Hi guys,
I am getting a 3rd unit and some QSFP112 cables to connect it in a triangle setup.
I am thinking of running Deepseek v4.1 Flash.
What is your experience with 3 units? What is your favorite setup?
Currently, I run Deepseek v4 Flash and it runs 35-45 tkps on two units.
I hope that the v4.1 version will be even smarter.
Coming this month, NVIDIA DGX Spark will be available with 64GB of unified memory from top manufacturer partners — Acer, ASUS, Dell, Gigabyte, HP and MSI — giving developers, researchers and AI enthusiasts a new configuration with DGX OS and the NVIDIA AI software stack ready to use from day one.
DGX Spark 64GB is available from Acer, ASUS, Dell, Gigabyte, HP and MSI on Friday, Oct. 23, starting at $4,999.
No mention of an actual Nvidia-branded unit, just partner ones. But $5k, ouch.
Edit: 128GB pricing just increased to $6,950
But this one is mine.
CAT5e running through the house was terminated to RJ11 (phone lines) throughout. Reterminated to
RJ45 to the patch panel. Patched over to a Unifi pro XG 24 port PoE switch. It serves 3 WAPs in the house over PoE despite the CAT5e spec not officially supporting it. It has a 2x25 Gb bond with the Microtik CRS804-4DDQ-hRM switch beneath it. The Microtik is connected to the DAC breakout cables for the 4 sparks, 2 GX10s and 2 DGXs.
The other rack is just AV, and has a bit of cleaning up to do still. There's a MiSTer Pi on that 2nd shelf with the 4x10Gb managed unifi switch which is downstream from the main rack.
The server is a home built Unraid server with 142TB of total disk space, 120TB usable. Future upgrade coming to its NIC with its own QSFP connection to support an nvme pool for models along with my other media.
I'm also working on running more 10Gb to my gaming rigs (not pictured) for wired connections.
At MicroCenter this AM to pick one up. Pricing just increased with the announcement of the 64GB model ($5k for that, btw).
Picked it up on Sunday. I don’t have anyone else I could share this with so I thought I’d come here and say hi.
Does anyone have a decent model or two that behaves with 4?
I’m already running Qwen 27B and so far I’m impressed! Deepseek-flash which I haven’t really tuned yet.
PS-They aren’t stacked like this any more. They will eventually end up in my server rack.
My local stack is a couple DGX Sparks, a 5090, and a 4070 TI. This radio site took three local LLMs, and we generate music, voices, and wacky sound effects.
I give you https://pilgrim.farm
It's a lot more farm than pilgrim.
I own a single DGX Spark (Gigabyte AI TOP ATOM, which I use mostly to run Qwen 3.8 27B and Qwen 3.8 Flash Next) and am considering buying a second one, before they become too expensive.
Which models can you run on two Sparks that you cannot run on one? What speeds do you get (single stream, and at what context length)?
Are there any restrictions on the way you run them (vLLM/SGLang vs llama.cpp) that affect usage of some tools, for example DSpark or DFlash 2? Also, any issues pairing two different units (Gigabyte and ASUS GX10) ?
I ordered 2 DGX Sparks for a POC at my company. My reseller notified me this morning that they are officially EOL and that they don't have a direct replacement to sell me at this time. Anyone else seen this?
This must be related to the announcements yesterday of the N1x laptops and desktop.
Folks
I am working on the 2 trays for 2xDgxSpark. The goal is to fit em all in 3U 10inch server rack (including bricks and an extension at the rear). They are conceptually ready, will give it a 3D-print soon and post update soon with the links to STL files.
Meanwhile, please take a look and let me know if you guys have any more ideas here. I put the bricks on the bottom tray vertically with pockets on the top one. The bottom tray takes 5 holes and top one takes 4 holes (9 holes comprise the 3U size).
Carelessly swapped SGLang for DGPP ( https://github.com/HawkBearPig/dgpp ) using an example config ( https://raw.githubusercontent.com/HawkBearPig/dgpp/refs/heads/master/deploy/cluster_qwen-3.8-flash-next_nvfp4_w1.example.json ) on my single DGX Spark setup with Qwen3.8-Flash-Next and I'm amazed by the solid performance and surprised that I never read about it before. It even does mixed chunking while SGLang couldn't.
Anybody here is using it and/or did accuracy tests?
EDIT Oct, 3rd: I find out the the stock recipe is very conservative about memory. I use DGX Spark as a headless server, so I bumped both KV Cache and prompt cache.
Here's my current config:
{
"model": "nvidia/Qwen3.8-Flash-Next-NVFP4",
"world_size": 1,
"engine": {
"max_concurrency": 4,
"kv_capacity": 524288,
"kv_dtype": "bf16",
"bf16_weights": "bf12+bf16",
"ngram_table": "mmap",
"default_max_tokens": 32768,
"queue_limit": 8,
"max_connections": 64,
"decode_graph": true,
"mtp": true,
"mtp_depth": 1,
"prefix_cache_gib": 6,
"admission": "grow",
"admission_window": 256,
"model_alias": "Qwen3.8-Flash-Next",
"sampling_candidates": 128,
"stats_interval_s": 10,
"dense_weights": "fp8",
"fp8_head": "mma",
"prefill_budget_tokens": 1024,
"prefill_idle_budget_tokens": 4096
},
"paths": {
"resident_cache": ""
},
"http": {
"bind_host": "0.0.0.0",
"port": 8000
}
}
I see two prices for a DGX Spark - with and without an NVIDIA Subscription. For anyone who bought the subscription, is it worth the extra money?
The blurb says “NVIDIA AI Enterprise is a cloud-native suite of software tools, libraries, and frameworks, including NVIDIA NIM and NeMo microservices, that accelerate and simplify the development, deployment, and scaling of AI applications.” Are those things behind a paywall?
I just met a normal non technical guy that is using Muse from meta. He’s been only using it for a week and the way he’s talking about it is like the way we were talking about Openclaw or Hermes when they came out. It was a bit surreal. It was interesting how he was implementing it in his workflow in his business. It almost sounded like he had a whole 1months of experience in 1 week! I haven’t personally checked out Muse but it sounds like there is nothing to build and plenty of features that are useful. Told him about the DGX sparks and local LLM capabilities and he was swearing that local LLMs could never be that good. Tried to tell him that it’s the harness and infrastructure that gives him all the features he likes but I don’t think understood what I was saying. Kinda blew my mind that he was using something he didn’t understand besides the basic “tell it to do something and it does it.” I almost want to try Muse.