171–180 of 203 posts
I ordered one that arrived last week. It seems like a great idea with horrible execution. The UI shows strange glitchy/artifacts occasionally as if there's a hardware failure. To get a sense for use cases, see the playbooks on this website: https://build.nvidia.com/spark . Regarding limited memory bandwidth: my impression is that this is part of the onramp for the DGX Cloud. Heavy lifting/production workloads will st…
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I’m pretty sure I could spit out something that doesn’t work in half a minute.
Don't undersell it. The game is playable in a browser. The graphics are just blocks, the aliens don't return fire. There are no bunkers. The aliens change colors when they descend to a new level (whoops). But for less than 60 seconds of effort it does include the aliens (who do properly go all the way to the edges, so the strategy of shooting the sides off of the formation still works--not every implementation gets t…
That is how Space Invaders originally worked, used strips of colored cellophane to give the B&W graphics color and the aliens moved behind a different colored strip on each level down. So, maybe not an whoops?
Edit: After some reading, I guess it was the second release of Space Invaders which had the aliens change color as they dropped, first version only used the cellophane for a couple parts of the screen.
From the FAQ… doesn’t seem promising when they ask and then evade a crucial question. > What is the memory bandwidth supported by Ascent GX10? AI applications often require a bigger memory. With the NVIDIA Blackwell GPU that supports 128GB of unified memory, ASUS Ascent GX10 is an AI supercomputer that enables faster training, better real-time inference, and support larger models like LLMs.
They seem to have another FAQ here that gives a real answer (273GB/s): https://www.asus.com/us/support/faq/1056142/
File this one in the blue folder like the DGX
These AI boxes resemble gaming consoles in both form factor and architecture, makes me curious if they could make good gaming machines.
But if gaming is what you're actually interested in, then it's a pretty terrible buy. You can get a much cheaper x86-based system with a discrete GPU that runs circles around this.
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They seem to have another FAQ here that gives a real answer (273GB/s): https://www.asus.com/us/support/faq/1056142/
Now we can see why they avoided giving a straight answer. File this one in the blue folder like the DGX
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Now we can see why they avoided giving a straight answer. File this one in the blue folder like the DGX
Noob here. Why is that number bad?
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What's the cheapest way to get the same memory and memory bandwidth as a Mac Studio but also CUDA support?
CUDA is only on nvidia GPUs, I guess a RTX Pro 6000 would get you close, two of them are 192GB in total. Vastly increased memory bandwidth too. Maybe two/four of the older A100/A6000 could do the trick too.
At least the m5 ultra should finally balance things given the significant improvements to prompt processing in the m5 from what we've seen. Apple has had significantly higher memory bandwidth since the m1 series approaching 5 years old now. Surely an nvidia machine like this could have at bare minimum 500Gb+ if they cared in the slightest about competition.
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Now we can see why they avoided giving a straight answer. File this one in the blue folder like the DGX
Noob here. Why is that number bad?
There's newer models called "Mixture of Experts" that are, say, 120b parameters, but only use 5b parameters per token (the specific parameters are chosen via a much smaller routing model). That is the kind of model that excels on this machine. Unfortunately again, those models work really well when doing hybrid inference, because the GPU can handle the small-but-computationally-complex fully connected layers while the CPU can handle the large-but-computationally-easy expert layers.
This product doesn't really have a niche for inference. For training and prototyping is another story, but I'm a noob on those topics.