[butILoveLife]

I think its just marketing, and the marketing is working. Look how many people bought Minis and ended up just paying for API calls anyway. (Saw it IRL 2x, see it on reddit openclaw daily)

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.

[jsheard]

> Look how many people bought Minis and ended up just paying for API calls anyway. (Saw it IRL 2x, see it on reddit openclaw daily)

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.

[ErneX]

It’s exactly that. They are buying the base model just for that. You are not going to do much local AI with those 16GB of ram anyway, it could be useful for small things but the main purpose of the Mini is being able to interact with the apple apps and services.

[rafaelmn]

16GB should be enough for TTS/Voice models running locally no ? I was thinking about having a home assistant setup like that where the voice is local and the brain is API based

[0x457]

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

[ErneX]

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.

[chaostheory]

No one is buying a base model Mac for local LLM. Everyone is forgetting the PC prices have drastically increased due to RAM and SSD. Meanwhile, Macs had no such price change… at least for the models that didn’t just drop today. Mac’s are just a good deal at the moment.

[jsheard]

> 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.

[AnthonyMouse]

I'm kind of curious how Apple's supply contracts actually work, because it's currently more attractive to buy a Mac with a lot of RAM than it usually is, relative to a PC. So if it's "we negotiated a price and you give us as much RAM as we sell machines" the company supplying the RAM is getting soaked because they're having to supply even more RAM to Apple for a below-market price.

But if the contract was for a specific amount of RAM and then people start coming to Apple more for high RAM machines, they're going to exhaust their contract sooner than usual and run out of cheap memory to buy. Then they have to decide if they want to lower their margins or raise the already-high price up to nosebleed levels.

[walterbell]

https://www.linkedin.com/pulse/memory-supply-chain-ai-disrup...

  Apple has accepted a 100% price increase for Samsung's LPDDR5X memory, with DRAM supply commitments secured only through the first half of 2026. Tim Cook acknowledged during the Q1 FY2026 earnings call that storage price increases would significantly impact Q2 gross margins.. Apple is evaluating ChangXin Memory Technologies (CXMT) and Yangtze Memory Technologies (YMTC) as new supply sources, attempting to rebuild pricing leverage through supply chain diversification.

[briffle]

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

[Forgeties79]

That's been the case for years. Not new to the M5's

[re-thc]

> Aren't the OpenClaw enjoyers buying Mac Minis because it's the cheapest thing which runs macOS

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.

[CrazyStat]

It's not cheap, though. Two weeks ago I bought a computer with a similar form factor (GMKtec G10). Worse CPU and GPU but same 16GB memory and a larger SSD for 40% the price of a base mac mini ($239 vs $599). It came with Windows preinstalled, but I immediately wiped that to install linux. Even a used (M-series) mac mini is substantially more expensive. It will cost me about an extra penny per day in electricity costs over a mac mini, but I won't be alive long enough for the mac mini to catch up on that metric.

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.

[stanmancan]

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.

[kllrnohj]

> you an also just buy a RPI for a fraction of the price of the GMKtec G10.

Sadly not really. The Pi 5 8gb canakit starter set, which feels like a more true price since it's including power supply, MicroSD card, and case, is now $210. The pi5 8gb by itself is $135.

A 16gb pi5 kit, to match just the RAM capacity to say nothing of the difference in storage {size, speed, quality} and networking, is then also an eye watering $300

[operatingthetan]

>Sadly not really. The Pi 5 8gb canakit starter set, which feels like a more true price since it's including power supply, MicroSD card, and case, is now $210. The pi5 8gb by itself is $135.

At that point buy a used macbook air m1.

[edm0nd]

>you an also just buy a RPI for a fraction of the price

lol. you need to look at rpi 5 prices again. they are insane.

[nicoburns]

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

[re-thc]

> 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).

[Forgeties79]

That’s a big “if” at the end. You can always make a computer cheaper “if” you strip down what you need to do with it.

The Mac mini strangely is and has been a very good deal for years now.

[philistine]

There are so few used Mac Mini around, those are all gone and what is left is to buy new.

[jermaustin1]

Worse than that, they hold their value, so buying a used M1 mini is still a few hundred bucks, and saving $200-300 by purchasing a 5 generation older mini seems like a bad deal in comparison.

[teaearlgraycold]

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.

[Octoth0rpe]

Up until a week ago, the base m4 mini (16gb ram/256gb ssd) was $399 at microcenter, now $499. Pretty shocking how good of a value that is IMO.

[teaearlgraycold]

Damn. Would be awesome to network a bunch over thunderbolt.

[Forgeties79]

That’s just somebody not doing their research and overpaying unfortunately

[someperson]

Just like with GPUs and Bitcoin they'll be a flood of old hardware on the market eventually.

[Forgeties79]

Depends on what you mean by “eventually”

[renewiltord]

Bro. The used M1 mini and studio are all gone. I was thinking of buying one for local AI before openclaw came out and went back to look and the order book is near empty. Swappa is cleared out. eBay is to the point that the m1 studio is selling for at least a thousand more.

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.

[BeetleB]

Can't they simply run MacOS on a VM on existing Mac hardware?

[sneak]

Not if you want it to be able to use the hardware identifiers to register for use with iMessage.

[jen20]

Not true as of macOS 15 onwards [1].

[1]: https://developer.apple.com/documentation/virtualization/usi...

[webXL]

I have it running in a macos VM using lume & BlueBubbles on a throwaway iCloud account. A lot of hoops to jump through, though

https://cua.ai/docs/lume https://docs.openclaw.ai/channels/bluebubbles

[shuckles]

You aren’t going to run a network connected 24/7 online agent from a laptop because it’s battery powered and portable.

[llmslave]

yes, and its funny that all these critical people dont know this

[rafram]

Why not? The integrated GPUs are quite powerful, and having access to 32+ GB of GPU memory is amazing. There's a reason people buy Macs for local LLM work. Nothing else on the market really beats it right now.

[mleo]

My M4 MacBook Pro for work just came a few weeks ago with 128 GB of RAM. Some simple voice customization started using 90GB. The unified memory value is there.

[lizknope]

Jeff Geerling had a video of using 4 Mac Studios each with 512GB RAM connected by Thunderbolt. Each machine is around $10K so this isn't cheap but the performance is impressive.

https://www.youtube.com/watch?v=x4_RsUxRjKU

[Greed]

If 40k is the barrier to entry for impressive, that doesn't really sell the usecase of local LLMs very well.

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.

[jazzyjackson]

It’s what a small business might have paid for an onprem web server a couple of decades ago before clouds caught on. I figure if a legal or medical practice saw value in LLMs it wouldn’t be a big deal to shove 50k into a closet

[Greed]

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.

[LunaSea]

At least with the server you know what you are buying.

With Anthropic you're paying for "more tokens than the free plan" which has no meaning

[ttoinou]

With M3 Max with 64GB of unified ram you can code with a local LLM, so the bar is much lower

[Greed]

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.

[nurettin]

I ran the qwen 3.5 35b a3b q4 model locally on a ryzen server with 64k context window and 5-8 tokens a second.

It is the first local model I've tried which could reason properly. Similar to Gemini 2.5 or sonnet 3.5. I gave it some tools to call , asked claude to order it around, (download quotes, print charts, set up a gnome extension) even claude was sort of impressed that it could get the job done.

Point is, it is really close. It isn't opus 4.5 yet, but very promising given the size. Local is definitely getting there and even without GPUs.

But you're right, I see no reason to spend right now.

[Greed]

Getting Opus to call something local sounds interesting, since that's more or less what it's doing with Sonnet anyway if you're using Claude Code. How are you getting it to call out to local models? Skills? Or paying the API costs and using Pi?

[robotresearcher]

Sometimes you can't push your working data to third party service, by law, by contract, or by preference.

[0x457]

I started doing it to hedge myself for inevitable disappearance of cheap inference.

[blackqueeriroh]

Sure, but now double the team size. Double it again.

Suddenly that $40k is quite reasonable because you’ll never pay another dollar for st least 2-3 years.

[prmoustache]

Would you?

2-3 years ago people were fantasizing on running local models on a consumer nvidia RTX GPU.

[spacedcowboy]

It's not. I've got a single one of those 512GB machines and it's pretty damn impressive for a local model.

[Greed]

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"?

[Hamuko]

I've tried to use a local LLM on an M4 Pro machine and it's quite painful. Not surprised that people into LLMs would pay for tokens instead of trying to force their poor MacBooks to do it.

[atwrk]

Local LLM inference is all about memory bandwidth, and an M4 pro only has about the same as a Strix Halo or DGX Spark. That's why the older ultras are popular with the local LLM crowd.

[usagisushi]

Qwen 3.5 35B-A3B and 27B have changed the game for me. I expect we'll see something comparable to Sonnet 4.6 running locally sometime this year.

[OtomotO]

This would be an absolute game changer for me. I am dictating this text now on a local model and I think this is the way to go. I want to have everything locally. I'm not opposed to AI in general or LLMs in general, but I think that sending everything over the pond is a no-go. And even if it were European, I still wouldn't want to send everything to some data center and so on. So I think this is a good, it would be a good development and I think I would even buy an Apple device for the first time since the iPod just for that.

[prettyblocks]

Could be, but it likely won't be able to support the massive context window required for performance on par with sonnet 4.6

[freeone3000]

I’m super happy with it for embedding, image recog, and semantic video segmentation tasks.

[giancarlostoro]

What are the other specs and how's your setup look? You need a minimum of 24GB of RAM for it to run 16GB or less models.

[SV_BubbleTime]

This is typically true.

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.

[jazzyjackson]

Tokens per second is abysmal no matter how much ram you have

[giancarlostoro]

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

[Hamuko]

48 GB MacBook Pro. All of the models I've tried have been slow and also offered terrible results.

[giancarlostoro]

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.

[andoando]

Local LLMs are useful for stuff like tool calling

[renewiltord]

What models are you using? I’ve found that SOTA Claudes outperform even gpt-5.2 so hard on this that it’s cheaper to just use Sonnet because num output tokens to solve problem is so much lower that TCO is lower. I’m in SF where home power is 54¢/kWh.

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.

[tcmart14]

I'm not really into AI and LLMs. I personally don't like anything they output. But the people I know who are into it and into running their own local setups are buying Studios and Minis for their at home local LLM set ups. Really, everyone I personally know who is doing their build your own with local LLMs are doing this. I don't know anyone anymore buying other computers and NVIDIA graphics cards for it.

[threatofrain]

The biggest problem with personal ML workflows on Mac right now is the software.

[cmdrmac]

I'm curious to know what software you're referring to.

[csullivannet]

Yes

[0x457]

I think people buying those don't realize requirements to run something as big as Opus, they think those gigabytes of memory on Mac studio/mini is a lot only to find out that its "meh" on context of LLMs. Plus most buy it as a gateway into Apple ecosystem for their Claws, iMessage for example.

> 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.