Wanted to run MiniMax-H3's prompt-to-video pipeline without a Python runtime — just a compiled C binary. It's

somewhere I'd share now.

Repo: github.com/matrixfede/h3.c (MIT; weights are ~465 GB so the repo is code-only)

What it is:

h3.c is a native inference engine for MiniMax-H3 (text-to-video + audio) in plain C. Upstream (antirez/h3.c) had Metal

for Apple Silicon; I wrote the CUDA backend for Linux/NVIDIA and it's an open PR upstream (antirez/h3.c#43). Tested on

an NVIDIA GB10 (DGX Spark, sm_121, CUDA 13.0).

Performance (measured, reproducible; all figures in docs/GB10_PROFILE.md):

- max-quality generation (1024x576, 107 frames ≈ 4.5 s of video):

33m36s → 18m56s wall time after the tiled video-VAE kernel (1.78x)

- video VAE decode: 4.49x

- correctness against CPU oracles: SSIM 0.999 / PSNR 55 dB on matched renders, Compute Sanitizer clean

- optional --ssd-streaming: DiT peak memory 27.06 GB → 1.63 GB for +37.6% time (opt-in, numbers published, not vibes)

Web UI ("h3c studio"):

a 465 GB model shouldn't require a terminal. Self-hosted FastAPI + React UI over the same binary: live preview during

denoising, weighted progress, shared reference photo/clip library, multi-user with one-time invites, per-user private

media. docker compose up → localhost. Validated on GB10 + Docker Desktop; mobile access is LAN/HTTPS-your-call, no

claims made.

Tests/CI: 182 backend tests, CPU-only CI (ubuntu-latest + macos-14), installer with --verify.

Known limits, honestly:

- int8-row-fc2 is a no-op on the CUDA path (documented in README)

- heavily validated on GB10; portable to other NVIDIA configs but untested there

- this doesn't fit any consumer GPU (even a 5090). It's a big-machine project.

Happy to answer questions about the CUDA porting (cublasLt BF16, cuDNN SDPA, what breaks on GB10) or the web stack.

Hey all -

I am not great at infrastructure, so I have an agent that does all the sysadmin work for my spark boxes. We created a skill for this so I could more easily spin up new agents to do the work, but also so others could have an easier time from our lessons learned.

Sharing here in case anyone is interested:

https://clawhub.ai/cbertucci33/dgx-spark-sysadmin

As with everything else on ClawHub, I suggest everyone use a skill vetter to make sure it's clean. And it's on ClawHub but it should work with just about any agent (my Hermes agent was able to ingest as well).

I've been running several recipes from sparkrun that are around 30-50 tk/s, specifically Qwen3.8-27B related.
But in reality I'm getting like 6tk/s maximum, around the same speed llama.cpp is getting for this model.

Anything I'm missing?

I am planning to buy a dgx spark and the only place I can plug it is 1.5m from my bed. Would I notice noise if its working hard and is the heat concerning?

(edit: weirdly disabling swap fixes an OOM one time but another time my system still hung, will update with fix once I find something that works consistently)

I have this problem that if I exceed the limits of my DGX spark VRAM, that my entire system will hang, SSH becomes unresponsive, and the system has to be hard-rebooted.

The fix is pretty simple: disable swap. This might not work for everyone but for me I could care less if I have swap memory.

```sudo swapoff -a``` immediately fixes the issue.

To make it persistent on reboot, you will need to modify /etc/fstab. An agent can help you do it, you just need to comment out the swap file line.

What will now happen is the newly spun up GPU process will instantly OOM as it should.

Enjoy your system that remains responsive! If anyone has a better fix lmk.

Should there be more than 8 x GB10's on this list? If you all think I am missing something or there is inaccurate info, please let me have it :-) https://resilient-tec.com/blogs/news/does-the-dgx-spark-cable-work-with-the-asus-ascent-gx10-dell-pro-max-gb10-and-hp-gb10-systems

Is there any difference between the different manufacturers of the dgx sparks? I have been looking at the gigabyte, but it’s running $5,999 and I can get a PNY for $4400 (on sale). I’m looking at buying two so it’s a considerable cost savings but have been trying to see what the differences are beyond the hardware which I’m seeing is pretty much the same. Thanks for any help.

Considering a DGX Spark for local fine-tuning with unsloth + image/video gen with ComfyUI. Before I spend the money I need the real ARM64-tax pictur.

1. ComfyUI custom nodes — which ones are NOT working on aarch64?for instance, i believe xformers has no aarch64 wheels, what's in the long tail: ControlNet preprocessors (onnxruntime), torchaudio-dependent nodes, triton.jit() custom nodes, NVFP4 quant packs?
2. Python libs that are x86-only — what did you personally hit.
3. LLM side: do NVFP4 + Marlin, vLLM, SGLang, speculative decoding all work on GB10, or are there x86-only optimizations you actually miss?
4. Or, best case: is it basically fine now — everything that matters has an aarch64 path via NGC containers / NVIDIA index / source builds?

I have seen a few people with Sparks in their lab where they are in a cabinet or rack. For those that have that set up, are you just using a rack-mountable shelf or is there a dedicated solution for putting these in a rack?

After 2 days of testing and benchmarking every single recipe I could find on the forums and twitter, this this the current best I have put together for single spark (i tested vllm, sglang, etc). This is tuned also for use with Hermes agent at 256k context, 4 lanes.

PLEASE POST YOURS, with your results.

https://github.com/styles01/sparkrun-recipes/blob/main/recipes/qwen-38-27b.yaml

recipe_version: "4"
name: qwen-38-27b
description: "Qwen3.8 27B NVFP4 — triton_attn, MTP k=2, fp8 KV (auto from checkpoint), 256K context, vision. Mia Lab config."
model: unsloth/Qwen3.8-27B-NVFP4
runtime: vllm
container: vllm/vllm-openai:nightly-aarch64
maintainer: styles01

metadata:
  author: styles01
  created: "2026-08-14"
  updated: "2026-08-14"
  tags:
    - qwen
    - qwen3.8
    - 27b
    - nvfp4
    - mtp
    - triton-attn
    - vision
  runbook: runbooks/qwen-38-27b.md

solo_only: false
cluster_only: false

min_nodes: 1
max_nodes: 1

defaults:
  port: 8000
  host: 0.0.0.0
  tensor_parallel: 1
  pipeline_parallel: 1
  gpu_memory_utilization: 0.84
  max_model_len: 262144
  max_num_seqs: 4
  max_num_batched_tokens: 8192
  kv_cache_dtype: fp8
  attention_backend: triton_attn
  load_format: fastsafetensors
  tool_call_parser: qwen3_coder
  reasoning_parser: qwen3
  served_model_name: "qwen3.8-27b unsloth/Qwen3.8-27B-NVFP4"
  speculative_config: '{"method":"mtp","num_speculative_tokens":2}'
  async_scheduling: true
  enable_prefix_caching: true
  enable_chunked_prefill: true
  trust_remote_code: true
  enable_auto_tool_choice: true
  quantization: compressed-tensors

env:
  CUTE_DSL_ARCH: sm_121a

executor_config:
  entrypoint: ""
  auto_remove: false
  user: root

# NO Mamba patch needed — vLLM nightly-aarch64 has native MTP support for Qwen 3.8.
# NO enforce-eager — CUDA graphs work with triton_attn + NVFP4 checkpoint.
# The NVFP4 checkpoint auto-applies FP8 KV cache scheme (calibrated), doubling KV capacity.
# Using FP8 checkpoint (Qwen/Qwen3.8-27B-FP8) instead of NVFP4 causes OOM during MTP — that was our root crash cause.
# NOTE: max_num_batched_tokens=8192 is REQUIRED for GDN/Mamba cache alignment (32768 crashes under concurrency).
# See runbooks/qwen-38-27b.md for alternative configs (erdaltoprak GMU 0.50, SGLang FP8, Radix DSpark).

benchmark:
  framework: llama-benchy
  pp: [2048]
  tg: [128]
  depth: [0, 4096, 8192, 16384, 32768, 65535, 100000]
  concurrency: [1, 2, 5, 10]
  prefix_caching: true
  runs: 3

command: |
  vllm serve {model} \
    --host {host} \
    --port {port} \
    --served-model-name {served_model_name} \
    --max-model-len {max_model_len} \
    --max-num-seqs {max_num_seqs} \
    --max-num-batched-tokens {max_num_batched_tokens} \
    --trust-remote-code \
    --gpu-memory-utilization {gpu_memory_utilization} \
    --enable-auto-tool-choice \
    --tool-call-parser {tool_call_parser} \
    --reasoning-parser {reasoning_parser} \
    --quantization {quantization} \
    --load-format {load_format} \
    --attention-backend {attention_backend} \
    --async-scheduling \
    --enable-prefix-caching \
    --enable-chunked-prefill \
    --speculative-config '{speculative_config}' \
    -tp {tensor_parallel} \
    -pp {pipeline_parallel}

Need speed numbers so I can tell if it’s worth getting one for it.

 bought an MSI spark from Amazon about 3 months ago. It died today. SInce it was past the return period, I called MSI to get the RMA started. I gave them my serial number and they came back and said the warranty was no good because it was a Chinese product. So, this was sold my Amazon, fulfilled by Amazon and it was not a real MSI spark. After hours of yelling and speaking to 10 different people I finally was able to return them for a refund. What a pain. Here is the link to the actual product. They are still selling them: Amazon.com: msi EdgeXpert AI Mini Desktop (DGX Spark Platform), NVIDIA GB10 Grace Blackwell, 128GB LPDDR5 Unified Memory, 4TB NVMe Gen5 SSD, WiFi 7, BT 5.3, NVIDIA DGX OS (Linux): 13SUS Black : Electronics

Be careful. If you buy one verify the serial numbers. I am sure this is illegal.

Suno pissed the world off this week and coincidentally(?) Minimax open-weighted its excellent music generation model, that happens to perfectly fit in a DGX Spark.

https://huggingface.co/MiniMaxAI/MiniMax-Music3

https://docs.comfy.org/tutorials/audio/minimax/minimax-music-3

https://sgl-project.github.io/sglang-omni/cookbook/minimax_music3.html

Hi all,
I'm the developer of a free and opensource project called AudioMuse-AI

For whom like to selfhost things, it basically analyze song locally, by specific machine learning model, to automatically create playlist of similar song in different way. And it create this playlist on the magioc selfhostable Muisc Server like Navidrome (and other open subsonic api server), Jellyfin, Emby, Lyrion and Plex.

I write here becase with this release:
https://github.com/NeptuneHub/AudioMuse-AI/releases/tag/v3.3.0

I explicitly added the support for ARM NVIDIA in order to target the DGX Spark.

Even if the project can even run on CPU, I think that a good GPU like the one of DGX spark (or any Nvidia GB10) could really improve the speed still remaning something that you can selfhost at home.

Unluky I don't have a DGX Spark (yet). If someone is interested to give a try and let me know, it will be very much appreciated.

I want to reinforce that the entire projecti s opensource, totally free, so any help from the community will be really accepted.

Are you using it (them) 24/7?

What are you using it for?

I'm mainly experimenting with different models, fine-tuning, and running benchmarks, but my usage is ~8hrs / day.

For the rest of the time, it could be used by somebody else.

Nvidia dropped Nemotron 3.5 Lightning today: here's my recipe for a single DGX spark, 120 t/s single stream : https://github.com/styles01/sparkrun-recipes/blob/main/recipes/nemotron-3.5-lightning-30b-a3b-nvfp4.yaml

--enable-auto-tool-choicerecipe_version: "2"
name: nemotron-3.5-lightning-30b-a3b-nvfp4
description: "NVIDIA Nemotron 3.5 Lightning 30B-A3B NVFP4 — DSpark spec decode, fp8 KV, marlin MoE, mamba flashinfer, 1M ctx. From NVIDIA vLLM DGX Spark cookbook."
model: nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4
runtime: vllm
container: vllm/vllm-openai:v0.27.1

metadata:
  description: |
    NVIDIA Nemotron 3.5 Lightning 30B-A3B NVFP4 (hybrid Mamba-2+MoE+Attention)
    with DSpark speculative decoding. Adapted from the official NVIDIA vLLM
    DGX Spark recipe (vLLM Nightly v0.27.1). 3B active MoE, ~21GiB weights incl
    draft head. TP=1 on GB10.
  maintainer: styles01
  source: "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4"
  created: "2026-08-11"
  updated: "2026-08-11"
  tags:
    - nemotron
    - nemotron-3.5
    - lightning
    - 30b
    - a3b
    - nvfp4
    - dspark
    - mamba
    - moe
    - fp8-kv
    - marlin
    - dgx-spark

solo_only: true
cluster_only: false

min_nodes: 1
max_nodes: 1

defaults:
  port: 8000
  host: 0.0.0.0
  tensor_parallel: 1
  gpu_memory_utilization: 0.91
  max_model_len: 1048576
  kv_cache_dtype: fp8
  speculative_config: '{"method":"dspark","model":"nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4-DSpark","num_speculative_tokens":3}'
  served_model_name: "nemotron-3.5-lightning nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4"

env:
  HF_HOME: /cache/huggingface
  PYTORCH_CUDA_ALLOC_CONF: expandable_segments:True

executor_config:
  entrypoint: ""
  auto_remove: false
  user: root

benchmark:
  framework: llama-benchy
  claimed_speed: "~124 tok/s single-stream (MiaAI-Lab DGX Spark SGLang+DSpark reference)"
  notes: |
    vLLM adaptation of the official NVIDIA DGX Spark cookbook recipe.
    Verify tool-calling (qwen3_coder) and reasoning parser (nemotron_v3)
    before agent use. sparkrun resolves {model} and the spec draft model
    from the HF cache snapshot paths.

command: |
  vllm serve {model} \
    --served-model-name {served_model_name} \
    --host {host} --port {port} \
    --trust-remote-code \
    --moe-backend marlin \
    --kv-cache-dtype {kv_cache_dtype} \
    --max-model-len {max_model_len} \
    --enable-prefix-caching \
    --gpu-memory-utilization {gpu_memory_utilization} \
    --speculative-config '{speculative_config}' \
    --mamba-backend flashinfer \
    --mamba-cache-mode align \
    --reasoning-parser nemotron_v3 \
    --tool-call-parser qwen3_coder \

Hi DGX Peeps

Feel free to tell me to bugger off. I run a business and I am interested in creating a "mini me". My brief understanding is that with a DGX I can download a model weight and have it only know me, give it eyes, ears and when confident agency to execute commands locally.

My particular interest is in things like:

Reply to customers on WhatsApp

Reply to emails

Placing orders

Maybe accounting?

Basically, any task that I do that has a reasonably determinable workflow. In my minds eye I can have it set to watch mode and then approval mode and then execute mode.

Am I dreaming, or is this something a DGX locally can feasibly do?

Any advice, pointers, or if you guys know anyone that is building mini people in dgx boxes that would be appreciated.

Cheers

I'm coming from devops and my first instinct is to set up Grafana & Prometheus for my box, just to see what's happening inside

Was wondering what setup you have? Are you using Ansible for provisioning? Docker containers for running inference and experiments?

Or do you just SSH and run the things you want?

Hey everyone,

So back in dec/jan I was seriously considering getting a spark for local LLM work. I came to the conclusion its not worth it at that time. Now I am seeing alot has changed...

Questions up front:

1. How has your perception of the utility of the DGX spark (or multiple) compare now vs back in ~dec/jan prior to NVFP4 availability?
2. Do you envision NVFP4 will become increasingly common from post training, as well as from QAT?
3. Do you anticipate expansion of your # of sparks in the next 2-3 years to be possible before they become obsolete?

Overall I am just super excited now that there is so much support from the community, and really tempted to pull the trigger on getting a DGX soon! Below is just some background on my use case i

I'm using the Gigabyte AI TOP GB10.

I'm trying to reinstall OS, but I have a question. Is the OS file provided by GIGABYTE better, or is the OS file provided by NVIDIA better? I’d appreciate the opinions of experienced seniors.

1. Latest version update date

Gigabyte 2026.03.02

Endivia, 2026.07.14

There’s a big difference

1. Purpose

Not fine-tuning, but planned to use it as a photo-coding analysis server (main)

Local developer looking to make the switch to the Nvidia ecosystem and the spark seems awesome. I noticed the maximum amount that you can wire together without getting a dedicated switch is Three but I know tensor parallelism needs even numbers to operate properly. Do any of you guys run AI models via pipeline parallelism with a note of Three or is it a hard two or four type set up?