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spark cli
One bash script to set up and serve LLMs on DGX Spark
A single Bash script that automates the full DGX Spark lifecycle from your laptop. One command to set up everything (Docker, NGC, Tailscale, SSH, vLLM). One command to serve any model with auto-detected optimal flags (e.g. reasoning), tool-calling, context, multimodal, MoE, GPU memory. Zero dependencies: just bash and curl.
Hey everyone! 👋
I recently got an Asus Ascent GX10 (DGX Spark) and quickly realized that going from unboxing to actually serving an LLM was a surprisingly manual process: system updates, GPU checks, Docker, NGC auth, HuggingFace, Tailscale, SSH keys, vLLM container config…
I work from a MacBook Pro, so I also needed reliable remote access from day one.
After going through all of that, I figured every DGX Spark owner would face the exact same steps, so I automated the entire thing into a single Bash script.
What I'm most proud of is the auto-profiler: instead of figuring out the right vLLM flags for each model, spark reads the model's config.json and calculates everything automatically, tool-call support, context window, multimodal detection, MoE architecture, and memory utilization based on your actual VRAM.
The whole thing is zero-dependency (just bash + curl), remote-first (Tailscale built in), and auto-updating.
I'd love feedback from anyone running local LLMs, especially other DGX Spark owners.
What's missing? What would make your workflow easier? PRs are very welcome.
About spark cli on Product Hunt
“One bash script to set up and serve LLMs on DGX Spark”
spark cli was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #131 on the daily leaderboard. A single Bash script that automates the full DGX Spark lifecycle from your laptop. One command to set up everything (Docker, NGC, Tailscale, SSH, vLLM). One command to serve any model with auto-detected optimal flags (e.g. reasoning), tool-calling, context, multimodal, MoE, GPU memory. Zero dependencies: just bash and curl.
On the analytics side, spark cli competes within Open Source, Developer Tools and GitHub — topics that collectively have 622.6k followers on Product Hunt. The dashboard above tracks how spark cli performed against the three products that launched closest to it on the same day.
Who hunted spark cli?
spark cli was hunted by Massimo Angelini. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of spark cli including community comment highlights and product details, visit the product overview.
Massimo Angelini