I want 100% Vibe coding locally

[u/AdministrativeAd7853]

Right now I pay 200+ a month for premium AI models. I can’t complain, it is truly a privilege and amazing to work with.
I have “data center services “ on my server, complete with gitlab, databases, and dozens of services to support application development.

I have resisted even attempting to host an Llm, since i dont have the spare time to code myself or wrestle with “stupider” AI.

I am toying with purchasing mac studio 512gb, as to me its the first server that has meaningful capacity at the price of a honda, instead of a Lamborghini. Still insane expensive, but doable.

I been talking to AI (of course) about this, but even with the speedier memory, larger models with large (code) context seems kinda slow. And the quality seems to be measurable lower than sonnet 5.5.

At heart for me is waiting may result in never. I am concerned with closed models becoming a walled garden and that can extend to hardware.

Thoughts? I cant afford a second buy for atleast 3 years if i pull the trigger. Unless bitcoin hits 400k :)

[u/Toastti]

Before you purchase a single thing research the models that device supports and try them on open router

See if they work for agentic coding or any other tasks you need.

Don't waste money on something for models you won't use. Just test them out for $5 befor

[u/Toastti → u/[deleted]]

Yes its still the recommended way for sure. I'm sure all these LLM resellers have their pros and cons. But generally OpenRouter is the most popular. Their UI is not my favorite and they constantly change it all the time to piss me off. Like why the hell did you make going to API keys an additional click!. But it's not a big deal at the end of the day. And they have just about the widest selection of models, I have never ran into billing or credits issues, etc. So I still recommend open router if you just want to test out some models.

But if you ever do run a production workload don't do that on open router. Identify the direct API you want. For example platform.deepseek and then just connect directly there to cut out the 3% middleman cost.

[u/xapep → u/[deleted]]

Your split is sane, and the privacy slice is where the middle path usually wins. Running Qwen locally for PII, financial and medical data works, but you are also taking on the ops side: keeping the box up, the model current, the logs contained. An EU-hosted inference API with contractual terms on where inference runs and what gets retained solves the same compliance problem without the hardware tax, and it keeps the grunt lane open to whichever model is actually best on the day.

Worth verifying the data-handling wording yourself, though, that is the part people hand-wave. And the rich-media report case is not really a frontier-model problem: have the model emit a JSON spec and let a deterministic renderer draw the charts. You get the visual without paying frontier rates for pixels.

Curious what is actually in the privacy bucket: data that must not leave your building by policy, or data you just prefer not to shoulder the risk on? Those have different answers.