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Nvidia DGX Spark: great hardware, early days for the ecosystem

simonwillison.net

21–30 of 116 posts

Re: Nvidia DGX Spark: great hardware, early days for the ecosystem

#21
post #17
post #14

The reported 119GB vs. 128GB according to spec is because 128GB (1e9 bytes) equals 119GiB (2^30 bytes).

That can't be right because RAM has always been reported in binary units. Only storage and networking use lame decimal units.

Looks like Claude reported it based on this:

  ● Bash(free -h)
    ⎿                 total        used        free      shared  buff/cache   available
       Mem:           119Gi       7.5Gi       100Gi        17Mi        12Gi       112Gi
       Swap:             0B          0B          0B
That 119Gi is indeed gibibytes, and 119Gi in GB is 128GB.

Re: Nvidia DGX Spark: great hardware, early days for the ecosystem

#22
post #16

This seems to be missing the obligatory pelican on a bicycle.

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.

Re: Nvidia DGX Spark: great hardware, early days for the ecosystem

#25

Is 128 GB of unified memory enough? I've found that the smaller models are great as a toy but useless for anything realistic. Will 128 GB hold any model that you can do actual work with or query for answers that returns useful information?

There are several 70B+ models that are genuinely useful these days.

I'm looking forward to GLM 4.6 Air - I expect that one should be pretty excellent, based on experiments with a quantized version of its predecessor on my Mac. https://simonwillison.net/2025/Jul/29/space-invaders/

Re: Nvidia DGX Spark: great hardware, early days for the ecosystem

#26
post #21
post #17

Earlier quoted context omitted.

That can't be right because RAM has always been reported in binary units. Only storage and networking use lame decimal units.

Looks like Claude reported it based on this: ● Bash(free -h) ⎿ total used free shared buff/cache available Mem: 119Gi 7.5Gi 100Gi 17Mi 12Gi 112Gi Swap: 0B 0B 0B That 119Gi is indeed gibibytes, and 119Gi in GB is 128GB.

[deleted]

Re: Nvidia DGX Spark: great hardware, early days for the ecosystem

#27
post #4
post #3

As is usual for NVidia: great hardware, an effing nightmare figuring out how to setup the pile of crap they call software.

And yet CUDA has looked way better than ATi/AMD offerings in the same area despite ATi/AMD technically being first to deliver GPGPU (major difference is that CUDA arrived year later but supported everything from G80 up, and nicely evolved, while AMD managed to have multiple platforms with patchy support and total rewrites in between)

What was the AMD GPGPU called?

Re: Nvidia DGX Spark: great hardware, early days for the ecosystem

#29

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

When the networking is 25GB/s and the memory bandwidth is 210GB/s you know something is seriously wrong.

Re: Nvidia DGX Spark: great hardware, early days for the ecosystem

#30
post #6

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…

I'm curious, does its architecture support all CUDA features out of the box or is it limited compared to 5090/6000 Blackwell?
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