<p align="center"> <img src="assets/sparkrun-banner.svg" alt="sparkrun — Part of the Spark Arena ecosystem" width="480" /> </p>
<p align="center"> <a href="https://pypi.org/project/sparkrun/"><img src="https://img.shields.io/pypi/v/sparkrun?color=76b900" alt="PyPI version" /></a> <a href="https://github.com/spark-arena/sparkrun/blob/main/LICENSE"><img src="https://img.shields.io/github/license/spark-arena/sparkrun" alt="License" /></a> <a href="https://sparkrun.dev"><img src="https://img.shields.io/badge/docs-sparkrun.dev-1e40af" alt="Documentation" /></a> <a href="https://spark-arena.com"><img src="https://img.shields.io/badge/Spark_Arena-community-76b900" alt="Spark Arena" /></a> </p>
<h3 align="center">One command to rule them all</h3>
<p align="center"> Launch, manage, and stop LLM inference workloads on one or more NVIDIA DGX Spark systems — no Slurm, no Kubernetes, no fuss. </p>
<p align="center"> <a href="https://sparkrun.dev">Documentation</a> · <a href="https://sparkrun.dev/getting-started/quick-start/">Quick Start</a> · <a href="https://sparkrun.dev/recipes/overview/">Recipes</a> · <a href="https://spark-arena.com">Spark Arena</a> </p>
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Install
uvx sparkrun setupOne command — installs sparkrun, then launches the guided setup wizard to create a cluster, configure SSH mesh, detect ConnectX-7 NICs, set up sudoers, and enable earlyoom.
Quick Start
# Run an inference workload
sparkrun run qwen3-1.7b-vllm
# Multi-node tensor parallelism (TP maps to node count on DGX Spark)
sparkrun run qwen3-1.7b-vllm --tp 2
# Re-attach to logs, stop a workload, check status
sparkrun logs qwen3-1.7b-vllm
sparkrun stop qwen3-1.7b-vllm
sparkrun statusCtrl+C detaches from logs — it never kills your inference job. Your model keeps serving.
See the full CLI reference for all commands and options.
Updating
sparkrun updateUpgrades sparkrun (when installed via uv tool) and refreshes recipe registries.
Update channels (advanced)
Opt into preview builds installed from git instead of PyPI:
sparkrun update --stable # PyPI stable release (default)
sparkrun update --beta # develop branch preview
sparkrun update --alpha # develop-next branch (bleeding edge)
sparkrun update --yolo # alias for --alphasparkrun update with no flag stays on your current channel; a channel flag switches and is remembered for future updates. The same flags work with sparkrun setup install and sparkrun setup update. Stable prints a plain version (0.2.40); beta/alpha add a channel suffix and commit (0.3.0-alpha+g1a2b3c4). Switching from a preview channel back to --stable may downgrade.
Highlights
- Multi-runtime — vLLM, SGLang, llama.cpp out of the box
- Multi-node tensor parallelism —
--tp 2= 2 hosts, automatic InfiniBand/RDMA detection - VRAM estimation — know if your model fits before you launch (
sparkrun show <recipe>) - Git-based recipe registries — we publish official recipes, community recipes, and benchmarked recipes via Spark Arena, plus you can add your own registries.
- Guided setup wizard — cluster creation, SSH mesh, CX7 auto-detection, sudoers, earlyoom
- Model & container distribution — syncs models and images to cluster nodes over SSH automatically
Spark Arena
Spark Arena is the community hub for DGX Spark recipe benchmarks — browse benchmark results, then run them directly with sparkrun.
Official Recipes
Official Recipes are maintained by the Spark Arena team and hosted on GitHub. They are tested and optimized for NVIDIA DGX Spark systems.
Community Recipes
Community Recipes are contributed by the community and hosted on GitHub.
Sponsored by
<a href="https://scitrera.ai"><img src="https://scitrera.com/logo2.png" alt="scitrera.ai" height="40" /></a>
License
Apache License 2.0 — see LICENSE for details.
Anonymous Telemetry
sparkrun sends basic anonymous usage telemetry to https://telemetry.sparkrun.dev by default. Events include a random installation id stored in ~/.config/sparkrun/config.yaml, sparkrun version, OS/version, system architecture, and command-specific metadata such as run runtime/model/parallelism/source/hardware counts, benchmark category/framework/profile/result keys, update version and registry counts, and setup-wizard step choices.
The data allows us to make informed decisions about new features for sparkrun or the greater DGX Spark ecosystem.
Telemetry very specifically does not include personally identifiable information or information that may reveal trade secrets. Telemetry does not include hostnames, usernames, local file paths, tokens, secrets, logs, private HF or local models, or full command arguments. Disable it persistently with sparkrun setup telemetry --disable, re-enable with sparkrun setup telemetry --enable, or opt out for one process with SPARKRUN_NO_TELEMETRY=1.
