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Asus Ascent GX10

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Re: Asus Ascent GX10

#141

Post by sirlancer »

npalli wrote: post #8

Seems this is basically DGX Spark with 1TB of disk so about $1000 bucks cheaper. DGX Spark has not been received well (at least online, Carmack saying it runs at half the spec, low memory bandwidth etc.) so perhaps this is way to reduce buyers regret, you are out only $3000 and not $4000 (with DGX Spark).

Except Carmack, as much as I hate to say it, was simply wrong. If you run the GPU at full throttle then you get the power draw that he reported. However, if you run the CPU AND the GPU at full throttle, then you can draw all the power that’s available.

Re: Asus Ascent GX10

#142

Post by MangoToupe »

bigyabai wrote: post #114
MangoToupe wrote: post #93

Earlier quoted context omitted.

Presumably the second point is irrelevant if you're choosing among devices with unified memory.

It is not. Unified memory is not a panacea, it says nothing about the compute performance of the hardware. The Spark's GPU gets ~4x the FP16 compute performance of an M3 Ultra GPU on less than half the Mac Studio's total TDP.

right, but that doesn't describe a "high end consumer CUDA device". Nothing under that description has unified memory.

Re: Asus Ascent GX10

#143

Post by blmarket »

aseipp wrote: post #84
joelthelion wrote: post #20

"Nvidia dgx os", ugh. It would be a lot more enticing if that thing could run stock Linux.

It's just Ubuntu with precanned Nvidia software, otherwise it's a "normal" UEFI + ACPI booting machine, just like any x86 desktop. People have already installed NixOS and Fedora 43, and you can even go ahead and then install CUDA and it will work, too. (You might be able to forgo the nvidia modules and run upstream Mesa+NVK, even.) It's very different from Jetson and much more like a normal x86 desktop. The kernel is…

Wait, x86? you mean arm64?

Re: Asus Ascent GX10

#144

Post by aseipp »

BoredPositron wrote: post #116
aseipp wrote: post #84

Earlier quoted context omitted.

It's just Ubuntu with precanned Nvidia software, otherwise it's a "normal" UEFI + ACPI booting machine, just like any x86 desktop. People have already installed NixOS and Fedora 43, and you can even go ahead and then install CUDA and it will work, too. (You might be able to forgo the nvidia modules and run upstream Mesa+NVK, even.) It's very different from Jetson and much more like a normal x86 desktop. The kernel is…

I got burned more than once with Nvidia not providing kernel updates straight after release...

That was also my experience with their Jetson series [1], but my understanding is that these DGX kernels are not maintained by Nvidia but by Canonical, so they operate directly out of their package repos and on Canonicals' release and support schedule (e.g. 24.04 supported until 2029.) You can already get 6.14 from the package repos, and 6.17 can be built from source and is regularly updated if you follow the Git repositories. It's also not like the system is unusable without patches, and I suspect most will go upstream.

Based on my experience it feels quite different and much closer to a normal x86 machine, probably intentional. Maybe it helped that Nvidia did not design the full CPU complex, Mediatek did that.

[1] They even claim that Thor is now fully SBSA compliant (Xavier had UEFI, Orin had better UEFI, and now this) -- which would imply it has full UEFI + ACPI like the Spark. But when I looked at the kernel in their Thor L4T release, it looked like it was still loaded with Jetson-specific SOC drivers on top of a heavy fork of the PREEMPT_RT patch series for Linux 6.8; I did not look too hard, but it still didn't seem ideal. Maybe you can probably boot a "normal" OS missing most of the actual Jetson-specific peripherals, I guess.

Re: Asus Ascent GX10

#145

Post by aseipp »

blmarket wrote: post #143
aseipp wrote: post #84

Earlier quoted context omitted.

It's just Ubuntu with precanned Nvidia software, otherwise it's a "normal" UEFI + ACPI booting machine, just like any x86 desktop. People have already installed NixOS and Fedora 43, and you can even go ahead and then install CUDA and it will work, too. (You might be able to forgo the nvidia modules and run upstream Mesa+NVK, even.) It's very different from Jetson and much more like a normal x86 desktop. The kernel is…

Wait, x86? you mean arm64?

It's a bit ambiguous but I can't edit now, sorry. What I meant to say was that it boots using the same mechanism as x86 machines that you are familiar with, not that it is an x86 machine itself.

Re: Asus Ascent GX10

#146

Post by tgma »

WhitneyLand wrote: post #117
tgma wrote: post #95

Earlier quoted context omitted.

You should not be using an adapter to get IP over Thunderbolt. Just connect a Thunderbolt5 cable to both machines.

For point to point sure, but if you want to connect multiple machines in an actual fabric you’ll need some kind of network interop. The Asus clustering speed is not limited to p2p.

Fair enough. On the other hand you have more thunderbolts to make up a clique mesh of seven point to point Macs.

Re: Asus Ascent GX10

#147

Post by hasperdi »

WhitneyLand wrote: post #46

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

AMD 395+ is more bang for the buck IMO.

GMKtec EVO-X2 vs GX10 vs MacBook Pro M4 Max

  Price:  $2,199.99 / $3,000 / $5,000
  CPU:  Ryzen AI Max 395+ (Strix Halo, 16C/32T) / NVIDIA Grace Blackwell GB200 Superchip (20-core ARM v9.2) / Apple M4 Max (12C)
  GPU:  Radeon 890M (RDNA3 iGPU) / Integrated Blackwell GPU (up to 1 PFLOP FP4) / 40-core integrated GPU
  Memory:  128GB LPDDR5X / 128GB LPDDR5X unified / 128GB unified
  Memory bandwidth:  ???GB/s / ~500GB/s / ~546GB/s
  SSD:  1TB PCIe 4.0 / 4TB PCIe 5.0 / 1TB NVMe
  GPU advantage:  Similar (EVO-X2 trades blows with GB10 depending on model and framework)
  Network:  2.5GbE / 10GbE / 10GbE (via TB)
  Direct cluster:  40Gb (USB4/TB4) / 200Gb / 80Gb
  Portable:  Semi (compact desktop) / No / Yes
  Free Mac included:  No / No / Yes
  Free monitor:  No / No / Yes
  Linux out of the box:  Yes / Yes / No
  CUDA dev environment:  No (ROCm) / Yes / No

Re: Asus Ascent GX10

#148

Post by npalli »

jandrese wrote: post #80
simlevesque wrote: post #24

Earlier quoted context omitted.

Simon Willison seems to like his: https://til.simonwillison.net/llms/codex-spark-gpt-oss

Performance wise it was able to spit out about half of a buggy version of Space Invaders as a single HTML file in roughly a minute.

I think this is the key, it can do impressive stuff but it won't be fast. For that, you have to put in a NVidia data center / AI Factory.

Re: Asus Ascent GX10

#150

Post by mindcrash »

canucker2016 wrote: post #139
mindcrash wrote: post #129

ServeTheHome has already benchmarked the DGX Spark architecture against the (very obvious) Ryzen AI Max 395+ with 128G RAM: https://www.servethehome.com/nvidia-dgx-spark-review-the-gb1... If (and in case of Nvidia that's a big if at the moment) they get their software straight on Linux for once this piece of hardware seems to be something to keep an eye on.

GMKtec, maker of the EVO-X2 mini-PC that uses a Ryzen AI Max 395+, posted a blog post with a comparison between the DGX Spark and their EVO-X2 miniPC. from https://www.gmktec.com/blog/evo-x2-vs-nvidia-dgx-spark-redef... (text taken from https://wccftech.com/forget-nvidia-dgx-spark-amd-strix-halo-... since the GMKtec table was an image, but wccftech converted to an HTML table - EDIT-reformatted to make table look nice…

And additionally Framework apparently benchmarked GPT-OSS 120B (!) on the maxed out 395+ Desktop and reached a 38.0 tok/sec Generation Speed. Given that Nvidia can't even keep up on a 20B model, I assume they can't keep up on the 120B model aswell.

https://frame.work/nl/en/desktop?tab=machine-learning

So to me the only thing which seems to be interesting about the Spark atm is the ability to daisy link several units together so you can create a InfiniBand-ish network at InfiniBand speeds of Sparks.

But overall for just plain development and experimentation, and since I don't work at Big AI, I'm pretty sure I would not purchase Nvidia at the moment.

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