# NVIDIA/dgx-spark-playbooks

Collection of step-by-step playbooks for setting up AI/ML workloads on NVIDIA DGX Spark devices with Blackwell architecture.

Repository: https://github.com/NVIDIA/dgx-spark-playbooks
Canonical: https://ross.abutalabs.com/products/dgx-spark-playbooks
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Last push: 2026-07-29T15:15:56+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 95, release rhythm 35, longevity 23
- inputs: {"age_days": 334, "days_push": 35, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1299, forks 281 (observed 2026-08-28T04:04:17.181910+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: llm-inference, llm-training, rag, machine-learning, developer-tools, documentation
- domain: artificial-intelligence, large-language-models, machine-learning, developer-tools, gpu-computing, tutorials
- platform: cli, self-hosted
- tags: dgx-spark, nvidia, playbooks, blackwell, jupyter-notebooks, edge-ai, local-llm, fine-tuning, inference, linux, gpu

## Member repositories
- NVIDIA/dgx-spark-playbooks (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:17.181910+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:53:24.565292+00:00, confidence not recorded.
  - readme: https://github.com/NVIDIA/dgx-spark-playbooks (fetched 2026-08-28T04:04:17.181910+00:00, sha 04c5ba9d9ce4)
- Data as of 2026-08-30T08:39:29.467469+00:00.