[matthew-wegner]

For researchers, sure, but it's still quite an apples-to-oranges comparison.

A6000 is ~$5k per card. I guess you're referring to something like an A100 on that other spec, which is $10k/card (for 40GB of memory).

I do a fair bit of neural/AI art experimentation, where memory on the execution side is sometimes a limiting factor for me. I'm not training models, I'm not a hardcore researcher--those folks will absolutely be using NVIDIA's high-end stuff or TPU pods.

128GB in a Studio is super compelling if it means I can up-res some of my pieces without needing to use high-memory-but-super-slow CPU cloud VMs, or hope I get lucky with an A100 on Colab (or just pay for a GPU VM).

I have a 128GB/Ultra Studio in my office now. It's a great piece of kit, and a big reason I splurged on it--okay, maybe "excuse"--was that I expect it'll be useful for a lot of my side project workloads over the next couple of years...

[sudosysgen]

Hmm, that's interesting. What kind of inference workload requires more than the 48GB of memory you'd get from 2 3090s, for example? I'm genuinely curious because I haven't ran across them and it sounds interesting

[matthew-wegner]

Mostly it's old-school transfer style transfer! Well, "old" in the sense that it's pre-CLIP. I've played with CLIP-guided stuff too, but I've been tinkering with a custom style transfer workflow for a few years. The pipeline here is fractal IFS images (Chaotica/JWildfire) -> misc processing -> style transfer -> photo editing, basically.

Only the workflow is the custom part--the core here is literally the original jcjohnson implementation. Occasionally I look around at recent work in the area, but most seems focused on fast (video-speed) inference or pre-baked style models. I've never seen something that retains artistic flexibility.

My original gut feeling on style transfer was that it would be possible to mold it into a neat tool, but most people bumped into it, ran their profile photo against Starry Night, said "cool" and bounced off. And I get that--parameter tuning can be a sloooow process. When I really explore a series with a particular style I start to feed it custom content images made just for how it's reacting with various inputs.

Here's a piece that just finished a few minutes ago: https://mwegner.com/misc/styled_render-BMrHXWz_2RBaUq8pAYKfL...

That's from a local server in my garage with a K80. At some point I had two K80s in there (so basically four K40s with how they work), but dialed it back for power consumption/power reasons.

I do have a 3090 in the house, and a decent amount of cloud infra that I sometimes tap. The jcjohnson implementation is so far back that it doesn't even run against modern hardware. At some point I need to sort that out, or figure out how to wrangle a more modern implementation into behaving in the way that I like.

I don't really post these anywhere, although do throw them over the wall on Twitter if anyone is curious to see more. These are a mix of things, although the CLIP/Midjourney/etc stuff is pretty easy to spot: https://twitter.com/mwegner/media

[mkaic]

Not sure about inference but for training, 128GB is big enough to fit a decent-sized dataset entirely into memory, which causes a massive speedup. It's also probably cheaper to get a 128GB Mac Studio than a dual-3090 rig unless you're willing to build the rig yourself and pay the bare minimum for every component except the GPUs themselves.

As for 128GB memory on-inference models that a consumer would be interested in, I got nothing, though it certainly seems like it would be fun to mess around with haha

[visarga]

GPT-3 sized models need that kind of memory for inference

[sudosysgen]

GPT-3 is more like 300GB iirc