Sure, that’s why I said maybe it’s useful for a few things. But the main reason people were recommending the Mini was for its price (base model) and having access to the Apple services for clawdbot to leverage. Not precisely for local AI.

I run ministral for my home knowledge database on 24G iMac and some other non-agentic LLM things.

> Meanwhile, Macs had no such price change

Yeah because Mac upgrade prices were already sky high, long before the component shortage. 32GB of DDR5-6000 for a PC rocketed from $100 to $500, while the cost of adding 16GB to a Mac was and still is $400.

the new models cost $200 more for each 8GB of Ram you add.. Ouch...

Someone came to be excited they got a "deal" on the newest Intel Mac Mini for hosting OpenClaw. 8GB model for $300. I kind of regret bursting their bubble by telling them you can walk over to Costco (nearest one at time of discussion was walking distance) and pay $550 for one with an M4 and 16GB of RAM.

It's cheap for what you get.

If you just need "a small box to make API calls and do minimal local processing" you an also just buy a RPI for a fraction of the price of the GMKtec G10.

All 3 serve a different purpose; just because you can buy a slower machine for less doesn't mean the price:performance of the M1 Mac Mini changes.

If you need the CPU power in the Mac Mini then it is a pretty good price-to-performance ratio.

> It came with Windows preinstalled, but I immediately wiped that to install linux.

Do you really need Openclaw now? And not claude code + zapier or Claude code + cron?

That's the point. If you have worse CPU and GPU Windows will be sluggish (it's bloated).

You would still have to do some pretty outstanding volume before that makes sense over choosing the "Enterprise" plan from OpenAI or Anthropic if data retention is the motivation.

Assuming, of course, that your legal team signs off on their assurance not to train on or store your data with said Enterprise plans.

Assuming you ran the gamut up from what you could fit on 32 or 64GB previously, how noticeable is the difference between models you can run on that vs. the 512GB you have now?

I've been working my way up from a 3090 system and I've been surprised by how underwhelming even the finetunes are for complex coding tasks, once you've worked with Opus. Does it get better? As in, noticeably and not just "hallucinates a few minutes later than usual"?

But why? Spending several thousand dollars to run sub-par models when the break-even point could still be years away seems bizarre for any real usecase where your goal is productivity over novelty. Anyone who has used Codex or Opus can attest that the difference between those and a locally available model like Qwen or Codestral is night and day.

To be clear, I totally get the idea of running local LLMs for toy reasons. But in a business context the sell on a stack of Mac Pros seems misguided at best.

Some models run worse than others but I have gotten reasonable performance on my M4 Pro with 24 GB of RAM

Try a software called TG Pro lets you override fan settings, Apple likes to let your Mac burn in an inferno before the fans kick in. It gives me more consistent throughput. I have less RAM than you and I can run some smaller models just fine, with reasonable performance. GPT20b was one.