Comment by tristanj
10 hours ago
Holy price increase, in 2025 the equivalent M3 Ultra (32-core CPU / 80-core GPU with 512GB of RAM) cost $9,499.
Now the equivalent M5 Ultra (36-core CPU / 80-core GPU with 512GB of RAM) is priced at $22,299.
10 hours ago
Holy price increase, in 2025 the equivalent M3 Ultra (32-core CPU / 80-core GPU with 512GB of RAM) cost $9,499.
Now the equivalent M5 Ultra (36-core CPU / 80-core GPU with 512GB of RAM) is priced at $22,299.
This thing will sell out within a month
Which means nothing. Apple is extremely good at calculating how much inventory they should produce. They’re not going to make more than they’ll sell and have them sit on shelves. They’re also not going to cut into their profit margin too much by paying their way out of the memory shortage.
If a new premiere high-margin product sells out "within a month" that means there's a disruption in sales and an opportunity for competitors to meet the demand they just generated while manufacturing/distribution tries to catch up.
It would absolutely represent either a mis-estimation of demand or compromise to some supply/manufacturing bottleneck that they couldn't overcome without sending their release calendar into chaos.
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> Apple is extremely good at calculating how much inventory they should produce.
That doesn't really matter though for a physical product, unless you are implying they are 1) selling it at a loss or 2) not attempting to maximize profits from the product. If they sell out, it means they hit the target profit from the sale of the product.
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The 256 GB is backordered by 13–15 weeks, if you click through to that.
What would be the use case for this over a dedicated 8xgpu server?
Getting an equivalent amount of ram in GPU's is substantially more expensive than this. A single RTX 6000 Blackwell with 96gb of ram is like $16,000 now. Someone please correct me if I'm wrong but essentially with the Mac Studio you're getting the ability to run larger models, at slower speeds, for a lot cheaper.
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Large mixture-of-experts models, like Muse Spark or Kimi K3.
MoE models want a different ratio of memory/compute/$ than dense models like a Gemma4 31B or Qwen3.8 27B.
Since only some of the experts are active each round, you don’t need as much compute to get good tok/sec, but you do need a lot of memory to avoid streaming weights from SSD or system RAM.
You could run a full Muse Spark on one of these for $24k at probably around 50 tok/sec.
You’d need 5x RTX 6000 Pros at $80k for Muse Spark (albeit at much higher tok/sec).
On a $24k budget, you’re looking at a maximum of 96GB from Nvidia or 384GB from Intel or AMD. So you’re bottlenecked by PCIe bandwidth, and all those TFLOPS go to waste.
Other configurations of the Studio have similar logic for other MoE models. For instance, Qwen3.8 flash-next runs at a very acceptable 75 tok/sec on a 128GB M5 Max for about 1/3rd the price of an RTX 6000 or 40% less than 4x B70’s.
Data centers outfitted with Ikea furniture instead of the usual 19" racks.
1.3TB/s of Unified Memory. I'm not sure today how much distributing dense models across GPUs is ok or bad these dats but you don't have to think about it by having all the memory pool available on the one GPU you have.
Makes me feel like we're back in the era of SGI workstations in the late 20th century! I got to play with a few of those in college.
still cheap compared to those things. lab down the hall in grad school had an onyx [1] with the fancy polarized shutter 3d glasses for playing what we called "chem tv" or semi-empirical small molecule docking experiments - that was something like $250k or so in the early 90s.
[1] https://en.wikipedia.org/wiki/SGI_Onyx
Yes but they were heavy.
They were built like a brick shithouse and felt expensive. You always knew they belonged to someone else.
In other words you couldn't mistake them for an $800 Mac Mini.
Now try imagining a SGI workstation with 512GB of SOLDERED DOWN RAM. You can't because no one would have bought them.
To be fair, these are significantly larger than a Mac Mini. More like one of the old Macintosh Quadras or Performas.
But your main point still stands.
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