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by tony_cannistra8 hours ago |
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upvoteby bensyverson8 hours ago|
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Yes, I'm very glad they're doing this, but as of 2026, it's a bit academic. Pricing for the 512GB Mac Studio hasn't even been announced yet, but the 256GB version starts at $9499. So when you start talking about four of those puppies, it's substantial.

Someone else will do the math on how many years it would take to spend $40k on Muse Spark Open via OpenRouter.

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upvoteby mbreese7 hours ago|
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But there are definitely people out there that will need to run these models locally (contractual or legal requirements). By validating a Mac Studio as an option for running local models, it might be the more appealing option.

For most of us though, you're right -- the OpenRouter option is far more cost effective.

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upvoteby hirako20007 hours ago|
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It seems Apple figured they could sell more Mac Studios on the basis of frontier local AI models, profit to be made while users haven't figured out the math yet.
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upvoteby bensyverson7 hours ago|
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Don't get me wrong; in the long term I see the price of local inference falling as their chips continue to get 15-20% faster every year, and meanwhile the "intelligence per billion parameter" chart is rising. There will be a magic point where they cross, and "good enough" reaches your phone. That's when Apple starts to drink Nvidia's milkshake.
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upvoteby cyanydeez7 hours ago|
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Apple and NVIDIA prices are not going to fall.

Their competitors will. Anyone locked into these two ecosystems are willingly going along with it.

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upvoteby bensyverson6 hours ago|
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It’s not about the price of Apple products falling—I was commenting that the price of inference will continue to fall (chips getting faster). If “good enough” inference is in your pocket, this benefits Apple and harms Nvidia.
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upvoteby hirako200058 minutes ago|
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I got your insight as you intended, and I agree that good enough models able to run on commodity hardware will hurt Nvidia.

We aren't far from that point.

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upvoteby bitcurious7 hours ago|
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Puts into perspective how cheap the frontier models are via subs or even API.
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upvoteby mlsu7 hours ago|
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You can't really compare API prices though. The cards that they are running frontier models on in the datacenter can service hundreds of simultaneous requests and are at nearly 100% utilization. This thing would sit on your desk and be used to service a single user for a couple hours per day.
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upvoteby yndoendo3 minutes ago|
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It is the locally ran models that get me excited. They have an immutable user agreement and a focus on privacy.

Subscription / on-demand polices are a moving target, along with cost.

I foresee the ML market replicating the video streaming services market. Join us at a low rate and no advertisements. Six months latter, advertisements for everyone, unless you pay more $$. Now $$ will still show you ads. We are raising the rate to $$$ with adds ...

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upvoteby chaostheory5 hours ago|
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The main subsidy, from OpenAI, is ending 10/26. It’s still “cheap” for now compared to all the other non-open weight frontier models, but it won’t last since OpenAI’s stated goal is to make it so that there’s no much of a difference price wise between on demand API calls and subscriptions
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upvoteby LoganDark8 hours ago|
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There was the M3 Ultra 512GB Mac Studio, though.
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upvoteby mft_7 hours ago|
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…which is in the region of €20k on eBay, so the calculus doesn’t really shift…
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upvoteby LoganDark7 hours ago|
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It was only like $14k new. Why are you people acting like nobody has this stuff? People were already building clusters when that thing first came out. The research isn't bunk just because the refresh hasn't landed yet, my god.
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upvoteby mft_4 hours ago|
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Apple’s article isn’t targeting people who bought multiple M3 Ultras before the RAMpocalypse - those folks can be safely assumed to already be on board with running large models locally.
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upvoteby LoganDark2 hours ago|
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I thought the article was about showing this specific model can be run locally, not convincing people about running models locally in the first place.
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upvoteby unshavedyak7 hours ago|
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Maybe you (or i?) missed the point - it wasn't that nobody has those machines, it was that at $40k for 4x the 256GB versions is a substantial price, and so the parent parent was suggesting that it would be compared against OpenRouter. Ie how long would it take someone to spend $40k worth of credits on OpenRouter?

I've had the same thought on self hosting for the past 2 years. Eg i could invest $10k into hardware or spend those on credits. I chose credits, because by the time i spend 10k i suspect the market will have dramatically shifted in requirements, compute, etc.

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upvoteby nhecker5 hours ago|
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> I chose credits, because by the time i spend 10k i suspect the market will have dramatically shifted in requirements, compute, etc.

I agree, at least for inference credits. But anecdotal counterpoint: about ten years ago I put together a system using about $1600 worth of new and used components. Including electricity that TCO has been something like $14/month. I'd be hard pressed to find 128GB of RAM, 12 cores of x86_84, and 40 TiB (raw) worth of disk, and unlimited ingress/egress traffic for rent at that rate. That's what, $0.02 an hour, rounding up?

Yes, a hyperscaler's rent-a-computer would get faster and be more energy efficient. Possibly more carbon efficient over it's lifetime, although I have no way to quantify that. But in raw dollars it would have costed me absurdly more to rent. On the other hand, my machine has sat cold and unused for multiple months when I didn't need it to be running, and I don't consume all of it's resources around the clock. So maybe I could have gotten away with a lesser amount of local compute in a Raspberry Pi or similar, and an equally small cloud box for $5 a month or something. I dunno. I don't even own a laptop or desktop, and try to use 'cloud' services whenever practical. (Edit: always giving priority to privacy in those circumstances; only data I don't care about goes up.)

There are tradeoffs. I don't use my local server for LLM inference, it's far to inefficient for that; so I'll happily pay OpenRouter per token as needed. So I do think that hosted compute for rent does have a purpose. I guess all that's to say that I don't see it in black and white; there are times to invest in local hw and times to ride on the optimized bleeding edge of what other companies are willing to host in big datacenters.

This turned into a bit more of a ramble than I intended.

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upvoteby LoganDark5 hours ago|
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The substantial price is one reason I'm disappointed that open models are still getting bigger and bigger, rather than more efficient in any way.

I just also think that plenty of people buy clusters of Mac Studios anyway.

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upvoteby nhecker5 hours ago|
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Haven't smallish (16-32 GB VRAM) open-weights models been getting better and more capable over the last few years? Seems like what you can run on 'a laptop' today beats the pants off of whatever you could run on that same laptop two years ago.
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upvoteby LoganDark5 hours ago|
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I guess. I found that Gemini 3.1 Pro and 4 Argon are by far the best at what I like to use LLMs for. Even 3.8 Flash doesn't compare, and I'd imagine something I could run on my local hardware (M4 Max 128GB) would be just as disappointing as the rest, at least for now, even if better than earlier models. One of the reasons I've been hoping to see diffusion language models eventually.
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