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> x86 architecture for the rest of the machine. Can anyone explain this? Does this machine have multiple CPU architectures?
No, he means most NVIDIA-related software assumes a x86 CPU whereas this one is ARM.
Is that true? nvidia Jetson is quite mature now, and runs on ARM.
It's notable how much easier it is to get things working now that the embargo has lifted and other projects have shared their integrations. I'm running VLLM on it now and it was as simple as: docker run --gpus all -it --rm \ --ipc=host --ulimit memlock=-1 \ --ulimit stack=67108864 \ nvcr.io/nvidia/vllm:25.09-py3 (That recipe from https://catalog.ngc.nvidia.com/orgs/nvidia/containers/vllm?v... ) And then in the Docker…
Installation instructions: https://github.com/comfyanonymous/ComfyUI#nvidia
It's a webUI that'll let you try a bunch of different, super powerful things, including easily doing image and video generation in lots of different ways.
It was really useful to me when benching stuff at work on various gear. ie L4 vs A40 vs H100 vs 5th gen EPYC cpus, etc.
P.S. exploded view from the horse's mouth: https://www.nvidia.com/pt-br/products/workstations/dgx-spark...
The DGX Spark is completely overpriced for its performance compared to a single RTX 5090.
Earlier quoted context omitted.
Here's one I made with it - I didn't include it in the blog post because I had so many experiments running that I lost track of which model I'd used to create it! https://tools.simonwillison.net/svg-render#%3Csvg%20width%3D...
That seat post looks fairly unpleasant.
Earlier quoted context omitted.
Why Macbook Pro? Isn't Mac Studio is a lot cheaper and the right one to compare with DGX Spark?
I think the idea is that instead of spending an additional $4000 on external hardware, you can just buy one thing (your main work machine) and call it a day. Also, the Mac Studio isn’t that much cheaper at that price point.
In the list price, it's 1000 USD cheaper. 3,699 vs 4,699 I know a lot can be relative but that's a lot for me for sure.
TLDR: Just buy a RTX 5090. The DGX Spark is completely overpriced for its performance compared to a single RTX 5090.
I don't think the 5090 could do that with only 32G of VRAM, couldn't it ?
Whole thing feels like a paper launch being held up by people looking for blog traffic missing the point. I'd be pissed if I paid this much for hardware and the performance was this lacklustre while also being kneecapped for training
Obviously, even with connectx, it's only 240Gi of VRAM, so no big models can be trained.
How would this fare alongside the new Ryzen chips, ooi? From memory is seems to be getting the same amount of tok/s but would the Ryzen box be more useful for other computing, not just AI?
From reading reviews, dont have either yet: the nvidia actually has unified memory, AMD you have to specify the allocation split. Nvidia maybe has some form of gpu partitioning so you can run multiple smaller models but no one got it working yet. The Ryzen is very different from the pro gpus and the software support wont benefit from work done there, while nvidia is same. You can play games on Ryzen.
Beyond that, seems like the 395 in practice smashes the dgx spark in inference speeds for most models. I haven't seen nvfp4 comparisons yet and would be very interested to.
> even in a Docker container I should be allowed to do stupid things when I want. Give me an override!
A couple of people have since tipped me off that this works around that: IS_SANDBOX=0 claude --dangerously-skip-permissions You can run that as root and Claude won't complain.
(Because Docker doesn't do this as by default, best practice is to create a non root user in your dockerfile and run as that)
