NVIDIA/dgx-spark-playbooks resource

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1299 stars · 281 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of step-by-step playbooks (Jupyter Notebook-based guides) for setting up AI/ML workloads on NVIDIA DGX Spark devices with Blackwell architecture. It covers installing AI frameworks, running optimized model inference, fine-tuning, multi-device networking, and development environment setup.

Use cases

• set up ollama or vllm on a dgx spark
• run local llm inference on nvidia spark
• fine-tune models with llama factory or unsloth on dgx spark
• build a rag application on dgx spark
• connect multiple dgx spark devices for distributed training
• set up a local ai agent with local models
• install vs code and dev tools on dgx spark
• quantize models with nvfp4 on blackwell hardware

When to choose

• you own or plan to use an NVIDIA DGX Spark device
• you want curated, tested setup instructions for AI frameworks on Blackwell hardware
• you need guidance on inference, fine-tuning, or multi-Spark clustering

When to avoid

• you use non-NVIDIA or non-Blackwell hardware
• you need general GPU tutorials unrelated to DGX Spark
• you want production deployment infrastructure rather than setup guides

Facets

learning-resource · maturity active

llm-inference llm-training rag machine-learning developer-tools documentation artificial-intelligence large-language-models machine-learning developer-tools gpu-computing tutorials cli self-hosted dgx-spark nvidia playbooks blackwell jupyter-notebooks edge-ai local-llm fine-tuning inference linux gpu

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markdown · JSON · MCP: product_card(name="NVIDIA/dgx-spark-playbooks")