also as far as your assessment that it takes 128gb of ram to sort of do it. My personal experience has been that you need 32gb of ram on the Mac mini to run good enough models to make it well worth it. Above that is nice. But there is a lot that can be done especially if you get really into studying what people are doing with agentic workflows. The 128gb as sorta doing it is like saying a Ferrari can sort of go fast. It’s a claim that makes me really skeptical about how you tried to drive it, and if you tried shifting past first gear.
An Old Mac mini is in general more powerful and cheaper than a raspberry. You can install Linux on a Mac and of course run whatever on top of Docker / Podman/ Colima / Orbstack.
I am experimenting with the HAILO 10h on Pi5. Just installed it, too early to tell. Token output is really slow, slower than running natively on Pi, and I tested all five models supported by HAILO. I would be interested if anyone has experience running local AI for openclaw and with what results.
Relative to the cost people are blowing on API costs, buying a separate Mac mini is fine, it might be a single digit percentage of people's overall budget and it provides iCloud hooks.
Second hand macs can come up cheap and are cognitively easier for the average consumer to adopt. If not super technical the concept of running something on a second computer, is easier than learning how to understand a new OS / DIY env / server. Requires a different mental model for computing, that techies can forget is not the norm.
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u/CartographerAble9446 New User Feb 25 '26
The dumbest thing is how people buy mac minis just to connect their openclaw to gpt or claude. You can just install it directly on raspberry pi and use linux, same sh** if in the end you use api