mirror of
https://github.com/NVIDIA/dgx-spark-playbooks.git
synced 2026-04-26 20:03:52 +00:00
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/.
24 lines
1.3 KiB
Markdown
24 lines
1.3 KiB
Markdown
---
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name: dgx-spark-trt-llm
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description: Install and use TensorRT-LLM on DGX Spark — on NVIDIA DGX Spark. Use when setting up trt-llm on Spark hardware.
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---
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<!-- GENERATED:BEGIN from nvidia/trt-llm/README.md -->
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# TRT LLM for Inference
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> Install and use TensorRT-LLM on DGX Spark
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**NVIDIA TensorRT-LLM (TRT-LLM)** is an open-source library for optimizing and accelerating large language model (LLM) inference on NVIDIA GPUs.
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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.
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TRT-LLM integrates with frameworks like Hugging Face and PyTorch, making it easier to deploy state-of-the-art models at scale.
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**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
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inference through kernel-level optimizations, efficient memory layouts, and advanced quantization.
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Duration: 45-60 minutes for setup and API server deployment · Risk: Medium - container pulls and model downloads may fail due to network issues
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**Full playbook**: `/Users/jkneen/Documents/GitHub/dgx-spark-playbooks/nvidia/trt-llm/README.md`
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<!-- GENERATED:END -->
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