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Apple caught off guard by AI demand for Mac Mini and Mac Studio

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Re: Apple caught off guard by AI demand for Mac Mini and Mac Studio

#641

Earlier quoted context omitted.

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).

64gb unified memory is incredibly powerful for a local machine. Most of us have 16GB which is almost useless compared to the API models.

Right now it's about €1650 for that machine (used, on ebay obviously)

Re: Apple caught off guard by AI demand for Mac Mini and Mac Studio

#642
post #532

Earlier quoted context omitted.

Yeah, through multiple layers of approvals. Not shtposting on hacker news.

Shit posting looks like natural use of social media so it's actually preferred https://www.forbes.com/sites/jaymcgregor/2017/02/20/reddit-i... “I have worked over 100 of these kinds of campaigns and never had it come back on the client. I’ve been doing viral marketing and reputation management since 2005. =In the past year I’ve worked for a major entertainment network to magnify a rumor within sports entertainment, a…

If apple indeed use shit posting, there has to be several layers of subcontractors in between for plausible deniability. Apple's own comms guidelines strictly prohibits pay-per-review, manipulative, "shit posting" style practices.

Re: Apple caught off guard by AI demand for Mac Mini and Mac Studio

#643

Earlier quoted context omitted.

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 s…

try ninfer once you get your 5090 https://github.com/Neroued/ninfer

Just wanted to share, I had a SaaS AI drop me a script to bench ninfer against llama-cpp and it is impressive. The place that it's doing better at than llama-cpp seems to really be late in the context window.

Initial results boiled down as follows.

# lmstudio-community/qwen3.8-27b@q4_k_m decode falloff 104.3 tok/s @ 12,683 -> 55.8 tok/s @ 240,755 (53% retained) prefill falloff 3,274 tok/s -> 1,059 tok/s (32% retained)

# qwen3_8_27b_nvfp4.ninfer decode falloff 173.3 tok/s @ 11,867 -> 139.3 tok/s @ 225,710 (80% retained) prefill falloff 8,726 tok/s -> 2,816 tok/s (32% retained)

I should still have room for more performance on the table. I've not even touched the overclock settings on the GPU.

This is a really cool project, I'm going to have to get into what those 3 guys are doing... assuming it can be done with what I got.

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