This thing will sell out within a month

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.

Enthusiasts generally don't see a "return on investment" on their luxury cars or their boats either, but they enjoy what they're able to do with them and aren't exactly embarassed about the class signaling of being a high spender.

Add to that the smattering people with genuinely justifiable use cases, and you have more than enough demand for a limit-pushing, highest-margin model of your product platform.

I'd be willing to spend that much if it unlocked the ability to run top-of-the-line open weight models at their best quantization at a decent enough speed for personal agentic use. We're clearly not there yet, and I just can't imagine dropping the price of a brand new car on something that will only get me heavily quantized versions of the smaller models.

It is not exactly an investment, but I guess a lot depends on what you actually would want to do with it.

Those are corporate dollars making that spend, thats the only people.

Wait, where is Apple hyping the M7 Ultra?

The 256GB M5 Ultra Mac Studio can (AFAIK) run the latest open weight Flash models but only 2 bit quant. This by extension will be able to do 4 and that's a big difference. We're not at the point of running 1T+ parameter models locally yet but we'll be there by 2030 easily.

I personally believe that local LLMs have a huge captive market for people who cannot or should not host that data with a third-party. For example, it's an open question of whether or not using cloud LLMs by lawyers breaks attorney-client privilege. HIPAA with medical records is another one.

But I want to expound on one point that you made, which is really important. You say that this spec only has a SOTA life of ~1.5 years and that's an issue. Take that to its natural conclusion. Why isn't it an issue for hyperscalar data centers, particularly as NVidia wants to move to annual chip updates.

I believe the hardware is going to be commoditized. At that point the only thing that matters is performance-per-Watt. NVidia's R100/200 doesn't make the B100/200 obsolete but if the R series gets 50-100% PPW more and these DCs are using gigawatts of power, that's a massive difference.

What really is the effective life of AI hardware?

How many parameters are you planning to use for your doc editor? Email prose is optimal on a different model. Web parser? You might only be able to do one at a time with switching, which will work, but could be tiresome.

> Is this good enough for light web browsing, emails and word editing?

How many Chrome tabs do you have open?

And 2 electron apps.

Might even work as a basic Facebook machine.

As someone also considering getting one, what would make it worth it in your opinion?

Wish I had your economy for sure.

Release date? I know someone that has one right now.

I have one. It can run lots of smaller models really well. I’ve been loving Gemma4 especially. My needs more more large scale information extraction and not trying to replace Claude code locally though.

If anything the interesting models that can run in about 64gb of memory are increasing because most people can’t access more memory than that. I’m especially excited for clef and cleric as they continue to improve. Yeah, you don’t get 1mil token context length, but even frontier models can lose focus at that level

512gb being more expensive than just two 256g is a little strange. You mean you can get the same exact total cluster ram but double the compute/storage nodes and save $700?

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This is why people like to pick up the new Studios from the Apple Store in person - for that short ride back home their car is worth four times what it was on the way there.