> 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.

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.

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.

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.

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.

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).

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.

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.

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.

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 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...

> 16GB RTX 9070

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

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.

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.

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.

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.

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.

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

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.

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

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.

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.

> 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.

Apple did not raise prices fast enough

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.

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/

Please address all complaints to the Office of CEO, Open AI, 123 Main Street, San Francisco, California, U.S.A., 94158.

Your letter will help feed the boiler that heats the building and is much appreciated.

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)