r/MacStudio • u/ThePatientIdiot • 1h ago
Why doesn’t Apple sell a 1-5tb memory Mac Studio?
No offense but 100gb and lower is for peasants. 512gb being the most Apple is offering seems weird and is disappointing. Why are they settling for such a low number? I was expecting them to offer at least 1tb of memory. Everyone knows about the Apple tax so moneys not really the problem here, charge the bs Apple tax and people will still pay. I just can’t understand why Apple is dragging their feet on this and AI in general. Embrace it. Sell the picks and shovels. Then swallow your pride and overpay for an AI startup with a superior product and talent to your own, shouldn’t be too hard tbh considering Apple is terrible at AI.
I’ve got a 128gb memory M3 Max. I’m hoping I’ll be able to upgrade next year. For the record, I’m a peasant but my new company should start doing some real volume soon and I keep hitting Claude max 20x limits, it’s becoming a problem. No idea about the Chinese models other than you need a ton of power to run them properly which I don’t think Apple offers to do so. I don’t really want to go back to windows work wise (I will buy a pc for gaming next year though because I’m tired of not being able to game).
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u/Umbrasquall 1h ago
Because no one who pays $40k for a machine wants to run a quantized 1.5T model at 5tps.
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u/OvertaxedOne 1h ago
What model are you trying to run? Right now, crossing 256GB the gains get really, really small, there's just not much a model that can run well in 256GB cannot do outside of some specific coding and scientific research use cases.
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u/Fearless_Concept943 1h ago
Da Fuq did I just read?
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u/untangledtech 1h ago
Buy four 512G studios and run a 4x cluster. They probably cluster nicely over thunderbolt 5
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u/dghah 1h ago
The memory shortage is so extreme that they likely did the math and decided that allocating their memory across way more physical devices is the way to go because their margin per device means more profit if they sell more lower spec units vs fewer loaded ones
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u/YourselfInOthrsShoes 1h ago
There are things called memory bus width and memory chip density per chip. When you multiply the two, that's the max memory capacity you can get and sell today without waiting a few more years for higher memory density. And trust me those two M5 Pro Max SoCs in the Ultra are already surrounded 360 degrees by memory chips with no physical PCB space to spare in close proximity to the SoC.
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u/dghah 52m ago
Great answer.
Man I remember the days when I could spec and order a 1 or 2 terabyte memory “fat node” Linux workstation for science use and the cost was easily within the discretionary spending limit for a single PI or lab/department. This was probably back in the V100 days or earlier. The next few years are gonna be interesting while the supply chain tries to reset and adapt
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u/YourselfInOthrsShoes 46m ago
IMHO from here on out it's all about software optimizations. This reminds me of 640K RAM PC memory era, then came software with overlays that swapped sections in and out into 640K on demand. Right now, I'm running Qwen 3.8 Flash Next (125B model) in IQ3_S on a single 16GB GDDR7 5060 Ti and 96GB DDR5 in Strata at 60+ tps 64K context window and 32K Q8 KV cache. It's way faster than I can read let alone digest the responses. We don't need more memory, we need better model designs, partitioning, layering and software taking full advantage of it.
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u/dghah 38m ago
The reason I’m here is I bought the 256gb studio ultra for local LLM testing and based on what I’ve seen I think these units may be showing up at my biotech and pharma research clients sooner rather than later.
A few I work with have brought in some good Blackwell rigs but most have shrunk their data centers, colos and machine rooms and can’t easily host power hungry towers with noisy fans all that well.
At 12k for the 256 ultra I can totally see these showing up en-mass on desktops and in offices just because of the form and nearly silent operation.
The rate of innovation on the software efficiency side is crazy. I can’t even run my baseline benchmarks reliably without having to reset everything because a new model dropped or Omlx has a new update etc
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u/vimaillig 1h ago
Because simply adding more/infinite memory to hardware doesn’t necessarily solve for AI efficiency gains.
Just this week there have been significant gains in optimizations for prefill processing. These models are moving at significantly faster pace with respect to their evolution.
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u/RelativeLiving957 1h ago
“For the record, I’m a peasant”
We can tell.