One command to rule them all
Launch, manage, and stop LLM inference workloads on one or more NVIDIA DGX Spark systems — no Slurm, no Kubernetes, no fuss.
Documentation · Quick Start · Recipes · Spark Arena
Install
uvx sparkrun setup
One 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 status
Ctrl+C detaches from logs — it never kills your inference job. Your model keeps serving.
Watched launches also report Docker-start TTR and TTFT using a rank-local streaming readiness check for Docker vLLM/SGLang launches. See startup readiness for configuration, timing boundaries, per-recipe overrides, and execution-strategy integration.
See the full CLI reference for all commands and options.
Updating
sparkrun update
Upgrades 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 --alpha
sparkrun 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
License
Apache License 2.0 — see LICENSE for details.
The bundled SparkRoute integration is AGPL-3.0-only with an additional permission for combining and distributing it with SparkRun. Its notices and immutable source provenance ship in the installed plugin package. The independently acquired SparkRoute executable is AGPL-3.0-only. SparkRoute defaults on for alpha; LiteLLM defaults on for stable and beta. See channel defaults and managed plugin updates.
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.
1 maintainer
Metadata
1 maintainer
Release files for sparkrun 0.3.9
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sparkrun-0.3.9.tar.gz | 2.3 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sparkrun-0.3.9-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 3.8 MB
Release files / sparkrun-0.3.9.tar.gz
| Download URL | sparkrun-0.3.9.tar.gz |
|---|---|
| Size | 2.3 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
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Signed by GitHub Actions, verified by PyPI on Sep 13, 2026.
Transparency logRelease files / sparkrun-0.3.9-py3-none-any.whl
| Download URL | sparkrun-0.3.9-py3-none-any.whl |
|---|---|
| Size | 1.5 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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|
BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 13, 2026.
Transparency log
