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dgx-spark-setupdgx 火花设置

Agent Skill

dgx-spark-setup 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:dgx-spark-setup(dgx 火花设置)
来源仓库:https://github.com/jimmy-hernandez/dgx-spark-setup
安装命令:
openclaw skills install dgx-spark-setup
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

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openclaw skills install dgx-spark-setup

简介

用于配置 NVIDIA DGX Spark 作为本地 LLM 推理服务器,支持 vLLM 和 LiteLLM。

  • 适合在 OpenClaw 中搭建私有化大模型部署环境。
  • 通过 clawhub 安装,需 GPU 驱动和 CUDA 环境预先准备。
  • 高负载运行时需监控显存和温度,避免硬件过载。
  • 仅供研究用途,商业部署请参考官方许可协议。

SKILL.md

name
dgx-spark-setup
version
1.0.0
description
Set up and maintain an NVIDIA DGX Spark (GB10 Blackwell, 128GB unified memory) as a local LLM inference server running vLLM + LiteLLM + OpenClaw. Use when installing vLLM from source on a DGX Spark, troubleshooting Blackwell/sm_121 GPU compatibility, configuring LiteLLM virtual keys for multi-user access, setting up Tailscale for private remote access, or recovering from a broken vLLM environment (torch downgrade, Triton mismatch, flashinfer version conflict).

DGX Spark Setup

Complete setup guide for running Nemotron Super 120B (NVFP4) on a DGX Spark as a private OpenClaw backend with multi-user LiteLLM routing.

Architecture

MacBook (remote) ──Tailscale──► Mac Mini (OpenClaw host, SatPicks worker)
                                      │ LAN SSH
                                      ▼
                               DGX Spark (192.168.1.234)
                               ├── vLLM :8000  (inference)
                               └── LiteLLM :4000 (auth/routing)

Prerequisites

  • DGX Spark with Ubuntu (user: jhernandez)
  • Model downloaded to /home/jhernandez/models/nemotron-super-120b-nvfp4
  • Python 3.12 available (python3 --version)
  • uv installed (curl -LsSf https://astral.sh/uv/install.sh | sh)

1. vLLM Environment Setup

The DGX Spark uses the GB10 Blackwell chip (sm_121). Stock PyPI packages do NOT support sm_121 — everything must be custom built or sourced from specific index URLs.

mkdir -p ~/vllm-install
cd ~/vllm-install
uv venv .vllm --python 3.12
source .vllm/bin/activate

Install PyTorch (CUDA 13.0)

Must use uv pip install with the cu130 index — regular pip may resolve the wrong CUDA variant:

uv pip install torch torchvision torchaudio \
  --index-url https://download.pytorch.org/whl/cu130

Verify: python3 -c "import torch; print(torch.__version__)" → should show 2.11.0+cu130

Build Custom Triton (sm_121 support)

Stock Triton does not support sm_121. Must build from this exact commit:

cd ~/vllm-install
git clone https://github.com/triton-lang/triton.git
cd triton
git checkout 4caa0328bf8df64896dd5f6fb9df41b0eb2e750a
pip install ninja cmake wheel
pip install -e python/

Verify: python3 -c "import triton; print(triton.__version__)" → should show 3.5.0+git4caa0328

Install flashinfer

Versions must match exactly — mismatched cubin/flashinfer causes silent failures:

pip install flashinfer-python
pip install flashinfer  # cubin package — must match flashinfer-python version

Install vLLM from Source

cd ~/vllm-install
git clone https://github.com/vllm-project/vllm.git
cd vllm
git checkout 66a168a197ba214a5b70a74fa2e713c9eeb3251a
pip install -e . --no-build-isolation

2. Running vLLM

Always launch inside the tmux session so it survives SSH disconnects:

tmux new-session -s nemotron   # or: tmux attach -t nemotron

export PATH=$HOME/.local/bin:$PATH
source ~/vllm-install/.vllm/bin/activate

TORCH_CUDA_ARCH_LIST=12.1a \
VLLM_USE_FLASHINFER_MXFP4_MOE=1 \
TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas \
  python -m vllm.entrypoints.openai.api_server \
  --model /home/jhernandez/models/nemotron-super-120b-nvfp4 \
  --trust-remote-code --max-model-len 8192 \
  --gpu-memory-utilization 0.85 --port 8000

Startup takes ~8 minutes (loading 17 safetensor shards). Ready when log shows Application startup complete.

Note: nvidia-smi shows N/A for memory on the GB10 (unified memory architecture) — this is normal, not a bug.

3. LiteLLM Setup

LiteLLM proxies vLLM and handles per-user auth and rate limiting.

Install

pip install litellm

Config (~/litellm-config.yaml)

See references/litellm-config-template.yaml for a full config with virtual keys and rate limits.

Run as systemd service

mkdir -p ~/.config/systemd/user
cat > ~/.config/systemd/user/litellm.service << 'EOF'
[Unit]
Description=LiteLLM Proxy
After=network.target

[Service]
ExecStart=/home/jhernandez/.local/bin/litellm --config /home/jhernandez/litellm-config.yaml --port 4000
Restart=on-failure
RestartSec=5
StandardOutput=append:/home/jhernandez/litellm.log
StandardError=append:/home/jhernandez/litellm.log
Environment=PATH=/home/jhernandez/.local/bin:/usr/local/bin:/usr/bin:/bin

[Install]
WantedBy=default.target
EOF

systemctl --user daemon-reload
systemctl --user enable litellm
systemctl --user start litellm

Verify: curl http://localhost:4000/health/liveliness"I'm alive!"

4. Tailscale

curl -fsSL https://tailscale.com/install.sh | sh
sudo tailscale up
# Visit the auth URL shown, then approve in Tailscale admin
tailscale ip -4  # note this IP for OpenClaw client configs

5. OpenClaw Client Config

Point any OpenClaw instance at LiteLLM:

model:
  provider: openai-compatible
  baseUrl: http://<dgx-tailscale-ip>:4000/v1
  apiKey: <virtual-key>
  model: nemotron-super

Troubleshooting

See references/troubleshooting.md for common failure modes and fixes.

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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