sparkrun 0.2.40

Launch and manage Docker-based inference workloads on NVIDIA DGX Spark systems

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

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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.

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.