51–60 of 203 posts
They're not the best choice for anyone who wants to run LLMs as fast and cheap as possible at home. Think of it like a developer tool.
These boxes are confusing the internet because they've let the marketing teams run wild (or at least the marketing LLMs run wild) trying to make them out to be something everyone should want.
If you touch the image when scrolling on mobile then it opens when you lift your finger. Then when you press the cross in the corner to close the image, the search button behind it is activated. How can a serious company not notice these glaring issues in their websites?
Its not that they dont notice.
They dont care.
Any good ideas for what these can be used for? I am still trying to think a use case that a Ryzen AI Max/MacBook or a plain gaming gpu cannot cover.
Last few jobs I've had were for binaries compiled to target ARM AArch64 SBC devices, and cross compiling was sometimes annoying, and you couldn't truly eat your own dogfood on workstations as there's subtle things around atomics and memory consistency guarantees that differ between ISAs.
Mac M series machines are an option except that then you're not running Linux, except in VM, and then that's awkward too. Or Asahi which comes with its own constraints.
Having a beefy ARM machine at my desk natively running Linux would have pleased me greatly. Especially if my employer was paying for it.
Earlier quoted context omitted.
Probably LLM slop, but also it's the same GB10 chip as the DGX Spark so why would the memory bandwidth be significantly different?
As far as I can tell these are all the same hardware just different enclosures. I'm not sure why Nvidia went this route given that they have a first party device. Usually you only see this when the original manufacturer doesn't want to be in the distribution or support game.
If you touch the image when scrolling on mobile then it opens when you lift your finger. Then when you press the cross in the corner to close the image, the search button behind it is activated. How can a serious company not notice these glaring issues in their websites?
But I'm not surprised, this is ASUS. As a company, they don't really seem to care about software quality.
GX10 vs MacBook Pro M4 Max: - Price: $3k / $5k - Memory: same (128GB) - Memory bandwidth: ~273GB/s / 546GB/sec - SSD: same (1 TB) - GPU advantage: ~5x-10x depending on memory bottleneck - Network: same 10Gbe (via TB) - Direct cluster: 200Gb / 80Gb - Portable: No / Yes - Free Mac included: No / Yes - Free monitor: No / Yes - Linux out of the box: Yes / No - CUDA Dev environment: Yes : No
Couldn't you buy a Mac Ultra with more memory for the same price?
This Asus box costs $3000, and the cheapest Mac Studio with the same amount of RAM costs $3500, or $3700 if you also match the SSD capacity. You do get about twice as much memory bandwidth out of the Mac though.
These AI boxes resemble gaming consoles in both form factor and architecture, makes me curious if they could make good gaming machines.
Plus ofc software stack for gaming on this isn’t available
I wonder why they even added this to the FAQ if they're gonna weasel their way around it and not answer properly? > 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 LL…
Probably LLM slop, but also it's the same GB10 chip as the DGX Spark so why would the memory bandwidth be significantly different?