Mac Studio 256gb unified RAM worth it for MiniMax 2.5 and Qwen3.5?

Posted by Apart_Paramedic_7767@reddit | LocalLLaMA | View on Reddit | 25 comments

For a while now I’ve been itching for ‘ChatGPT at home’ because I process a lot of documents and information that is private.

With EDU pricing I can get a Mac Studio for $7000. According to Unsloth, “Run Unsloth dynamic 4-bit MXFP4 on 256GB Mac / RAM device for 20+ tokens/s”

With access to Google search for grounding its answers I think local models have finally reached the point of usable for most things that I’d use Chat for.

What do you guys think?

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datbackup@reddit

I have answered the “is mac worth it” question a number of times, tldr is, with mac you get smarter but slower responses (and the longer, the slower), with PC/discrete gpu you get dumber but faster responses. The AI specialized boxes like dgx are somewhere in between.

If you want both smart and fast you have to pay way more, like $20k would be the barebones, $40k to really start to compete, but it still won’t give you opus level, although that might change this year.

Mac can give you sota chat experience but if you try using it for agentic coding expect to start waiting an hour for it to finish a single response. This is completely inviable imo because coding agents screw up often and that hour will have been just electricity and time down the drain.

I always say if you’re going to get a mac get the 512gb, that way when you’re waiting you at least get the smartest answer (by running unsloth quant of huge sota models, or running full precision of smaller models)

The one huge advantage mac has is the setup and heat management is all done for you, and this is not a small task, read about people trying to cool their big ram/multi gpu rigs, not to mention noise. Power consumption is not the black and white victory in favor of mac that some people make it out to be, because the mac will be drawing power continuously for the long time it’s processing.

I think the bottom line is that owning a big unified ram mac now puts you at the baseline for where we all should have been if google hadn’t decided to purposefully lower its search result quality sometime in the late 2010s. If you haven’t read about that look it up, it’s truly disgusting

It’s weird but i think it’s actually true… owning a sota local ai setup in 2026 is basically giving you the analogously powerful functionality that we all had circa 2015 before google search became a dumpster fire

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PhoynixStriker@reddit

Can you explain how models run on GPU giver dumber responses?

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datbackup@reddit

Don’t misunderstand, my tldr is referring not to absolutes but only to “on average” because the average gpu owner with one or two 3090s (actually that’s probably way above average right?) just can’t run the bigger models which are what gets you the smart responses

Obviously if you have 10x 3090 then yeah you can run minimax 2.1 entirely in VRAM and your answers will be pretty near sota, so in this case gpus are clearly not dumber, the problem is this is an extreme edge case

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sdexca@reddit

I mean I have been thinking about this, 3090s aren't that expensive where I live, so getting 10x 3090s doesn't even sound that unrealistic lmao

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1-800-methdyke@reddit

Please unpack: how does having a local LLM get you pre-enshitifaction Google?

I haven’t Googled anything since 2014. Was on Bing for a decade and then Perplexity.

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Apart_Paramedic_7767@reddit (OP)

Really dude.. bing? I’d rather walk from one city to another collecting information like a hadith collector.

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1-800-methdyke@reddit

Microsoft gets 4% of revenue from ads, with Google is 74%. Pushing ads in the search engine wasn’t a big priority for them.

Also, search results tended to be better because SEO was targeting Google’s algorithm for rankings, so Bing’ too results were more organic. This became more apparent as Google got worse.

DuckDuckGo, Yahoo and AOL are powered by Bing on the backend. Apple used Bing for Siri and iOS search results until 2017 when Google started paying them billions to be the provider.

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datbackup@reddit

The 80% explanation is because the llms are trained on mass scrapes of the web plus they don’t (yet) have ads baked in

As for the remaining 20%, I mean i used the word “analogously” for a good reason, obviously an llm is not a search engine, but i think if you have tool calling working, and a decent rag / mcp search setup yes the llm can reasonably replace a search engine, and the search engines all seem to agree with me as you can see they are including llm generated text at the top of their results

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Apart_Paramedic_7767@reddit (OP)

Wow. I wonder how long it will be until we have MiniMax2.5/GLM5 or Opus 4.5 quality on hardware like the 3090 and not 40k setups. And I believe you, Google is truly one evil company just like ‘Meta’.

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datbackup@reddit

On a single 3090? I don’t know, maybe 2 or three years, but it would probably be a domain expert (not ‘coding’ but ‘coding databases in c#’ or even smaller domain)

I do feel that we see opus 4.5 level fitting in 192GB by end of this year (2026)

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PrinceOfLeon@reddit

Run the model you want through a hosting service with the target configuration for a week or two and make sure it will do what you want before spending that kind of money to find out.

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Tough-Survey-2155@reddit

You don't need a fancy machine to build a rag/chatgpt for your private documents. Encoders require much less compute, build your search first. You can use a quantized version of 8b model from your machine for generation. Retrieval requires encoder models, usually, unless you want to add query rewriting + breakdown + routing (highly recommend using an SLM for that)

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Inevitable-Jury-6271@reddit

If your goal is “private ChatGPT at home,” 256GB can be worth it — but only if your workload actually needs very large models and long context.

Before spending $7k, run this decision test:

1) Pick 10 real tasks (your actual docs/workflow). 2) Measure for each setup: TTFT, tokens/s, answer quality, and total time-to-final-answer. 3) Include one cheaper baseline (smaller local model + retrieval) and one cloud baseline.

In practice, many document-heavy workflows are bottlenecked by retrieval/chunking quality, not raw model size. If that’s your case, better indexing + disciplined prompting often beats buying the biggest box.

But if you consistently need giant context windows and low-latency local inference with no cloud fallback, then yes — 256GB is one of the few “single machine” options that makes that realistic.

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Consistent_Wash_276@reddit

Yes as someone who owns the exact model you’re referring to I would say “NO” and here’s the details why.

If you’re spending $5,000 + on a device it better be making you money in the end or at least saving you money. Assuming it’s meant to save you you money replacing a subscription?
I’m testing the MiniMax 2.5 186 gb model and it’s pretty f’n great actually, but that’s one model being ran at 40 tokens per second at a time. Nothing in parallel and 40 tokens per second is very solid but there’s faster options to utilize.

I’m would look at that device in the way of having, lets say a chatting UI running gpt-oss:120b , VS Code running glm-4.7-flash, and Opencode running another model in parallel with some agentic coding. Just constantly working through workflows and abusing tokens per second is where you get the real value.

If you’re just chatting with models I would suggest a 32gb Mac mini or studio or just a Claude pro account or some kind of commercial account. Not enough to justify the investment.
Also, why $7,000? I got the same model with 2 tbs of storage for $5,400 from Microcenter.

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Cronus_k98@reddit

$7k gets you the 80 core GPU, $5k gets you the 60 core.

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No_Conversation9561@reddit

surprisingly the difference between 80 core GPU and 60 core GPU is minimal in prefill and almost not existent in generation

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Apart_Paramedic_7767@reddit (OP)

I think just because i’m talking about CAD not USD

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Consistent_Wash_276@reddit

Ahhh. And let’s hope by 2028 we both still use the same currency as we do that represents each nation.

🇺🇸 🫡 🇨🇦

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Cronus_k98@reddit

We need some more details. How are you processing the documents? Rag ingestion, summarization, or upload for Q&A? Are you waiting for the files to process or can you batch them and let them process overnight? How large are the documents?

You don’t necessarily need a large model to process documents, I’m using Qwen3 VL 4b to read/OCR documents and GPT OSS 20b to extract info. That’s able to process a hundred 1-50 page documents an hour on a 5090.

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