I don't mind it, I open Apple stock. But I'm def not buying into their rebranding of integrated GPU under the guise of Unified Memory.
Aren't the OpenClaw enjoyers buying Mac Minis because it's the cheapest thing which runs macOS, the only platform which can programmatically interface with iMessage and other Apple ecosystem stuff? It has nothing to do with the hardware really.
Still, buying a brand new Mac Mini for that purpose seems kind of pointless when a used M1 model would achieve the same thing.
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.
That's likely only part of the reason. Mac Mini is now "cheap" because everyone exploded in price. RAM and SSD etc have all gone up massively. Not the mention Mac mini is easy out of the box experience.
I considered the mac mini at the time, but the mac mini only makes sense if you need the local processing power or the apple ecosystem integration. It's certainly not cheaper if you just need a small box to make API calls and do minimal local processing.
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.
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).
The Mac mini strangely is and has been a very good deal for years now.
This arb you’re talking about doesn’t exist. An m1 studio with 64 gb was $1300 prior to openclaw. You’re not getting that today.
I would have preferred that too since I could Asahi it later. It’s just not cheap any more. The m4 is flat $500 at microcenter.
[1]: https://developer.apple.com/documentation/virtualization/usi...
https://cua.ai/docs/lume https://docs.openclaw.ai/channels/bluebubbles
For the same price in API calls, you could fund AI driven development across a small team for quite a long while.
Whether that remains the case once those models are no longer subsidized, TBD. But as of today the comparison isn't even close.
Assuming, of course, that your legal team signs off on their assurance not to train on or store your data with said Enterprise plans.
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.
Suddenly that $40k is quite reasonable because you’ll never pay another dollar for st least 2-3 years.
2-3 years ago people were fantasizing on running local models on a consumer nvidia RTX GPU.
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"?
And while it is stupid slow, you can run models of hard drive or swap space. You wouldn’t do it normally, but it can be done to check an answer in one model versus another.
Sonnet is so fast too. GPT-5.2 needs reasoning tuned up to get tool calling reliable and Qwen3 Coder Next wasn’t close. I haven’t tried Qwen3.5-A3B. Hearing rave reviews though.
If you’re using successfully some model knowing that alone is very helpful to me.
> But I'm def not buying into their rebranding of integrated GPU under the guise of Unified Memory.
But it is Unified Memory? Thanks to Intel iGPU term is tainted for a long time.
