a680d0472b
Adds a Claude Code plugin structure that exposes each NVIDIA DGX Spark
playbook as a triggerable skill, with an index skill ('dgx-spark') that
routes users to the right leaf based on intent and encodes the
relationship graph between playbooks (prerequisites, alternatives,
composes-with, upgrade paths).
Structure:
- overrides/*.md hand-curated frontmatter + Related sections
- scripts/generate.mjs zero-dep Node generator: nvidia + overrides → skills
- scripts/install.sh symlinks skills into ~/.claude/skills (--plugin mode available)
- skills/ committed, browsable, installable without Node
- .github/workflows/ auto-regenerates skills/ when playbooks/overrides change
Initial curated leaves: ollama, open-webui, vllm, connect-to-your-spark.
Remaining 37 leaves use generator fallback (title + tagline + summary
extracted from README) and can be curated incrementally via overrides/.
1.0 KiB
| name | description |
|---|---|
| dgx-spark-sglang | Install and use SGLang on DGX Spark — on NVIDIA DGX Spark. Use when setting up sglang on Spark hardware. |
SGLang for Inference
Install and use SGLang on DGX Spark
SGLang is a fast serving framework for large language models and vision language models that makes your interaction with models faster and more controllable by co-designing the backend runtime and frontend language. This setup uses the optimized NVIDIA SGLang NGC Container on a single NVIDIA Spark device with Blackwell architecture, providing GPU-accelerated inference with all dependencies pre-installed.
Outcome: You'll deploy SGLang in both server and offline inference modes on your NVIDIA Spark device, enabling high-performance LLM serving with support for text generation, chat completion, and vision-language tasks using models like DeepSeek-V2-Lite.
Full playbook: /Users/jkneen/Documents/GitHub/dgx-spark-playbooks/nvidia/sglang/README.md