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/.
1.2 KiB
| name | description |
|---|---|
| dgx-spark-multi-modal-inference | Setup multi-modal inference with TensorRT — on NVIDIA DGX Spark. Use when setting up multi-modal-inference on Spark hardware. |
Multi-modal Inference
Setup multi-modal inference with TensorRT
Multi-modal inference combines different data types, such as text, images, and audio, within a single model pipeline to generate or interpret richer outputs.
Instead of processing one input type at a time, multi-modal systems have shared representations that text-to-image generation, image captioning, or vision-language reasoning.
On GPUs, this enables parallel processing across modalities for faster, higher-fidelity results for tasks that combine language and vision.
Outcome: You'll deploy GPU-accelerated multi-modal inference capabilities on NVIDIA Spark using TensorRT to run Flux.1 and SDXL diffusion models with optimized performance across multiple precision formats (FP16, FP8, FP4).
Duration: 45-90 minutes depending on model downloads and optimization steps
Full playbook: /Users/jkneen/Documents/GitHub/dgx-spark-playbooks/nvidia/multi-modal-inference/README.md