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/.
1.3 KiB
| name | description |
|---|---|
| dgx-spark-trt-llm | Install and use TensorRT-LLM on DGX Spark — on NVIDIA DGX Spark. Use when setting up trt-llm on Spark hardware. |
TRT LLM for Inference
Install and use TensorRT-LLM on DGX Spark
NVIDIA TensorRT-LLM (TRT-LLM) is an open-source library for optimizing and accelerating large language model (LLM) inference on NVIDIA GPUs.
It provides highly efficient kernels, memory management, and parallelism strategies—like tensor, pipeline, and sequence parallelism—so developers can serve LLMs with lower latency and higher throughput.
TRT-LLM integrates with frameworks like Hugging Face and PyTorch, making it easier to deploy state-of-the-art models at scale.
Outcome: You'll set up TensorRT-LLM to optimize and deploy large language models on your DGX Spark, achieving significantly higher throughput and lower latency than standard PyTorch inference through kernel-level optimizations, efficient memory layouts, and advanced quantization.
Duration: 45-60 minutes for setup and API server deployment · Risk: Medium - container pulls and model downloads may fail due to network issues
Full playbook: /Users/jkneen/Documents/GitHub/dgx-spark-playbooks/nvidia/trt-llm/README.md