Nvidia DGX Spark: When Benchmark Numbers Meet Production Reality
44 comments
·October 26, 2025RyeCatcher
I absolutely love it. I’ve been up for days playing with it. But there are some bleeding edge issues. I tried to write a balanced article. I would highly recommend for people that love to get their hands dirty. Blows away any consumer GPU.
furyofantares
Since the text is obviously LLM output, how much prompting and editing went into this post? Did you have to correct anything that you put into it that it then got wrong or added incorrect output to?
enum
+1
I have H100s to myself, and access to more GPUs than I know what to do with in national clusters.
The Spark is much more fun. And I’m more productive. With two of them, you can debug shallow NCCL/MPI problems before hitting a real cluster. I sincerely love Slurm, but nothing like a personal computer.
latchkey
Your complaint sounds more like the way that you have to access the HPC (via slurm), not the compute itself. After having now tried slurm myself, I don't understand the love for it at all.
As for debugging, that's where you should be allowed to spin up a small testing cluster on-demand. Why can't you do that with your slurm access?
enum
I’m not complaining. The clusters are great. The non-Slurm H100s are great. The Spark is more fun.
yunohn
Thanks for this bleeding edge content!
But please have your LLM post writer be less verbose and repetitive. This is like the stock output from any LLM, where it describes in detail and then summarizes back and forth over multiple useless sections. Please consider a smarter prompt and post-editing…
MaKey
Why would you get this when a Ryzen AI Max+ 395 with 128 GB is a fraction of the price?
zamadatix
Theoretically it has slightly better memory bandwidth, (you are supposed to get) the Nvidia AI software ecosystem support out of the box, and you can use the 200G NIC to stick 2 together more efficiently.
Practically, if the goal is 100% about AI and cloud isn't an option for some reason, both options are likely "a great way to waste a couple grand trying to save a couple grand" as you'd get 7x the performance and likely still feel it's a bit slow on larger models using an RTX Pro 6000. I say this as a Ryzen AI Max+ 395 owner, though I got mine because it's the closest thing to an x86 Apple Silicon laptop one can get at the moment.
pjmlp
Complete computer with everything working.
simjnd
The complete Framework Desktop with everything working (including said Ryzen AI Max 395+ and 128 GB of RAM) is 2500 EUR. In Europe the DGX Spark listings are at 4000+ EUR.
pjmlp
Framework doesn't sell in Europe and they are sponsoring the wrong kind of folks nowadays.
zamadatix
The vast majority of Ryzen AI Max+ 395s (by volume at least) are sold as complete system offerings as well. About as far as you can go the other way is getting one without an SSD, as the MB+RAM+CPU are an "all or nothing" bundle anyways.
pjmlp
Including a Linux distribution with working drivers?
simlevesque
CUDA
d3m0t3p
Because the ML ecosystem is more mature on the NVidia side. Software-wise the cuda platform is more advanced. It will be hard for AMD to catch up. It is good to see competition tho.
shikon7
But the article shows that the Nvidia ecosystem isn't that mature either on the DGX Spark with ARM64. I wonder if Nvidia is still ahead for such use cases, all things considered.
bigyabai
On the DGX Spark, yes. On ARM64, Nvidia has been shipping drivers for years now. The rest of the Linux ecosystem is going to be the problem, most distros and projects don't have anywhere near the incentive Nvidia does to treat ARM like a first-class citizen.
suprjami
So I can spend thousands of dollars to have an unstable training environment and inference performance worse than a US$200 3060.
Wow. Where do I sign up?
thehamkercat
The memory bandwidth on this thing is absolute trash, better buy a mac mini/studio with this much ram if you're throwing this much money, it'll be faster (M4 Max)
suprjami
Agree, any Max or Ultra should walk all over this thing, and has the advantage of many years of already-working software.
Apple benchmarks: https://github.com/ggml-org/llama.cpp/discussions/4167
vardump
3060 doesn't have 128 GB RAM.
suprjami
And a 14B model running at 22tg/s means you won't be using that 128G RAM for inference either.
yunohn
Yeah I’m honestly unclear on Nvidia’s thinking here - inference speed is unbelievably slow for the price.
Given the extreme advantage they have with CUDA and the whole ML ecosystem - barely matching Apple’s M-ultra speeds is a choice…
moffkalast
128GB / 12 GB = ~11, * 200€ = only 2200€ plus mining rig mobo.
It would be cheaper to buy up a dozen 3060s and build a custom PC around them than to buy the Spark.
pjmlp
Except the Spark was designed to have everything nicely working.
aseipp
I'm not yet using mine for ML stuff because there are still a lot of various issues like this post outlined. But I am using mine as an ARM dev system in the meantime, and as a "workstation" it's actually quite good. The Cortex-X925 cores are Zen5 class in performance and it is overall an absolute unit for its size, I'm very impressed that a standard ARM core is pushing this level of performance for a desktop-class machine. I thought about buying a new Linux desktop recently, and this is good enough I might just plug it into a monitor and use it instead.
It is also a standard UEFI+ACPI system; one Reddit user even reported that they were able to boot up Fedora 42 and install the open kernel modules no problem. The overall delta/number of specific patches for the Canonical 6.17-nvidia tree is pretty small when I looked (the current kernel is 6.11). That and the likelihood the consumer variant will support Windows hopefully bodes well for its upstream Linux compatibility, I hope.
To be fair, most of this also true of Strix Halo from what I can tell (most benchmarks put the DGX furthest ahead at prompt processing and a bit ahead at raw token output. But the software is still buggy and Blackwell is still a bumpy ride overall, so it might get better). But I think it's mostly the pricing that is holding it back. I'm curious what the consumer variant will be priced at.
eadwu
There are bleeding edge issues, everyone dials into transformers so that's generally pain proof.
I haven't exactly bisected the issue but I'm pretty sure convolutions are broken on sm_121 after a certain size, getting 20x memory blowup from a convolution from a 2x batch size increase _only_ on the DGX Spark.
I haven't had any problems with inference, but I also don't use the transformers library that much.
llama.cpp was working for openai-oss last time I checked and on release, not sure if something broke along the way.
I don't exactly know if memory fragmentation is something fixable on the driver side - this might just be the problem with kernel's policy and GPL, it prevents them from automatically interfering with the memory subsystem to the granularity they'd like - see zfs and their page table antics - or so my thoughts on it is.
If you've done stuff on WSL, you have similar issues and you can fix it by running a service that normally compacts and clean memory, I have it run every hour. Note that this does impact at the very least CPU performance and memory allocation speeds, but I have not have any issue with long training runs with it (24hr+, assuming that is the issue, I have never tried without it and put that service in place since getting it due to my experience on WSL).
jsheard
No mention of the monstrous 200GbE NIC, seems like a waste if people aren't finding a use for it.
RyeCatcher
Need to buy 2 and connect em. :-)
veber-alex
The llama.cpp issues are strange.
There are official benchmarks of the Spark running multiple models just fine on llama.cpp
CaptainOfCoit
There wasn't any instructions how the author got ollama/llama.cpp, could possibly be something nvidia shipped with the DGX Spark and is an old version?
RyeCatcher
Cool I’ll have a look. All reflections I made were first pass stuff.
moffkalast
Llama.cpp main branch doesn't run on Orins so it's actually weird that it does run on the Spark.
stuckinhell
I'm utterly shocked at the article saying GPU inference (PyTorch/Transformers)isn't working. Numerical instability produces bad outputs, Not viable for real-time serving, Wait for driver/CUDA updates!
My job just got me and our entire team a DGX spark. I'm impressed at the ease of use for ollama models I couldn't run on my laptop. gpt-oss:120b is shockingly better than what I thought it would be from running the 20b model on my laptop.
The DGX has changed my mind about the future being small specialized models.
RyeCatcher
Totally agree. I’ve been training nanochat models all morning. Hit some speed bumps. I’ll share more later in another article. Buts it’s absolutely amazing. I fine tuned a Gemma3 model in a day yesterday.
null
jasonjmcghee
> I'm utterly shocked at the article saying GPU inference (PyTorch/Transformers)isn't working
Are you shocked because that isn't your experience?
From the article it sounds like ollama runs cpu inference not GPU inference. Is that the case for you?
RyeCatcher
Would love to hear from others using the spark for model training and development.
One of my colleagues wrote a first impressions blog post last week. It's from our company's perspective, but is a solid overview of the product and intended capabilities, from the POV of an AI developer or data scientist.
https://www.anaconda.com/blog/python-nvidia-dgx-spark-first-...