I know it's just a quick test, but llama 3.1 is getting a bit old. I would have liked to see a newer model that can fit, such as gpt-oss-120, (gpt-oss-120b-mxfp4.gguf), which is about 60gb of weights (1).
Correct, most of r/LocalLlama moved onto next gen MoE models mostly. Deepseek introduced few good optimizations that every new model seems to use now too. Llama 4 was generally seen as a fiasco and Meta haven't made a release since
I've got the Dell version of the DGX Spark as well, and was very impressed with the build quality overall. Like Jeff Geerling noted, the fans are super quiet. And since I don't keep it powered on continuously and mainly connect to it remotely, the LED is a nice quick check for power.
You can get two Strix Halo PCs with similar specs for that $4000 price.
I just hope that prompt preprocessing speeds will continue to improve, because Strix Halo is still quite slow in that regard.
Then there is the networking. While Strix Halo systems come with two USB4 40Gbit/s ports, it's difficult to
a) connect more than 3 machines with two ports each
b) get more than 23GBit/s or so per connection, if you're lucky. Latency will also be in the 0.2ms range, which leaves room for improvement.
Something like Apple's RDMA via Thunderbolt would be great to have on Strix Halo…
As you allude, the prompt processing speeds are a killer improvement of the Spark which even 2 Strix Halo boxes would not match.
Prompt processing is literally 3x to 4x higher on GPT-OSS-120B once you are a little bit into your context window, and it is similarly much faster for image generation or any other AI task.
Plus the Nvidia ecosystem, as others have mentioned.
If all you care about is token generation with a tiny context window, then they are very close, but that’s basically the only time. I studied this problem extensively before deciding what to buy, and I wish Strix Halo had been the better option.
The primary advantage of the DGX box is that it gives you access to the nVidia ecosystem. You can develop against it almost like a mini version of the big servers you're targeting.
It's not really intended to be a great value box for running LLMs at home. Jeff Geerling talks about this in the article.
Exactly this. I'm not sure why people keep drumming the "a Mac or Strix Halo is faster/cheaper" drum. Different market.
If I want to do hobby / amateur AI research or do stuff with fine tuning models etc, learn the tooling. I'm better off with the DG10 than AMD or Apple's systems.
The Strix Halo machines look nice. I'd like one of those too. Especially if/when they ever get around to getting it into a compelling laptop.
But I ordered the ASUS Ascent DG10 machine (since it was more easily available for me than the other versions of these) because I want to play around with fine tuning open weight models, learning tooling, etc.
That and I like the idea of having a (non-Apple) Aarch64 linux workstation at home.
Now if the courier would just get their shit together and actually deliver the thing...
I have this device, it's exactly as you say. This is a device for AI research and development. My buddies mac ultra beats it squarely for inference workloads, but for real tinkering it can't be beat.
I've used it to fine tune 20+ models in the last couple of weeks. Neither a Mac or Strix Halo even try to compete.
IMHO DGX Spark at $4,000 is a bad deal with only 273 GB/s bandwidth and the compute capacity between a 5070 and a 5070 TI. And with PCIe 5.0 at 64 GB/s it's not such a big difference.
And the 2x 200 GBit/s QSFP... why would you stack a bunch of these? Does anybody actually use them in day-to-day work/research?
I think the selling point is the 128GB of unified system memory. With that you can run some interesting models. The 5090 maxes out at 32GB. And they cost about $3000 and more at the moment.
1. /r/localllama unanimously doesn't like the Spark for running models
2. and for CUDA dev it's not worth the crazy price when you can dev on a cheap RTX and then rent a GH or GB server for a couple of days if you need to adjust compatibility and scaling.
nothing beats perfectly good vendor firmware updates packaged in an obscenely complicated bash file that just extracts the tool and runs it while performing unnecessary and often broken validation that only runs on hardware that is part of their ecosystem (ex: dell nic on non dell chassis).
A nice little AI review with comparison of the CPU/Power Draw & Networking would be interested in seeing a fine-tuning comparison too. I think pricing was missing also.
I have a slightly cheaper similar box, NVIDIA Thor Dev Kit. The point is exactly to avoid deploying code to servers that cost half a million dollars each. It's quite capable in running or training smart LLMs like Qwen3-Next-80B-A3B-Instruct-NVFP4. So long as you don't tear your hair out first figuring out pecularities and fighting with bleeding edge nightly vLLM builds.
The memory bandwidth limitation is baked into the GB10, and every vendor is going to be very similar there.
I'm really curious to see how things shift when the M5 Ultra with "tensor" matmul functionality in the GPU cores rolls out. This should be a multiples speed up of that platform.
My guess is M5 Ultra will be like DGX Spark for token prefill and M3 Ultra for token generation, i.e. the best of both worlds, at FP4. Right now you can combine Spark with M3U, the former streaming the compute, lowering TTFT, the latter doing the token generation part; with M5U that should no longer be necessary. However given RAM prices situation I am wondering if M5U will ever get close to the price/performance of Spark + M3U we have right now.
Spark's biggest paint point is the price. Does it fix that?
I know it's just a quick test, but llama 3.1 is getting a bit old. I would have liked to see a newer model that can fit, such as gpt-oss-120, (gpt-oss-120b-mxfp4.gguf), which is about 60gb of weights (1).
(1) https://github.com/ggml-org/llama.cpp/discussions/15396
Correct, most of r/LocalLlama moved onto next gen MoE models mostly. Deepseek introduced few good optimizations that every new model seems to use now too. Llama 4 was generally seen as a fiasco and Meta haven't made a release since
It's a product without a purpose.
I've got the Dell version of the DGX Spark as well, and was very impressed with the build quality overall. Like Jeff Geerling noted, the fans are super quiet. And since I don't keep it powered on continuously and mainly connect to it remotely, the LED is a nice quick check for power.
But the nicest addition Dell made in my opinion is the retro 90's UNIX workstation-style wallpaper: https://jasoneckert.github.io/myblog/grace-blackwell/
I just want a standard, affordable mini PC that looks like this one. Or better yet, with the brown accents normally found on recent PowerEdge systems.
https://www.fsi-embedded.jp/contents/uploads/2018/11/DELLEMC...
Zotac has a bunch of x64 mini PCs that use a similar hexagonal styling.
You can get two Strix Halo PCs with similar specs for that $4000 price. I just hope that prompt preprocessing speeds will continue to improve, because Strix Halo is still quite slow in that regard.
Then there is the networking. While Strix Halo systems come with two USB4 40Gbit/s ports, it's difficult to
a) connect more than 3 machines with two ports each
b) get more than 23GBit/s or so per connection, if you're lucky. Latency will also be in the 0.2ms range, which leaves room for improvement.
Something like Apple's RDMA via Thunderbolt would be great to have on Strix Halo…
As you allude, the prompt processing speeds are a killer improvement of the Spark which even 2 Strix Halo boxes would not match.
Prompt processing is literally 3x to 4x higher on GPT-OSS-120B once you are a little bit into your context window, and it is similarly much faster for image generation or any other AI task.
Plus the Nvidia ecosystem, as others have mentioned.
One discussion with benchmarks: https://www.reddit.com/r/LocalLLaMA/comments/1oonomc/comment...
If all you care about is token generation with a tiny context window, then they are very close, but that’s basically the only time. I studied this problem extensively before deciding what to buy, and I wish Strix Halo had been the better option.
The primary advantage of the DGX box is that it gives you access to the nVidia ecosystem. You can develop against it almost like a mini version of the big servers you're targeting.
It's not really intended to be a great value box for running LLMs at home. Jeff Geerling talks about this in the article.
Exactly this. I'm not sure why people keep drumming the "a Mac or Strix Halo is faster/cheaper" drum. Different market.
If I want to do hobby / amateur AI research or do stuff with fine tuning models etc, learn the tooling. I'm better off with the DG10 than AMD or Apple's systems.
The Strix Halo machines look nice. I'd like one of those too. Especially if/when they ever get around to getting it into a compelling laptop.
But I ordered the ASUS Ascent DG10 machine (since it was more easily available for me than the other versions of these) because I want to play around with fine tuning open weight models, learning tooling, etc.
That and I like the idea of having a (non-Apple) Aarch64 linux workstation at home.
Now if the courier would just get their shit together and actually deliver the thing...
I have this device, it's exactly as you say. This is a device for AI research and development. My buddies mac ultra beats it squarely for inference workloads, but for real tinkering it can't be beat.
I've used it to fine tune 20+ models in the last couple of weeks. Neither a Mac or Strix Halo even try to compete.
IMHO DGX Spark at $4,000 is a bad deal with only 273 GB/s bandwidth and the compute capacity between a 5070 and a 5070 TI. And with PCIe 5.0 at 64 GB/s it's not such a big difference.
And the 2x 200 GBit/s QSFP... why would you stack a bunch of these? Does anybody actually use them in day-to-day work/research?
I liked the idea until the final specs came out.
I think the selling point is the 128GB of unified system memory. With that you can run some interesting models. The 5090 maxes out at 32GB. And they cost about $3000 and more at the moment.
1. /r/localllama unanimously doesn't like the Spark for running models
2. and for CUDA dev it's not worth the crazy price when you can dev on a cheap RTX and then rent a GH or GB server for a couple of days if you need to adjust compatibility and scaling.
I am not on reddit. What are they saying?
Seems you are paying the Dell tax of 15%. The same setup is $4K from NVidia, Lenovo and $3K for 1TB at Asus.
https://www.dell.com/en-us/shop/desktop-computers/dell-pro-m...
Dell fixing issues instead of creating new ones? That's a new one for me. Would rather still not deal with their firmware updaters thought.
Give them a chance, I’m sure they’ll add new issues in one of their monthly bios updates.
nothing beats perfectly good vendor firmware updates packaged in an obscenely complicated bash file that just extracts the tool and runs it while performing unnecessary and often broken validation that only runs on hardware that is part of their ecosystem (ex: dell nic on non dell chassis).
On linux I use fwupdmgr to upgrade the firmware on my dell laptop. Not sure if that works for servers though.
A nice little AI review with comparison of the CPU/Power Draw & Networking would be interested in seeing a fine-tuning comparison too. I think pricing was missing also.
I've been working on fine tuning testing, it's something I hope to set up for comparison against the Mac Studio and Framework Desktop clusters soon.
Great article but would be nice to see how larger models work.
I have a slightly cheaper similar box, NVIDIA Thor Dev Kit. The point is exactly to avoid deploying code to servers that cost half a million dollars each. It's quite capable in running or training smart LLMs like Qwen3-Next-80B-A3B-Instruct-NVFP4. So long as you don't tear your hair out first figuring out pecularities and fighting with bleeding edge nightly vLLM builds.
I assume they didn't fix the memory bandwidth pain point though.
The memory bandwidth limitation is baked into the GB10, and every vendor is going to be very similar there.
I'm really curious to see how things shift when the M5 Ultra with "tensor" matmul functionality in the GPU cores rolls out. This should be a multiples speed up of that platform.
My guess is M5 Ultra will be like DGX Spark for token prefill and M3 Ultra for token generation, i.e. the best of both worlds, at FP4. Right now you can combine Spark with M3U, the former streaming the compute, lowering TTFT, the latter doing the token generation part; with M5U that should no longer be necessary. However given RAM prices situation I am wondering if M5U will ever get close to the price/performance of Spark + M3U we have right now.
At least for transformers, it can be kind of fixed with MOE + NVFP4 for small working set despite large resident size.