Mac mini vs 256GB Mac Studio for an always-on AI home node -am I overthinking local AI?
I’m trying to decide between buying a 64GB Mac mini as an always on home node, or spending roughly $12,000 AUD more and getting a 256GB Mac Studio.
My main use case is building a personal AI assistant / second brain. I’d like something always on that I can access remotely, connect to my documents, notes, calendar, personal projects, etc., and eventually build more agentic workflows around.
The sensible option seems to be the Mac mini. I could run Codex/cloud frontier models for the actual intelligence and use the Mini as the always-on execution layer. It would also handle storage, databases, RAG, automations, smaller local models, remote access, etc.
The Mac Studio appeals to me because I could run genuinely large models locally I.e GPT OSS 120B and other ~100B+ models — and keep sensitive documents and personal context completely local. I’m also interested in experimenting with local AI/ML more generally, so part of the appeal is simply having a powerful AI playground at home.
My concern is that I’ll spend all this money on the Studio, spend weeks getting my local setup perfect, and then realise that frontier models are substantially better and I still default to ChatGPT/Codex for most things.
There’s also a 512GB Mac Studio, but I’ve more or less ruled that out. I can’t currently identify enough useful models that require >256GB but fit within 512GB to justify the extra cost. 256GB feels like the point where I can run almost everything I’m realistically interested in while still being somewhat financially comfortable with the purchase.
So I’m basically choosing between:
64GB Mac mini: inexpensive, low-power always-on node + Codex/frontier cloud models.
256GB Mac Studio: ~$12k AUD more, but becomes a serious local AI workstation capable of running large models privately and giving me a platform to experiment with local AI over the next several years.
For people who actually run large models locally: did having 128–256GB+ of unified memory meaningfully change what you use local AI for, or did you eventually find yourself going back to frontier APIs anyway?