There is a lot of "AI demand" that isn't just running inference on an LLM whose weights you downloaded.
I'm training a model using reinforcement learning with self-play. I can and do use vast.ai when scaling but for experiments it's far faster, and cheaper, to run it locally until the bugs are all figured out. Just provisioning a new instance and copying the relevant checkpoints and things can take 25 minutes. It's zero locally.
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.
It’s probably files that, over the course of a month, add up to multiple TBs.
Which would suggest that a 1Gbit fibre connection would be adequate. For serious commercial usage, multi Gbit fibre is available in many places around the world.
Is Modal at all similar to Vast.ai or just related because "It's for AI"? I looked at Modal's page for training, and it talks about using some SDK and other junk, can you not just get a beefy instance from Modal with tons of VRAM to do what you want with?
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.
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.
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.
Yeah this was what got me to start doing short rentals of bigger gpus in the clouds, upload your parquet files and it takes a couple of hours for a thing that would have my mac at 100% for a couple of days
I’m curious to know if these local AI setups are legitimately useful compared to cloud. I’ve struggled a lot to get something useful out of the hardware I have.
I realize I’m somewhat limited (16GB RX 9070), but still, it seems really far off from the kind of experience even a basic $20/month subscription gets me.
Any tips anyone might have are appreciated! I’d love to be local first and would be willing to buy hardware to get there.
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.
> I have an older M2 Mac mini that does the OCR and visual description of all my screenshots. Screenshots are stored on my NAS.
Doesn't Apple do this already within it's OS all locally? It certainly does it for OCR and categorization.
EDIT: Also, no reason to use a generic LLM for this. This functionality exists in something like Immich (both OCR and 'context categorization'), and doesn't tie you into the Apple ecosystem either.
I personally use Apple Photos for this. It stores the original, plus makes them nicely searchable, so I have a Hazel action that takes screenshots from the desktop (and from my NAS where mobile devices back them up) and imports them.
A $20/month Gemini subscription is truly all you need, then yeah, sure.... obviously a homelab setup is a ridiculous alternative on a pure cost basis. For most people doing "real" work with LLMs 40+ hours per week, a more apt comparison would be one or multiple $200/month subscriptions. At which point the break-even point of a homelab is much sooner.
However, most people running homelabs are doing it for other reasons. Independence, learning, and/or privacy issues.
having a 64GB mac mini m4 pro the last few years with some increasingly capable usefulness has kept me interested in this stuff in a way that using a paid platform wouldn't have. Similar to running K8s in a homelab, something about interacting with the hardware makes it more engaging/interesting, for me at least.
In general, I'm a big believer in doing more with fewer resources, within reason, and think having local setups really helps me be mindful with what's happening under the hood with these systems and managing context efficiently to get high quality results.
> There is no reason to believe that equivalent level model output will be more expensive in 12 months
It's almost never a drop-in replacement, and having to check and adjust integrations and workflows with new models gets old fast. My task was perfectly solved by the old model, I don't need a newer, "better" one - especially at higher prices ("more cost-effective" my foot). Local models lets one choose a model and freeze the downstream integrations forever, without being forced on the 6/8-month upgrade treadmill by aggressively short, scarcity-driven hosted model-deprecation schedules.
The big providers are losing on average tens of billions a year on these services, so yes prices must go up. Even Moore’s won’t help in the medium-term due to shortages and difficulty/reluctance to vastly increase capacity.
Surely you cannot possibly believe there is no reason.
Don't get me wrong: I hope you are right, and I am generally optimistic about the future of AI.
But do you really think, in a world filled with examples of big software companies repeatedly taking away or hamstringing capabilities we've taken for granted, that you can just count on a big tech company hosting cheap inference on incredibly powerful models forever? Surely we have learned by now that these companies do not exist to provide a public service to us, and the government cannot always be counted on to have the best interests of the citizens in mind.
I mean, how many times have we seen this in just the past decade or two?
- Consistent attempts to pass legislation weakening or banning the use of encryption
- Exorbitant Reddit API pricing (still salty about the death of the amazing Apollo app)
- Google fighting against sideloading on android
- US gov't issuing export control directive to suspend access to Fable/Mythos
- US lawmakers considering ways to regulate adoption of open weight models
- Chinese officials considering restricting overseas access to their most advanced models
I can absolutely see a much more restricted, closed down, and expensive future due to a combination of government regulations (regardless of which nation is doing it) and big companies rug-pulling as the check comes due on all the billions of dollars spent to get here.
This is such a tired argument and it seems to be parroted every single time someone talks about local models on hacker news.
Yes, of course the most economical path is to hand over all your data and become fully dependent on a cloud provider who is already operating as scale, hoping that they won't change/remove models, hamstring capabilities, or raise prices.
If this were a thread about hosting your own email or blog or cloud photos, you'd have plenty of people out here telling you how easy it is to do it yourself instead of relying on Gmail for email or WordPress/Medium/Substack for blogging, or iCloud for cloud photos.
And yet, without fail, every single thread about self hosting local models seems to have some copy/paste form of this cost-savings argument.
Where is the appreciation for this cool thing GP built? Where is the appreciation for the desire to figure out how to host your own version of the incredible capabilities that were not available merely a few years ago? And why, on this site of all places, would someone advocate trading all of the knowledge and independence gained from learning how to host something like this ourselves in favor of throwing it all over the wall to Google?
It's quite shocking to me how many experienced, tech-savvy people, who used to care about cookies and ad tracking - are now willingly sending their business strategies, highly confidential contracts, and intimate personal issues to a cloud provider because "it is only $0.0x per million tokens!".
> It's quite shocking to me how many experienced, tech-savvy people, who used to care about cookies and ad tracking - are now willingly sending their business strategies, highly confidential contracts, and intimate personal issues to a cloud provider because "it is only $0.0x per million tokens!".
Because there are more privacy guarantees there, depending on the provider. "But what if they violate their contract!" is some pretty tin-foil hat stuff.
How is this any different than a business running their website out of the cloud, assuming you are using a provider with appropriate contractual terms?
You can care about tracking and ads but still be comfortable storing your backups in the cloud, and many have been for quite awhile, even sometimes without encryption - that is totally different than e.g. Meta actively trying to track you and understand your relationship graph and your purchases etc.
I'm not sure about the tin-foil-hattedness of worrying about them violating their contract. But that's by-the-by. It is definitely not tin-foil-hat to worry about the data being taken in a breach.
> They are things that I would not be comfortable sending a cloud provider
It's also an old machine that the commenter already has; it's intellectually dishonest to compare it to the price of a brand new, 4-iteration-newer machine.
> 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.
There are a few use cases that are (somewhat) surprisingly unsuited for cloud providers:
- translations: cloud providers can bowdlerize (censor) bad words/content; also, if you want to do a translation for personal use of copyrighted materials, cloud providers may block it
- image generation: generating drawings with a style that even just resembles a copyrighted one (ie. Disney) may be blocked by cloud providers - for example, generating old cartoons style with GPT may not be possible.
What about a light but bulky AI job, like batch processing 50GB of files? I'm currently doing it on my used Macbook M1 Max 64gb, and it's chugging through it for the cost of electricity (free with my solar).
Cloud does not mean censored. You can rent gpu time and run whatever model you want, with your data kept as private as any other cloud instance you personally run. Cloud is location, with (for some work) wayyyy cheaper access.
I don't think it is really about "sensitive", but basically about any content you put in. Why would you give corporations your reasoning (data on how you interact with AI, how you "talk" etc.).
All of this is private, but not necessarily sensitive. You never know what is happening with this data. They might say they don't log it or don't sell it, then few years later you'll find it all online or read a book that has a story eerily similar to what you chatted about with GPT a year ago.
Although in this use case, it's likely because GPT guided you to write the same story as somebody else. Talking with an LLM about an idea is a great way to make it more predictable and homogenized. If you're fixing a bike or writing software, this is usually a good thing.
Gemini not long ago, when you said something "useful" said thank you, I will use it to help other users with similar problem. When asked "why would you do that, I thought our chat is private?" it would respond "Apologies. My mistake, of course this chat is private and your information will not be used." Funny.
> They also have enough power to negotiate contracts with strong privacy provisions.
What privacy provisions would you want to add to AWS? Most of the reasonable strong privacy provisions you'd want are already there and/or available if you want to sign up for it, even including US Govt Top Secret data if you meet some approval.
If they decide to ignore the contract and violate the privacy provisions, the US government can punish them, can the company from future contracts, throw executive in jail.
If they violate their privacy agreement with me there's effectively no punishment I can get that they will actually feel. There would have to be a class action law suit and they always just settle those for some small amount and admit to no wrong doing
I like to buy specific brand of soap. I don't want them to know that, it is my right and so is running local LLM "wasting" money on local inference to keep track of my stack of soap.
Those companies also have data sharing/use agreements that they can get from Cloud AI providers due to their size and spend. The secrecy and data protection is largely what they are paying for. Those types of agreements just aren’t available to individual customers. It’s only when you’re spending $$$ that it becomes worth it for the provider.
What supply crunch? Tons of RAM available for purchase. It's just expensive. That there is a "supply crunch" is made up to benefit from Trump administration not giving a shit how big corps operate
There's cloud hosts out there with unused compute. Wasted cycles are all over businesses running unused cloud apps and subscribed to services they don't use.
Still need a local computer to access the cloud; so a barely used gadget still exists. And this creates duplication of effort; we built RAM for servers AND the edge devices.
Seems redundant when tech nerds and corporations are really the only people that care.
And all that data in the web is meaningless yet we create a supply crunch storing it in servers.
This an out of touch nickel and dime perspective given the big picture to say nothing of the mess of strip mining and manufacturing pipelines that go into every screw, wire, and such
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.
>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.
> Why did I initially spend the extra $1600 if I knew ahead of time that it wasn't as good as cloud AI? Because I thought I could use some local LLM for the easy tasks or when I hit cloud rate limits.
The maths don't check. With Deepseek Flash one goes a very long way with 1600$ - even 10$/month, for easy jobs, are more than 13 years, and at a higher quality.
> AMD Strix Halo is about to release 192GB system which is a big upgrade to their current 128GB ai pc
Big upgrade to memory capacity but memory speed is only going up by a few percent, so its still going to be slow with more than a few B active params (I have one)
you can get 4xGB10 for <20K so that gets you about the same tg and pp will be probably better. Power consumption though will be something like 200W idle so that's a bummer. And you get VLLM and SGLANG unlike them mac where one has to use oMLX (nice but not the same concurrency or cache performance)
I have 128GB M5 Max laptop and sill prefer to run things on other boxes in basement because its no fun to have the primary device being overloaded.
It not only about it being an expensive setup (or not), and also other considerations:
- There's no guarantee of the $20/month service, and it likely has some limits compared to dedicated hardware token wise.
- Model are becoming more and more efficient, in many cases an M1 Max Mac Studio is still capable with 32 GB. 128 GB ram may not be the necessary baseline.
- Folks may think they want to only have a general model running locally (it's the comparable after all from the cloud providers), but we have to remember if the tasks we're trying to do ultimately are more specific than general and if there's space for the smaller models to do that.
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.
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.
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.
> 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
I'm curious about your setup. I've been tinkering with the idea of setting up Blender (cli use) in a container to allow agents to verify the scripts they are generating compile at a minimum. One thing I've found extremely helpful was generating a RAG of the current version of Blender.
For anyone wondering, I'm running Gemma4 26b A4B on a mini PC with 32 GB of DD4 and a Vega 7 iGPU (llama.cpp w/ Vulkan).
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.
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.
I have a RTX PRO 6000 96GB when the pricing was way better than now i also have a RTX 5090 too.
What I noticed is that (1) the great local models are optimized run inference (diffusion & LLMs) well on 32GB VRAM <= GPU's because that that's what the target has ...
(2) The quality of local models (esp. in diffusion) is increasing faster than the need for more VRAM - additional reason for the value of these FAST GPUs to increase!
(3) RTX PRO 6000 96GB is really great for fine tunes (ai-toolkit) :) but doesn't outperform my RTX 5090 with inference by anything significant on the good local models.
I have never run an AI job on a Mac, i also have doubts about performance and compatibilities - since the reviews almost never compare directly.
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.
I think your last point is exactly why I'm so interested in local models. The current landscape doesn't feel sustainable. The last few months we've seen the big providers (OpenAI, Anthropic) start to play with usage limits, resets, banked resets, pulling models, etc. I think local models are close to the point where, with a sufficiently well-architected harness, you can get results that are on par with the experience you'd have with cloud inference. It is nice to know that I have hardware under my desk that I control with open weight models that I can interact with on my terms.
It is certainly not sustainable but that is exactly why I want to use $400-$800 of resources a month for $20 while the deal last.
It is really a once in a lifetime deal.
Once the deal is over the local models will be better than what I am using now anyway and the hardware will be all the better than what I can get now for the price.
> local models are close to the point where, with a sufficiently well-architected harness, you can get results that are on par with the experience you'd have with cloud inference
In my opinion, 98% of the work most devs would send to an AI can be capably achieved with a local model and a frontier-level model is overkill.
The goalpost moving feeds right into Anthropic and OpenAI's interests.
> AI subscriptions are too highly subsidized right now
I've been running into annoying limits with Claude recently. It gives me like 5 questions over the course of 15 mins and then tells me to wait 5 hours. When companies can change things up to make the base subscription nearly useless (the last question always gets messed up, too), then you realize the value of owning your own infrastructure.
You're right that $200/mo is much cheaper than comparable infrastructure. OTOH, you don't get to have a computer that can also be used for other applications, or which works when the internet is down. Also, you remain tethered to whatever pricing the AI companies want to charge. If AI pricing goes like Uber/Lyft did when the VC cash ran out, then we'll be paying much more in a few years' time. We could look back and think "I wish I'd bought my own setup back in 2026" if it's going to be inevitable.
And all this before you get into privacy/security/compliance stuff.
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).
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.
How are you actually using the local model? I've played with Qwen 3.8 27b on ollama and the coding harnesses (Claude Code and OpenCode) seem to fail way more often then using the cloud models. And by fail, I mean the edits don't apply cleanly, it goes to add python code, but doesn't indent it properly, or the edit doesn't apply and so it tries again and again and eventually wipes out a different function then it "intended". It just gets really frustrating compared to the relative stability of Claude Cloud.
My use-case is only coding, every model sucks at writing good literature and there is no way around that (have had people try to debate me on this, but it's a taste thing, I have extensive English writing skills from my school years).
Prior to two weeks ago, I was just using Pi and Ollama.
I have tried my hand at putting together a few harnesses and I finally landed on what I like. Been working on this small app to handle running llama-server for me from any device that has the llama-cpp stack setup: https://github.com/SamInTheShell/loom
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.
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
I feel like for localAI t/s is less of an issue. Just make a PRD and run a ralph loop. For big slogging projects like reverse engineering, or converting a codebase to a new language it actually doesn't matter if it takes a day or seven days.
Yeah this is my experience. My 24GB 3090 + 64GB RAM takes a couple hours to crank out some code with largest Gemma 4 and Qwen3.8 models it can run
But in the meantime I get dishes done, vacuum, flip laundry... etc etc
Frontier models also seem in such a rush to emit anything they produce a mess that needs steering all day anyway
While I have not tested it, it feels like my local setup going slower is better at producing code that works the first time as its not trying to look fast for marketing sake
For the amount of tokens you get, based on your comment, ALL subscriptions are heavily subsidized, and the most expensive ones are the most subsidized.
For OpenAI and Anthropic, the $100 subscriptions cost 5x the $20 subscriptions and give you 5x the tokens. And the $200 subscriptions are 10x the cost for 20x the tokens. (Tokens cost 50% as much.)
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.
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.
In my case, I dev on macOS. The env the agent runs in is the same as my dev laptop, configured and in sync. Has access to all the same tools and environment as I would on my laptop.
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.
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.
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).
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’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.
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?
Usable certainly. But my impression is that useful models still need a bit more than 16GB. Something like Qwen 3.8 27B is useful but squeezing it into 16GB requires fairly aggressive quantisation which will make it unreliable (e.g it'll get stuck in loops) and won't leave enough space for a long context (which qwen 3.8 really likes)
I’m the parent of this thread, the person with the with the RX 9070.
My understanding would be that if you’re interested in this sort of card for AI that you should go with the AI PRO R9700, which is basically the professional version of the RX 9070XT but with 32GB of memory.
It’s significantly more money but not crazy like a 5090.
I just happen to have the 9070XT primarily for gaming purposes.
I’m not quite sure how to describe my experience using it other than “rudimentary,” and a lot of that is on me for not really understanding the best way to set it up.
If you have been using cloud hosted models, you will be severely disappointed with what you’d be able to run on 16GB VRAM. You will spend most of your time fighting with the model to fix its mistakes.
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...
> "not do significant work this at this late hour"
Is this Claude code? Or your local? I assume Claude? I'm more than a little staggered by this, like, it makes no sense! It doesn't even serve Anthropic's interests (surely better for them if it burns your token quota so you have to buy more the next morning.) The LLM just... decided? I'd be so mad.
> 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.
I think that's Anthropic trying to get you to not extract as much value out of that subsidized subscription as possible.
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.
What you’re describing is exactly what I’ve experienced in my time testing out this stuff locally, and I had a hard time figuring out what exactly to blame.
I keep hoping that one day some comment is going to paste a link to some kind of idiot-proof guide or piece of software that’s “90% as good as Claude but running local.”
And by 90% I don’t mean that the model is 90% as good or runs 90% as fast, more like all the other stuff you mentioned is set up out of the box.
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 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.
You have good enough hardware to run good models comparable with Gemini and ChatGPT.
What they are doing that you might not be is context caching, retaining a user profile about you in vector db, and adhering to strict output templates - often invoking multiple different prompts to assemble the end response to the user.
You’ll notice the mainstream ones always kinda answer in the same way. They are also able to mix code and images with text (multi-modality) because they are breaking down and routing your prompt to different models then assembling a result.
That’s all just software, has nothing to do with the model.
More people imo should be experimenting with it rather than chasing better models. This area is where all the differentiation is happening in the services, and what gives an AI provider like Gemini or Vidu or Singify an edge over running the equivalent locally
Ignore these shills in the replies. Models peaked a long time ago.
Yes there are smaller ones like mistral 7b vs larger ones which deliver only a slight improvement, but tbh most of the larger ones are worse than Mistral 7b.
The major providers like Google Gemini and ChatGPT and the “me too” company Anslopic are doing context caching (fact check me) and db retrieval to build responses.
Their responses adhere to templates and multiple different models are requested (one for the personalized description, one for the code solution, one for the image diagram, etc.) this is called multi-modality. Gemini and 4o stole most of their UX ideas from a project called Ragdoll (the first ever multimodal chat AI software released months before 4o).
You can make the same exact quality of AI chat service at home setting up harnessing like I described here namely the context caching and multi-modality. That is what differentiates an experience like Gemini from running local Ollama or Stable Diffusion or whatever where you are just getting a raw response from 1 model at a time.
I am running GLM 5.3 across 2x DGX Sparks and was doing comparisons and it absolutely can beat Gemini. Yesterday it corrected a poor Fable 5 response even
Yes they are quite good, but are not able to run on a 16GB RX 9070.
Quantized Qwen 3.8 Flash Next could maybe run eventually on that card with a highly optimized inference engine that dynamically caches the hottest layer experts. Even then you run into some hard limits.
The post was replying to someone with 16GB. (And also: no, even the best open weight models are not as good as what you can use on your ChatGPT subscription. They’ve gotten a lot better, but not that much better.)
It's fun to see that even an extremely large company can find unexpected product market fit [0]. Per this article, "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." That sounds insane in retrospect, but I think there's just inherent uncertainty in what people actually need and will use things for.
[0]https://pmarchive.com/guide_to_startups_part4.html: "In a great market—a market with lots of real potential customers—the market pulls product out of the startup... The product doesn’t need to be great; it just has to basically work."
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.
What year are you talking about? When I was in grad school, around 2007, Nvidia was aggressively marketing GPUs for high performance computing. They would go to campuses, talk to professors, etc.
Yes, the whole Deep Learning thing was luck, but as with most lucky things, they ensured they were positioned to capitalize on it.
to their credit, there was a lot of work behind "luck". Jensen showed up in person in 2017 in NEURIPS and he and likely a lot of his top brass basically sat down and read the entire conference proceedings/abstracts; there was likely a lot of work behind the scenes to behind the ML research pivot.
And 2017 was _late_ in their pivot. They'd been active for much, much longer. Last winter break I sat down to watch every GTC keynote, going back to 2009[1]. Even then, he's talking about expanding to non-graphics workloads. Google's GPU paper[2] just slotted naturally into their existing narrative and were happy to support it. "fortune favors the prepared" as they say.
Yeah, The NVIDIA Way goes into a lot of detail on how and why the pivot from graphics to AI happened. This is a prime example of “you make your own luck.” Jensen engineered an organization that was primed to recognize and pounce on the next big thing, and it ended up being AI. But they saw it coming WAY in advance (like 2011/2012, not 2017) because they were explicitly on the lookout.
Which makes it a Exempli gratia of "an organization that was primed to recognize and pounce on the next big thing." In addition, they also recognized early on some of the weaknesses of crypto-mining as an industry and limited their exposure while making a pivot to the next thing.
> "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.
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.
They were investing in ANE and Metal before everyone in consumer. Hardly asleep. They just underestimated the market size, as pretty much everyone did.
I think a reasonable story could have been told that goes like this: local models aren’t as good as frontier models with a $20/month subscription, and the hardware costs a lot. So only a few enthusiasts will buy Apple machines for this purpose.
This story turned out to be false but I think smart, reasonable people a couple years ago could have believed it with conviction. It doesn’t really seem like “completely asleep” to me.
I don’t understand how that’s possible. They should have had a better idea of what was happening in the memory markets than pretty much any other entity.
Their universal RAM strategy is so obviously helpful for AI. (1) GPU/NPU <--> CPU RAM copies eliminated. (2) All (most) RAM available for GPU/Neural, when local models are typically kneecapped by limited GPU RAM sizes vs. the much larger RAM options for M/Max/Pro/Ultras.
They have been taking NPU's seriously on their phones, tablets and laptops since the M1.
Then they enabled fully-connected RDMA for 4 x 512GB MacStudio's = 2TB RAM. Perfect for a large Mixture-of-Experts model.
It would be very strange if they didn't notice their product line had landed in a new sweet spot.
I am curious about the corporate disconnect from the frontline to the generals.
While the company I am in is embracing AI the disconnect and delay between what is available and possible versus what is approved and permitted is a three month window. The State employees I speak to are just now getting around to writing their usage policies for internal AI usage.
FWIW, the reported reason for OpenAI buying Macs has nothing to do with the memory by the sounds of it. Every single outlet I can find reporting on this seems to repeat that the intended use case is for agentic workloads and generating training data for reinforcement learning. They don't appear to be doing inference nor any sort of training AFAICT.
Tim Cook has been touted as the greatest supply chain logistics person on the planet and revolutionizing Apple's product delivery, securing exclusive contracts years in advance, etc., etc.
But "oops, we missed that people are interested in AI work on our machines" seems like a really fucking big myopia. But then again, Tim's off to retire on a bed made of cash this week, so...
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.
Mac Mini's were really nice HTPC candidates, too, before the AI boom. Like all things genuinely useful and affordable, they were snatched from the hands of normal consumers by a bunch of schmucks chasing the latest gold rush.
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.
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.
I've been using one for about a decade as a media server.
It just sits in the cabinet happily running the macOS TV program with the video files on an external hard drive. Playback on the TV is handled by the AppleTV's built-in Computer app. Works beautifully.
I have more movies and TV shows on that box than I could watch in my lifetime — a combination of ripped DVDs (Netflix, public library, and purchased) and OTA recordings.
When the cable goes out in my neighborhood (frequently), or a big storm screws up satellite reception (seasonally), I just don't care because I'm all localhost. As long as the lights stay on, everything is fine.
No ads. No privacy violation. No fees. No bandwidth congestion. No buffering. No subscription rate increases. All I pay for is electricity.
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.
Isn't OP complaining that the price went up because people want them for AI use? I guess it also went up because of cloud AI use increasing component costs, but those local models you want to run probably were trained in the cloud..
I bought a 5090 a year an a half ago for $2000. The same card, now a year and a half older, is $4000. Then there is the RAM - I bought 96GB, wishing it was 128, and now the price on my old RAM has doubled.
> Then there is the RAM - I bought 96GB, wishing it was 128, and now the price on my old RAM has doubled.
I also bought 96GB some while ago but after the initial increases, thinking I'll wait it out. Now 128GB is far more expensive than it was when I first looked. Luck has it I want DDR5 RDIMM as well, which seems the hardest hit when it comes to RAM prices, fun stuff.
> 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.
The base price of a mini has only gone up $200 from $699 in 2020 to $899 today, and for $699 you only got 8GB of RAM instead of 16. Yeah the price has gone up but not nearly as much as you seem to be remembering...
At this point it's basically like car models years. They rev them annually and make a few improvements but they're actually going longer between major redesigns. If they didn't bump the numbers every year people walking into stores would be worried about paying lots of money for an old phone that will soon be replaced by a much newer model.
Not just the high end stuff. The Neo is sold out until late September on the budget end, it seems like it is a smash for HS and college kids.
I hope Apple can take all this cash and do some stability releases like they used to do, bugs around things like Family Sharing, the painful "update" to Settings App, etc could all use a lot of love.
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).
> Apple's unusually timed announcement of new Mac mini and Mac Studio models this week was driven by unexpectedly strong enterprise appetite for AI hardware, according to The Information.
Maybe it's not anything specific to Apple? There's high demand and short supply elsewhere due to AI, so it doesn't seem all that odd that many companies would try to buy gear from Apple too.
Classic monopoly move: Control the user base, then control hardware. Any decent always-on local LLM setup with Apple devices will have to compete with these behemoths now. Great.
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.
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.
It's true, most people don't run models, but being the default platform for running open weights seems like it has plenty of advantages right now. Just like sales benefited from developers defaulting to MacOS for most open source languages like Ruby, Go, Rust, and TypeScript.
32GB of fast unified memory is enough for Qwen 3.8 27B.
- 16GB for the weights at Q4
- 9GB for the full 256K context at Q8
- 7GB spare for overhead and system.
The problem is that these Macs have 32GB of slow unified memory.
Edit: I'm thinking of a headless Mac mini, if you meant running it on the same machine you're using of course you'll need more memory, but LLMs are best served from a headless server so that's what I'd recommend.
> Edit: I'm thinking of a headless Mac mini, if you meant running it on the same machine you're using of course you'll need more memory, but LLMs are best served from a headless server so that's what I'd recommend.
What? LLMs are best served from a massive PD disaggregated cluster of B300s connected via NVLink.
If you're running LLMs on a Mac Mini, it's because you want to run local, not because it's the best setup.
You can, you just need a beefier PC, and it's more annoying in terms of noise and heat vs throwing something on your server closet. Plus you don't need to worry about other software stealing resources and whatnot.
> 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 are mistaken. I'm running Qwen 3.7 28B 4bit (MLX) with a 200k context window and everything total is 32GB RSS.
Is this the best? No. That's why I said the sweet spot. Getting from 16GB macs to 32GB is perhaps possible. Jumping to 64GB or 128GB as the default is simply unreasonable right now.
I have a similar machine, and briefly poked at running a local LLM, but got discouraged after a couple days. The quality, responsiveness, and impact on the rest of the system didn’t seem worth it to me.
What sorts of things are you doing with the local LLM? Anything interactive? Should I take another look?
Yes, 15-30 t/sec is pretty slow for local models so I recommend running local LLM tasks overnight where (vs paid plans) there isn't a risk of chewing through your token budget from a rogue loop or sub-agent. Even if it takes hours, you're sleeping anyway so no concern. herdr + pi works great for this but there are lots of harnesses.
I assume you mean Qwen 3.8-27B? Yes, you can run this in 32GB of RAM, but it's very context limited. With KV cache compression and other techniques, it's better now than in the past, but I'd still want more RAM, personally.
EDIT to add that you need to reserve 8GB for the system if you don't want to cause problems on macOS, which means 32GB RAM = 24GB max for model + context. It takes 18-19GB to load a 4-bit quant of Qwen3.8-27B, so I'd be really surprised if you can actually get a 200k context window. You need to fit within a 24GB WSS (which is generally a more constrained RSS) to get stable performance on 32GB RAM.
I run Qwen 3.8 27B just fine on my Mac mini M4 24GB. I use Unsloth's Q3 XXS with 128k context. It successfully completes long horizon tasks with OpenCode.
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.
So for people who don't understand, there are two markets for Apple hardware in this space:
1. Running an agent like OpenClaude. The $599 Mac Mini was an insanely good deal for this. I happened to buy a M5 Pro Mac Mini for $999 last year for other reasons. The equivalent is now almost $2000; and
2. Hardware for running inference on local models. This to me is the far more interesting market because Apple has a real opportunity to disrupt NVidia's stranglehold on the market.
With current architecture, the largest model you can reasonbly run is the amount of memory on the GPU and is a function of the quantization (eg int4, int8, fp8, fp16, etc) available and the number of parameters. NVidia aggressively segments the market. The most VRAM on a "consumer" card is 32GB on the 5090, which allows you to run ~31B parameter models.
In comparison, the RTX 6000 Pro has only slightly more CUDA units than a 5090 but has 80GB of VRAM. A few months ago they were $10-11k. Now they're ~$16k.
Macs use a shared memory architecture. Apple has previously sold Mac Studios with up to 512GB of RAM. Almost all of that memory can be used to hold much larger models without taking a penalty for interconnections between different GPUs or machines. Plus Apple interconnects between computers are actually relatively good by chaining TB5. It's still slow but it's about the best non-enterprise option available.
But the previous Mac Studios just didn't have the raw FLOPS and memory bandwidth. The M5 Ultras are up to 1.2TB/s of memory bandwidth. M3 Ultra had ~900GB/s. RTX 5090s and RTX 6000 Pros are 1.8TB/s. The current best HBM3 NVidia DC GPUs are at 3.2TB/s IIRC. But the M5 Ultra has a claimed ~4.5x the FLOPS of the M3 Ultra.
We don't have our hands on these yet but it probably means they are going to be much closer to a 5090. I expect ~50% of a 5090's inference speed. That may sound bad but it's actually really good because a 256/512GB Mac Studio can probably locally run the best Flash models. With NVidia hardware you'll need to spend many tens of thousands for that.
We'll see what the inference speed is but I expect it to be usable. DeepSeek v4 Flash, for example, will be entirely runnable. We're not at DeepSeek v4 Pro local yet.
> 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.
I hope Apple does not gain some exclusive enterprise tier for hardware. Part of what I love about them is that everything is available to consumers. A lowly home user can buy the exact same 256 (or 512) gigabytes of memory in a Mac from Apple, as long as they have a couple dozen thousand dollars to spare. I'd be really sad to lose that.
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.
At this point it’s a well known secret that Apple has real rack mount servers for their internal processes. They actually have officially released video of their servers in the WSJ report on their chip supply chain.
it’s a well known secret that Apple has real rack mount servers for their internal processes
Only if by "secret" you mean "announced in multiple press releases and a public event with federal, state, and local officials at its new sever factory in Houston."
Apple has to have significant build infrastructure to support internal iOS development, surely? They can't just be using whatever is at the developers' desk, or a big pile of Mac minis in a closet. That's far too pedestrian for Apple internal works.
Plus they did sell rackmount servers for some time.
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.
Were they really caught off guard or is it a long-term play by Apple who knows that consumers may just want machines capable of local models. Build-or-buy (subscribe) options. I for one would get a Mac Studio over a DGX Spark because you get a general Mac machine as a bonus. The big news is Apple being caught off guard by Nvidia buying Hugging Face. That should have been Apple's.
I imagine Apple could also grow their business in the EU by marketing to companies who want powerful AI features but can't leverage 3rd party AI services due to GDPR. If only they could come up with iOS, App Store, and developer policies that respect the sovereignty of the EU.
Now if only they hadn't discontinued the Mac Pro. Could be quite the AI machine with multiple compute GPUs at higher bandwidth than an external Thunderbolt enclosure.
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.
There are so many used M4 Mac Minis for sale on marketplace for $600+ now that the people I guess got bored of the local models and decided to see if they could make their money back as they probably bought them when they were $399 on sale earlier this year. I lowball them every time.
This may sound a little wacky, but one potential use case I'm considering is robotics. Say you want to use a fine-tuned mid-size VLM model right on the robot. You pretty much have to use the Jetson line, which has a great ecosystem and everything (depth cameras, lidar, SLAM, small segmentation models, etc) runs on it, but it's gets very expensive very fast if you want to run LLMs on it. The Jetson Thor lines are 3-5k depending on memory etc.
One very efficient option today is to have the cheapest Jetson (Orin Nano) run the classical robotics stack, then have a base mac mini run nothing but the VLM. The Mac mini is considerably cheaper and faster at these workloads than the mid-range Jetsons.
I think this wonky situation is because Apple us under immense consumer pressure to absorb the ridiculous memory prices, while the Jetson is aimed at "business" and much more likely to fluctuate with the market. Last year I bought a Jetson Orin Nano 8GB for $375CAD, today that official nVidia Amazon page is out of stock and other sellers have it listed for $900-$1100CAD. Absolutely bonkers pricing.
Apple has a huge opportunity here to lead the market for machines to run local models if they step into it. Their stuff is already better than what nVidia is offering with stuff like the DGX Spark.
It's a niche market but it's a market that overlaps heavily with professionals in the AI space and lead developers, so it's a market that gets them customers in those roles.
If I were running Apple I'd call the RAM price bubble for what it is and temporarily eat some margin to offer machines with more RAM than competitors, especially these models that are great for edge AI, and capture market share.
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 doesn't design GPUs on-par with Nvidia's efficiency yet
How much it matters in inference? Most GPUs have enough computing for that and the bottleneck is the RAM speed and size. And M5 Ultra is becoming to challenge this.
For prefill, it's basically all that matters. Long-horizon agent tasks, session compaction, file reads and context manipulation will all hit the compute bottleneck in regular usage, incurring several minutes of latency on most Apple Silicon chips, regardless of RAM.
It's kinda why memory bandwidth is an enormous red herring, even for datacenter applications. Nvidia's huge advantage is a compute-optimized GPU architecture and their Infiniband networking, their memory controllers aren't really the star of the show.
I am missing something in the article. From what I am reading, AI companies are so desperate for memory they are buying Apple Systems and other hardware and striping them for RAM and maybe other components.
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)
I'm training a model using reinforcement learning with self-play. I can and do use vast.ai when scaling but for experiments it's far faster, and cheaper, to run it locally until the bugs are all figured out. Just provisioning a new instance and copying the relevant checkpoints and things can take 25 minutes. It's zero locally.