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People really have a twisted idea about how corporate America works. Apple does not have a psyops division cooking up ever more elaborate ways to seed demand for low-end, low-margin Macs via bot armies. They honestly don’t need to, because they’re making an avalanche of cash from the iPhone.

People really were talking about and buying Minis due to OpenClaw, because they wanted something that was on all the time and had access to all their stuff in macOS. Occam’s Razor.

The openclaw crowd were just former cryptobros onto their next grift: performative posts about AI that would generate enough views/engagement/ad revenue to pay for the hardware… assuming they were even telling the truth about having it at all.

None shipped anything of value. Same with anyone claiming to be running dozens or hundreds of agents 24/4.

Apple's marketing isn't that smart. They're very traditional. You give them way too much credit.

I am using Mac studios for local inference, but not for openclaw. It was simply the best bang for the buck at the time, and the software side of things has gotten even better since then. I haven’t run the pricing comparison but I have a feeling with the direction they’re heading that it’s better to hold off for m6 as it may assuage various bottlenecks if you are latency sensitive.

It's not. This article is weirdly light on details. The original source article from The Information apparently mentions that companies like OpenAI are buying them up to run RL. So it's yet another case of AI companies buying all the compute.

* I say apparently because The Information wants you to sign up for access to the article, but I found this mentioned in multiple summaries of the article like this one [1].

[1]: https://tech-insider.org/openai-mac-buying-apple-supply-shor...

This may be your experience

For mine I was on the fence - waited an hour or so and the delivery date went from early oct to 10-12 weeks so i decided to wait on the 512gb version

Additional source: https://www.youtube.com/watch?v=OpqGf0m5FaQ

Agreed. It’s also nonsense. You need an NVidia gpu to do any serious work.

I don't think you need the scare quotes. product lifecycles can take years and the actual neo demand was actually pretty insane. They were using it to soak up demand for binned a18 chips and it would have been irresponsible to forecast that they'd have the demand they did when there's another perfectly reasonable universe where 8 gigs was too much of a compromise and it flopped.

this article's also about enterprise demand specifically. That's a bit surprising to me as well frankly. I'd have thought the primary market for mac studios would be hobbyists/enthusiasts with a bunch of disposable income who are willing to pay 18k for a 512 gb machine to run glm 3.5 flash or 9k to run deepseek v4 flash locally. It's competing with a $200/mo subscription or renting server gpu time for open source models during a memory shortage - and idk if it's going to be powerful enough to train or fine tune so it's really just inference. seems reasonable to be surprised

What is useful marketing in saying "we could have made more money but accidentally didn't"?

I have a strong suspicion its because of openclaw mania that happened like 3 to 4 months ago.

Basically you can have your own 24/7 AI Employee. At-least thats the appeal and folks were buying mac minis massively.

Just my gut feel. Everybody moved on from that now. But it was a big deal back then.

the machines arent optimized for it is why. the main driving factor is large unified memory which makes large(r) models possible, but there isnt the gpu horsepower to back it up. essentially, it fits the corner of the market that wants large models and is ok with running them slowly which doesnt sound like it would be a large market.

There are probably people in Apple who think desktop is dead or pro-only and didn’t think many people would want a lower end cheaper Mac. Why wouldn’t they just get an iPad?

Likewise. I have a huge demand personally to run AI noise-filtering models on many TB per month of raw video files. It takes about 3 days per file.

Apples ProRes codec is only licensed to run in high quality mode on a Mac, and so my Nvidia PC can’t do what I need. Thus, I own the beefiest Mac Studio you can currently buy. I would pay more for more TFlops.

I have done local LLM on there but it wasn’t interesting. Far worse performance and intelligence per dollar than the cloud boys.

There is no cloud offering for my video needs though.

A C-level executive I know is getting a top-of-the-line new Mac simply to function as a personal build server and host for agentic coding instances - they are able to orchestrate so many parallel projects that they're hitting RAM limits from sessions and the builds and local test runs they're kicking off (largly unsupervised). Before AI, they'd only had a MacBook Air; this completely changes their workflows. They talk about how many other executives they've met are equally giddy at having gone from coding few to no projects themselves, to coding more projects in parallel than any of their respective pre-AI technical colleagues.

I'd suspect that agentic coding has birthed so many new effective engineers, that the entire dynamics of demand for high-end machines have been upended.

I’ve been happy training and running inference for small language models on my M4 Mac.

Inference with MLX is surprisingly zippy. I’m running a classification task on the entire HN comment dataset and it’s projected to take about two and a half days, which is not bad considering we’re talking about tens of millions of comments.

Yes, I could do it much more quickly by throwing Modal GPUs at it but this is low-priority work. I might as well throw my M4 a bone.

> Just provisioning a new instance and copying the relevant checkpoints and things can take 25 minutes.

Modal significantly improves this. Highly recommend.

Are you training something so big you need that much unified RAM though?

If you can fit it on a GPU, and especially for training, it is so much quicker than a Mac.

Same, but with vision models. Unfortunately, I might be at my limit locally. I have three models that I'm using to find and identify objects in pictures. The largest dataset and model now takes about 8 hours per epoch on my Mac M4 with 16G memory.

What are you training on using self play? Like alpha go? Curious what your setup is like .

I’m doing the same!

Do you find that CoreML manages to fill up your drive with so many tiny files that a reboot takes hours to clean them up? I keep meaning to get my friends still inside the spaceship to file a radar about that.

What game are you building?

A simple example.

I have an older M2 Mac mini that does the OCR and visual description of all my screenshots. Screenshots are stored on my NAS.

I like to screenshot things as a quick way to remember. They are things that I would not be comfortable sending a cloud provider (customer data, prototype screenshots, bank dispute details).

It runs Qwen3.5:9b and glm5.2-ocr with Ollama and uses about 10GB of RAM. It automatically releases the models from RAM after 5 minutes of inactivity so it is pretty seamless to leave running in the background.

All the details are stored in a simple webapp with a SQLite db that I can search through.

> it seems really far off from the kind of experience even a basic $20/month subscription gets me.

The $20/month subs are much stronger than the local models you can run, even with how far local models have advanced lately.

The appeal of local models is that the data never leaves your network so you can feel safer putting sensitive content into it. It also feels “free” to use when you’ve already paid for the hardware.

But it doesn’t perform better and if you do the math you’re probably not saving money either. It’s helpful for things that you can’t or don’t want to outsource to a 3rd party.

>I realize I’m somewhat limited (16GB RTX 9070), but still, it seems really far off from the kind of experience even a basic $20/month subscription gets me.

I just ordered a new Mac Studio M5 Max 128GB $5899 ($6400 with tax) to be able to run the bigger "consumer size" models in the 70B parameter range (~96 GB). That said, I have no illusions that this expensive setup with a Qwen Flash coding LLM will be comparable to a $20/month subscription. Even upgrading to an even more expensive Mac Ultra 256GB for $10000 to hold a bigger model still won't be comparable. Apple hasn't shipped my Mac yet and I'm still considering cancelling it and downgrading to a smaller 64GB RAM config ($4299) to save $1600.

Why did I initially spend the extra $1600 if I knew ahead of time that it wasn't as good as cloud AI? Because I thought I could use some local LLM for the easy tasks or when I hit cloud rate limits. No issues with privacy so that wasn't part of the motivation at all. I just wanted some local AI capability to augment a subscription. I've not totally convinced myself of the cost/benefit of this.

Based on today's consumer hardware landscape, you're paying very high prices for crippled capability compared to the cloud AI subscriptions. We're also in a transition period where the next iteration of hardware improvements have some compelling features for local AI. Apple's upcoming M7 (2027 or 2028) is anticipated to have better GPU and neural engine to help with prefill TTFT. AMD Strix Halo is about to release 192GB system which is a big upgrade to their current 128GB ai pc. Maybe apply my $1600 savings towards those newer products. Those future products will still be very expensive but maybe the cost/benefit will be better.

A huge benefit of local setups at our studio is that a lot of our software can't run headless, so when we're having agents work in Blender or in Unity etc with MCP that'd otherwise eat up our normal computer use. (Try to have two people try to work in the same Unity editor at once... then try ten!)

We also built some QA agents that are always playing our games from the same builds a player would and flagging things to fix/improve; that alone needs the game focused and front-and-center so it can properly screen-capture for deciding what inputs to take next (and for screenshots/replays), which also means we can't really do any hands-on work at all on the machine when it's running.

Having a separate (and tiny) machine for all of this has been great. We don't bother with local models because, you're right, the $20/month sub is way better than anything that can run on small consumer hardware atm.

I’ve been running DeepSeek 4 Flash, Qwen 27B and Qwen 9B on local hardware. They work well for coding and document review tasks. I think Qwen 9B local on a 5090 might be legitimately helpful for small task agents in omp, since it is ridiculously fast. But my motivation is that I have data that I unfortunately can’t share with 3rd parties.

I have been eyeing a 512 GB Mac 5 Ultra to run full DS4 pro locally, which I expect would be pretty amazing as far as quality/recall. The only downside is that the speed is a lot slower than something like 27B on the 5090.

Local inference can't compete with cloud on speed, intelligence and economics. It's all about freedom, privacy, control, sovereignty.

It's about not having to accept any of the stupid "terms" of the corporations. It's about doing things the big labs don't allow you to do, like cybersecurity stuff, or even just chatting with the AI about some wrongthink.

Image and Video gen is superior locally, because you can tweak more, use LoRAs, use whatever model fine tunes, and generate uncensored content, plus as you're often cherry picking from multiple gens it ends up being cheaper for comparable quality as well.

Local coding is a step down but good enough for a lot of things if you have privacy concerns.

What models you running? What effort level?

Wild oversimplification, and benchmarks vary widely, but I've read a lot of benchmarks suggesting that Qwen3.8-27B (xhigh effort) competes with near-frontier models at a lot of coding tasks. To the best of my understanding it's not going to run very feasibly in 16GB of VRAM at usable quants however.

r/LocalLLM and r/LocalLlama are noisy, but valuable sources of anecdata if you have the time (or the tokens, hah) to comb through them. You are going to see a lot of modest setups there, and also guys with $20K+ of hardware.

The two things (besides my bank account) that keep me from investing heavily in local are (1) we are not guaranteed to get a steady release of open models in the future (2) a lot of the "fun" stuff LLM stuff that interests me involves orchestrating lots of parallel agents, which of course multiples the hardware you need to achieve it.

For example, I've been having good results having both Sol and Opus review the same PR, and then I have them cross-review each others' PRs. A next step I'd like to consider is maybe having a swarm of Luna agents review the same PR and have them fight it out... maybe with Sol doing final arbitration? I suspect 5-10 Lunas might outperform a single Opus. Or maybe not. But at any rate, that would be impractical in a homelab without a pretty big hardware (or time) budget.

In my limited experience, not quite yet but we are damn close. Qwen 3.8 27b is it. If I could run this as a decent speed, I would no longer need cloud models at all. I'm actually currently trying it out in the cloud to pay for the inference speed but the model is fully runnable at home.

I realistically costs $5-10k to replicate a ChatGPT like agent. And it doesn't scale.

That's still really close. And models and quantization etc keep improving.

I'm absolutely positive that I'll be switching to mostly local AI in the next 5 years.

It's not that far off anymore. On my 7900 XTX 24GB, I can run Qwen3.8 27B with 131K context at Q4_K_M (55 tok/s with MTP). Excluding hardware cost, it's about $0.02 tok/M in and $0.40 tok/M out (cached in $0.0001). On OpenRouter, that would cost more than 10x what it actually costs me.

Of course, 131k context at 4-bit quant is a trade off, but even then, it's VERY capable. It doesn't feel that far behind something like GPT 5.6 Luna.

I'm using a Mac to do bulk diarized transcription (STT). Most services run in the $.05-1.00+ per hour of transcribed audio.

My Mac can do ~200x realtime (1 hour takes 20s or so). I can do several thousand hours per day. Its pretty incredible

Not sure how much that qualifies as AI vs LLM usage, but it seems to work pretty good

Local setups aren't going to make sense purely from a cost perspective, and definitely not when you are buying Apple hardware. AI subscriptions are too highly subsidized right now.

One use case I find cost effective is using it as a voice assistant for Home Assistant. API pricing on models is very weird compared to the normal chats, so I use Qwen/GPT-OSS on my Mac Studio via llama.cpp server.

For software development tasks, Qwen 3.8 27B is genuinely excellent, but you need 32+ GB of VRAM to run it well with decent context, and enough memory bandwidth and compute to run it at a decent pace. With an M5 Max Mac Studio, you can do that decently well.

Also, the $20/month subscriptions are HEAVILY subsidized, so it's not an apples-to-apples comparison really

From what I’ve been seeing, the Mac studios do look like they have potential. I was looking to drop $10k-$15k on one until recently. After comparing a Radeon 7900 XTX vs Ryzen Halos 128GB vs M1 MacBook Pro 64Gb, I landed on just getting an external closure setup with Nvidia RTX 5090.

The model I’m specifically targeting to use at high speeds is Qwen 3.8 27b @q4ks. This model actually proved to be good at coding (it sits somewhere between Sonnet 5 and Opus 5 capability). M1 got 10 tok/s, Ryzen Halo 20tok/s, and Radeon 7900 XTX 50tok/s (can only do 128k context window in Radeon card).

The prefill gets extremely slow around 50k tokens in context window (whatever prompt processing stage entails could be wrong about phases here). It takes about 2 hours to fill the context.

Even with a drafter model intended for speed instead of mtp, I can’t get past 70tok/s, still is extremely slow to process prompts as context grows, and drops down to 40-50tok/s anyway making this config still moot for improvement on my Radeon card.

The only thing I can point to slowing me down is bandwidth of the card itself.

I am waiting to actually get my 5090 right now and I am betting that the 1700 Gbps of capacity will fix my prompt processing speeds. I don’t need full PCIe lane bandwidth to serve my house I just need to load the full model into vRAM and let the GPU do its thing.

Additional benefit to the external enclosure route is being able to migrate the inference between devices more easily. I can develop out the infrastructure then migrate the card to be hooked up to a shared node in the house with all the tools necessary for my family to take advantage of the privacy enhancement that comes with local inference.

The article implies the demand is for running locally. I’m not convinced, at least with a mac minis. Most people I know and myself buy the mini as it is always on, easy to setup, and isolated from my main computer which is a laptop. The mini is driving the use of the $20/month subscriptions.

I think 24gb is the bare minimum for a local qwen3.8 based setup. I've used qwen3.6 and it's not as straightforward as "can it replace <insert the most cost-effective cloud solution today>"

Local llms don't suffer from cloud availability issues. Anyone that used Google models know that sometimes they just don't have capacity whatsoever, at least that was the state of things some months back when I used them. Just bear in mind if needed, cloud providers will prioritise API and corporate customers over subscriptions if availability degrades more.

Also they don't have the same guardrails as the other models, so for hacking, reverse engineering and black coding (piracy etc...) these local models might be the only options.

16GB VRAM could load a small quantised qwen 27B model but it would be a ways away from a frontier cloud model.

Though keep in mind not being beholden to shenanigans from said cloud companies (and interference from government entities!) is definitely worth something intangible.

The principle of KISS keeps coming to mind when I consider local computing. I'm looking forward to the day we can just run Opus-level models at 100 tok/sec on consumer hardware.

But currently it's really hard to beat anything offered by the cloud companies. And the cost and complexity of setting it all up, just to barely (if at all) touch on Opus-level intelligence makes it seem like we're not quite there for the common man (enthusiasts are a different story.)

I am very excited for open source local models, and we're nearly there, but it's still too complex and expensive to be my daily driver (yet).

Right now sweet spot is voice transcription. Meeting recording apps are genuinely better locally than in cloud. Can run on an M1 easily. Latency matters. I built https://github.com/scosman/Biscotti and see zero reason to use cloud ever again.

LLMs are harder: not much useful below 12B, and the 700B+ ones are really much better. Models like Qwen 3.8 27b show promise: in a few years pretty good local AI should be in reach for anyone willing to buy a $1000 computer (but who knows what your $20 sub buys you then).

I was looking at $10k Mac Studio with M5 Ultra and 256 GB for local experiments, but then struggled to find what really good modern model I can fit into it. Yes, it can run a good dense 27B at Q8 with plenty of context, but what beyond that? IIUC, some Deepseek flash variants at Q4 are also feasible, but I am not sure if the quality will be good. They also don’t run that fast, like about 30 t/s

So if I stay within 35B, especially MOE, my M5 Pro 64GB MBP can also run them well, and it can do plenty of other stuff too including gaming. While 256 GB with such RAM bandwidth and powerful GPU sounds like fun on paper, it doesn’t seem to be the next level compared to 64 GB

Really curious what people run on 256 GB Macs

I have a RX 9070 also. I run llama-swap with a fill-in-middle 7B model, local 9B model, and it proxies up to OpenRouter for the bigger stuff. I think that's where the sweet spot is right now.

I've spent $2 in the last 2 weeks on OpenRouter. I've been trying to only use the medium sized models that I would otherwise be able to run on a nice local setup. That nice local setup would cost ~$4k. I don't know what the operating cost would be, but I would be concerned that my home electricity would cost more than at a datacenter. It just doesn't make sense right now except for privacy reasons.

I'm probably going to hoarde open weights models in the ~31B range until memory costs fall in a few years. Then, I'll buy some hardware to run at home just so I feel more sovereign over my stack regardless the cost/token speed.

In my experience so far, separately from privacy concerns there's a specific use case where cheap local shines--when unlimited shots on /goal with a dumber model is better than limited time with a smarter model. This looks less like the assisted-coding scenario that's commonly brought up as a good local scenario, because if you're searching for a fast and accurate solution to a single blocking problem, the bare minimum for a model that can do that is a 24GB dGPU or a 64GB Mac. Instead, this looks more like a Hermes agent on an Raspberry Pi driving OpenCode on an old gaming computer with just enough RAM and VRAM to handle an MoE, churning out something overnight that would quickly exhaust the subscription plans, like a knowledge graph for a large document corpus.

In my experience they work well for some jobs.

I recently built a minimal Dark Software Factory out of an N150 Mini PC. It uses three models; Sonnit, Sol, and Gemma.

But, I have a LOT of instructions about how I prefer the software it builds. Gemma doesn’t handle all my instructions very well. But it’s close!

I’m running gemma-4-12b because I have limited RAM and larger models were too slow.

I do two types of jobs: planning and prototyping. It has done fine at some of my planning rounds.

I still consider it experimental and don’t use it a lot but I think we’re getting there.

There are many enterprise environments where running modern models is... difficult. Rather than fighting security for months, a user could petition for a mac studio and have rough cost parity with a z.ai subscription.

if they are a heavy user, perhaps they string 4x together.

I’d be curious to use them on larger data sets. Log files, for instance. High volume, might be low value per line but not much cost per token if you already have a gpu to interpret them using a LLM.

Data privacy and "unsafe" models are pretty valid reasons to use local models. If I want to generate violent images/text you cannot do that using cloud models.

I was getting semi-useful results from a 128GB M4 Max. That was a few months ago, and the models have improved (quite a bit) since then, but now I'm happy to send my $20/month to get Claude code.

It's still frustrating as hell to come down in the morning, having given it a list of tasks to do overnight, with tests to pass before they're "done" and find that it worked for about 20 minutes after I went to bed, and decided that it would stop at "3am" (it wasn't) and "not do significant work this at this late hour". Like WTF ? You're an LLM. You don't sleep.

Bloody training data full of humans demanding sleep. I tells ya...

Could you elaborate on your experience with local models on your card? I've been thinking of upgrading to 9070 XT, and was thinking the 16GB would be okay-ish to at least run something usable locally, no?

I've been experimenting with local models on an M5 Max MBP w/ 128GB of RAM since March of this year. Generally I've had very good results. Where things were lacking initially was with tool calling and the need to rely on tool calling for functionality like web search, which is otherwise well integrated in the cloud models. There is also a lot more work required on the harness side, however at this point (August 2026) there is not only much better tool calling in local models, but community supported projects have built good harnesses. Pi.dev and OpenCode + a SOTA local model is /very/ /very/ capable these days. Using LM Studio's built-in chat with a decent system prompt and proper tuning with local models is /very/ capable these days. Cloud models are still better, but it's the "harness" (not in the desktop app, but in the backend) that makes it so for the most part.

IMO local models require a substantial amount of prompt+harness engineering to get in the neighborhood of what you'd get from a cloud model. Which isn't a bad thing, you'll learn a whole lot about how these things work.

What you'll learn pretty quickly from said engineering is that there's a lot more to a good LLM than just the weights themselves. You need a good search provider (also self-hostable, but sounds easier than it really is). You need (well, it's debatable) a memory system. You need a good system for up-to-date library references like a Context7 (also self-hostable but the options are surprisingly not that good). You need a good set of specialized subagents that can perform various tasks well -- for the sake of "doing things well" but also managing context efficiently.

When you've got all that, local models can be _extremely_ useful. But there's one other important thing and that's decent hardware, unfortunately. A lot of people try out local models using small consumer GPUs or Macs and are rightfully unimpressed with the performance. And if the performance doesn't get them, usually they have expectations that they'll perform at Claude levels out of the box. Getting in that neighborhood, like I said, definitely requires some work.

I have multiple 48GB friends that successfully run smaller quantized models for general assistance + light editing (coding, spreadsheets, etc), that don't require very heavy models.

So yes, they are genuinely very useful, but they are not yet a full replacement unless you have more powerful hardware and or don't need more intelligent ai.

You're limited by the manufacturer (CUDA is king, thus NVIDIA is the king right now) and your lack of VRAM will make using a useful model difficult.

I'm not surprised at all.

Context: I have a farm of DGX Sparks and several RTX 6000's, and can run very close to foundational models with ~2 sparks

I might be wrong but subscription models don't give you API access. I'm only interested in API access when it comes to personal use, so local models running for free makes sense for me.

That said I have an RTX 5090, not a Mac Mini, so it's not exactly the same level of performance... The latest open models run at 200 tpm at around 30B params.

> 16GB RTX 9070

What's an RTX 9070? Do you mean the RX 9070 or RTX 5070?

You should listen to the podcast Acquired, specifically Nvidia and then Jensen Huang. They basically lucked into AI. Some researcher was using Nvidia gaming cards, and reached out to them about questions on CUDA. That email eventually turned them into a trillion dollar question.

Maybe a bit of hindsight bias / the outside view here, but I feel like they're completely asleep if they didn't anticipate strong demand for this specific use case.

Was this the case in the past?

My vibes were that Apple wound down the “actual work” side of their operations (including machines like Xserve), because Ives couldn’t handle the unsexiness and unpredictability of business requirements in hardware.

He was self-indulgent and only wanted to work on things that “vibed” with him, rather than what the customers needed. It’s easy to be creative when you get to do what you want to do, it’s hard when you have hard constraints.

It's also fun to see how many people here believed this was all some clear deliberate strategy in the first place rather than an accident.

No ‘staff focused on developer relations’ is entirely unsurprising based on what I see from the outside.

> "The company reportedly did not possess an engineering team dedicated to business customers or staff focused on developer relations, and lacked an enterprise AI strategy"

This is clearly a mis-statement, they have a whole annual conference for developers. Maybe they mean specifically AI devs.

Isn’t a Mac mini annoying to use as an HTPC? You have to deal with a remote, software, and a full OS, compared with an Apple TV, which has a good remote and is optimized for TV use.

Our Blessed Homeland / Their Barbarous Wastes

I need a new little Mac for my music studio, currently an M2 MacBook Pro. I thought I'd be fun to experiment with some local models as well. Well, let's price up an M5 Pro. $3,019 with 64GB RAM and a 1TB HD. Three thousand American dollars for a Mac Mini. Beefy spec for sure but not comically so.

Frankly even the entry price is a bit high - I remember buying one for my son a few years ago (M1 mini) and it was a few hundred; now we're up to $900 for the base model.

This is just so incredibly disrespectful to so many people.

I wish they would stop with the new-iPhone-every-year nonsense and refocus on quality, fix some bugs… but yeah, not gonna happen

Huh, glad I grabbed my Neo two weeks ago. It's the "top" spec version, but still a good bit less than a MBA - seemed like a pretty reasonable replacement for the M1 iPadPro it replaced (wanted to go back to a normal laptop vs tablet).

They're making the smartest possible move: let others burn insane amounts of capital and time finding the quirks and once they see a viable lane, execute.

It's old Steve Jobs logic. Works backwards from the customer experience to the technology (they're the only big player I see doing this).

I'm excited to hear the ram crisis will be over. But will it?

Switching from Q4 to Q8 was a game changer when I upgraded

The part where people starting buying Mac minis just to run OpenClaw was a pretty sudden movement in the market. These computers aren't powerful enough to run big local LLMs but are still being purchased for AI workflows.

A year ago you could get an M4 Mac mini for $399 on sale and now the same one used goes for over $700. The general AI RAM/SSD spike is part of that but there was also a huge demand spike for small, powerful, desktop machines that could be easily configured with these workflow tools.

Sad that Apple is stooping to the level of spamming the web with bots and fake news to advertise their products. Are there laws against this sort of thing?

32GB is not enough, it's unified/shared memory, you need to have space for usual system and user apps/services.

64GB+ or dedicated 48GB (2x24 on GPUs) is IMHO absolute minimum.

> If I had to pick a product, I'd say an affordable 32GB mac would be the sweet spot for running local models that function well like Qwen 3.8.

https://www.canirun.ai (five months ago: https://news.ycombinator.com/item?id=47363754 377 comments)

> If I had to pick a product, I'd say an affordable 32GB mac would be the sweet spot for running local models that function well like Qwen 3.8.

32GB is not enough RAM. I don't even own a device with less than 36GB at this point, and that device I only have because my employer is being cheap. 64GB is a reasonable starting point for running local LLMs + normal tasks. 128GB let's you really run most smaller models like Qwen 27B and 35BA3B with good context. Even Qwen3.8-Flash-Next runs in 128GB with a 4-bit quant.

32GB would be limited to running models like Gemma4 12B and smaller dense Qwen versions like 9B unless you were using very small quants which damages quality of response.

I don't think they're ready for local ai. They have memory + memory bandwidth, that's it.

I also don't think they do "technology". For example, containers have been around for a long time, and apple didn't show up. (I know they have some support now). Imagine an apple-native docker/podman doing something like FROM macos:10.12

I was actually surprised when they did their own chips. I figure it was about control.

I think they’re too scared to ‘own’ it - it would be someone else’s model and potential security issue.

But apple have to own everything they do so they’re in a bind

If those customers are in the market you want to develop. If they are not, money is money but if it comes from the wrong people it might slow you down.

> 1. Running an agent like OpenClaude. The $599 Mac Mini was an insanely good deal for this.

I still have zero clue how "Buy a $599 Mac Mini to have a sandboxed LLM API caller" became the default. If you're not doing local inference and don't need to inject into iMessage or iCloud, all you need to run openclaw-style harnesses that call external APIs is a Raspberry Pi 4B, an N100, an HTPC, or that 10 year old laptop sitting in your desk.

You have the m4 pro right? I thought the m5 pro mac mini was only just announced

> Classic monopoly move: Control the user base, then control hardware

Classic monopoly move by who?

Apple created MLX as an open source framework to allow users to run any open model locally.

> you know they have them internally...

What makes you think that? There's a lot of data centers that sell you access to colocated Mac Mini's, they have added FileVault unlock via SSH in the boot process which also makes things easier. There's not that many reasons to run a Mac in the cloud unless you have some very specific Mac related workload.

They did. Now think they feel a stack on Minis or Studios fills the reduce needs better. The multiple machines one gets software redundancy in addition to everything else.

That's exactly what it is.

Which is why Apple is going to need to design around again and do something about that in house if you can design and engineer a processor or a modem you can certainly do something about memory and SSD’s, Apple buying PA Semi, Intrinsity and Anobit led to Apple Silicon, buying Infineon led to a new Apple modem the capabilities is there and the money is there in house it may take two-four years but long-term I don’t think there is any other choice.

One thing to watch for when Apple introduces the new phones coming up shortly is whether or not Apple has replaced Qualcomm in their flagship smart phones because that is coming up soon Qualcomm has given warning to their investors.

It definitely puts it out of the range of every day users but a non-insignificant proportion of people who use it for ai have become multi-millionaires because of ai. So there's definitely no shortage of people who have no problem paying those high prices.

I suspect it can't be helped at this point. RAM is the new gold. Valve even had to increase the price for a 4 year old piece of hardware[1].

[1] https://tech-insider.org/ca/steam-deck-price-increase-2026/

I don't know if you're young, or new to this industry, or what, but take a look around - the prices went up across the board. It's not just Apple.

Presumably this is on their roadmap. The Jobs-era thing would be to come in with something that people on HN would say, “yeah, I could do this myself with open source tools” but in practice falls into the category of “I could build this $30 thing for $15 for parts and then another $15 for the parts I had to replace because I screwed up the build the first time and then $30 to just buy the damn thing” that would really blow non-HN people away even if it is more expensive than most other local computing systems, but I don’t know what the Ternus-era Apple will be like.

Apple isn’t the company that eats margins but they are company that would design around the problem and I think that’s what they will do after all, they have the design and engineering and plenty of money because they didn’t burn it on AI models or data centers.

Apple doesn't design GPUs on-par with Nvidia's efficiency yet. They need an architectural overhaul to be a serious competitor, which is what I'm expecting is queued up for M7.

Nvidia has CUDA, AMD has CDNA, and Apple has... compute shaders, I guess?

Apple did not raise prices fast enough

Have you been successful with the lowball offers? That would help indicate where the supply/demand for them are.

When the M6 Minis were announced I pre-ordered and Apple gave me $480 trade-in on my entry level M4 Mini that I paid $499 for about 14 months ago

> I lowball them every time.

And?

Interesting downvote.

Apple hardware uses a unified architecture. That means the CPU and RAM are integrated together: it is very difficult/impossible to strip the RAM from these systems.

This unified architecture makes Apple hardware very good for AI work, where latency between RAM and CPU is very important

Even the SSDs (which are normally more strippable) use a proprietary hardware form factor.

I think the article mentioned the real draw:

> Apple noticeably promoted the ability to link multiple Mac Studios together into a single, more capable system for running large frontier AI models, a feature aimed at business and developer customers rather than everyday consumers.

(Couldn't read the source cited, might have more info: The Information)