Nvidia DGX Spark as a daily driver • Hacker News Board

Re: Nvidia DGX Spark as a daily driver

#21

Post by [deleted] » Wed, Jul 22, 2026, 9:11 PM UTC

dllu wrote: post #16

trentor wrote: post #9

I'm genuinely disappointed with my Spark. I don't know how anyone can claim it performs decently with LLMs or diffusion models. Back when I worked in VFX in the early 2000s, we had a saying: "Render time is coffee time" and if you try to run this thing with a usable context size, you'll be drinking a lot of coffee. Most of the optimizations it relies on for inference simply aren't available for training, so it crawls…

The DGX Spark hits a sweet spot for me where it can simultaneously work for general computing and run local LLM inference fast enough for some hobbyist dabbling. The RTX 6000 Pro Blackwell is more than twice the price (it has increased quite a bit recently from $8000 to $11600), not to mention the "rest of the PC" needed to get it working, so it's not really a fair comparison. Compared to other 128 GB unified memory…

[deleted]

Re: Nvidia DGX Spark as a daily driver

#22

Post by dllu » Wed, Jul 22, 2026, 9:14 PM UTC

aftbit wrote: post #17

Neat blog! I was intrigued by this bullet point mentioned in passing: >my four hard drive USB 3.2 ZFS raidz2 array with four 24 TB drives Can you speak more about this? Which USB array did you choose? How well does it work? I've been slowly planning a transition away from my power-hungry surplus enterprise gear in the 19" rack towards a smaller, quieter, lower power setup ... but storage is the real kicker right now.…

It's an Orico 9948C3 with four Seagate Barracuda 24TB drives. They were on sale last year [1].

Unfortunately, the enclosure doesn't work super well on Linux. There is a weird bug where the drives don't enumerate when I boot up my computer. This happens on both my x86_64 AMD machine running Linux, and on the DGX Spark. The solution is... simply power cycle the enclosure a couple of times by toggling the power button on it and then it works. Once all four drives show up in lsblk, I can `sudo zfs import ...` manually. This is really gross and annoying. Replacing the USB cable, flipping the USB-C cable 180 degrees, hot plugging it, etc, all didn't work, both on the DGX Spark and the other Linux machine. I've also read reports of it being unstable in UAS mode on Linux but I haven't found a big difference in stability between enabling UAS or falling back to usb-storage.

Once it starts up correctly though, the drives are fast. I store my huge amount of 100 megapixel photos on it.

The Seagate Barracudas are helium-filled HAMR/CMR drives and are apparently rebranded/binned Exos drives. They aren't rated for 24/7 use but then neither are the refurbished Exos drives.

[1] https://www.reddit.com/r/buildapcsales/comments/1p29pm8/hdd_...

Re: Nvidia DGX Spark as a daily driver

#23

Post by bigyabai » Wed, Jul 22, 2026, 9:18 PM UTC

ARandomerDude wrote: post #19

willis936 wrote: post #6

Earlier quoted context omitted.

A good instinct. There are a lot of things a $500 AMD GPU can do in linux that a $5000 DGX cannot.

I know nothing about this topic but your comment piqued my curiosity. What would a $500 AMD GPU do better than DGX?

AMD has Mesa drivers for graphics, which are better-optimized than Nvidia's proprietary Linux Vulkan drivers. It can be fixed in software, but Nvidia's only barely started to catch up.

The focus for Nvidia's GPU stack on Linux is getting CUDA working, which means that some traditional raster features get neglected.

Re: Nvidia DGX Spark as a daily driver

#24

Post by MrVitaliy » Wed, Jul 22, 2026, 9:19 PM UTC

I do appreciate how Nvidia tries to say close to vanilla with Linux and Android (nvidia shield). Instead of trying to build a shitty moat like Samsung with all their garbage software.

If nvidia ever releases Android smartphone, I'd probably stand in line to get one.

Re: Nvidia DGX Spark as a daily driver

#25

Post by jubilee33 » Wed, Jul 22, 2026, 9:22 PM UTC

This is an interesting review. I have a Chinese strix halo box that's isnt available in the west (favm faex1) I've been able to do some ok graphical gen, or some decent agentic tasks as a fallback for when some of the APIs are overloaded during business hours, but nothing amazing for sure, and also not both at the same time. But here's the thing...it cost me 1800usd two months ago....and it's runs x86. I am struggling to see why people pay +2x more for the Arm Nvidia version, despite the slightly higher bandwidth it still does basically the same AI tasks and alot fewer high end general computing tasks... I like my box but I wouldn't find it useful enough to pay more than I did for it or get more of them and cluster for instance. Can anyone explain the allure of the Nvidia box, other than brand name?

Re: Nvidia DGX Spark as a daily driver

#26

Post by bullen » Wed, Jul 22, 2026, 9:23 PM UTC

I have been running uConsoles with CM5 (2712 and 3588 with 16GB RAM) for 6 months as daily drivers.

They are ~$500* and present the same ARM problems/opportunities.

But they are completely silent (no fan, the case is the heat sink).

My 6600(3050) desktop from 2016(2024) with replaced SSD(2021)/RAM(2025) (they age like milk) now gets little use and M$ will soon sleep with the fishes.

*Hard to get now as the 3588 that has linux for uConsole is out of stock and the Raspberry one is rare and more expensive by the day.

Re: Nvidia DGX Spark as a daily driver

#27

Post by girvo » Wed, Jul 22, 2026, 9:25 PM UTC

willis936 wrote: post #6

cogman10 wrote: post #4

I strongly considered it, but the one thing that scares me away from wanting to do the spark is you basically have to use nvidia's linux (from what I've read) and it doesn't appear the nvidia is interested in upstreaming their kernel changes. I'm avoiding where possible buying electronics where support is controlled by the manufacturer and not me.

A good instinct. There are a lot of things a $500 AMD GPU can do in linux that a $5000 DGX cannot.

Although one of the ones it cannot do is have/address 128GB of video memory, so it depends on what you want to achieve.

Re: Nvidia DGX Spark as a daily driver

#28

Post by dietr1ch » Wed, Jul 22, 2026, 9:26 PM UTC

MrVitaliy wrote: post #24

I do appreciate how Nvidia tries to say close to vanilla with Linux and Android (nvidia shield). Instead of trying to build a shitty moat like Samsung with all their garbage software. If nvidia ever releases Android smartphone, I'd probably stand in line to get one.

After leaving a few of great-on-paper SoCs as paperweights I've learnt that I just don't want to deal with anyone's custom platform as I'll eventually be left with an outdated system that's annoying and time-consuming to maintain.

Re: Nvidia DGX Spark as a daily driver

#29

Post by embedding-shape » Wed, Jul 22, 2026, 9:26 PM UTC

jubilee33 wrote: post #25

This is an interesting review. I have a Chinese strix halo box that's isnt available in the west (favm faex1) I've been able to do some ok graphical gen, or some decent agentic tasks as a fallback for when some of the APIs are overloaded during business hours, but nothing amazing for sure, and also not both at the same time. But here's the thing...it cost me 1800usd two months ago....and it's runs x86. I am strugglin…

> isnt available in the west [...] Can anyone explain the allure of the Nvidia box

The first part might answer the second one. Otherwise, the lack of CUDA and the nvidia ecosystem of tooling could also explain why it doesn't seem so interesting for AI tasks.

Re: Nvidia DGX Spark as a daily driver

#30

Post by icedchai » Wed, Jul 22, 2026, 9:26 PM UTC

dllu wrote: post #16

trentor wrote: post #9

I'm genuinely disappointed with my Spark. I don't know how anyone can claim it performs decently with LLMs or diffusion models. Back when I worked in VFX in the early 2000s, we had a saying: "Render time is coffee time" and if you try to run this thing with a usable context size, you'll be drinking a lot of coffee. Most of the optimizations it relies on for inference simply aren't available for training, so it crawls…

The DGX Spark hits a sweet spot for me where it can simultaneously work for general computing and run local LLM inference fast enough for some hobbyist dabbling. The RTX 6000 Pro Blackwell is more than twice the price (it has increased quite a bit recently from $8000 to $11600), not to mention the "rest of the PC" needed to get it working, so it's not really a fair comparison. Compared to other 128 GB unified memory…

I have a AMD Strix Halo box I use for similar dabbling. It definitely wasn't an "out of the box" experience, fiddling around with kernel versions and ROCm installs. These days I mostly wind up using the Vulkan build of llama.cpp for inference.