mirror of
https://github.com/NVIDIA/dgx-spark-playbooks.git
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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/.
23 lines
1.6 KiB
Markdown
23 lines
1.6 KiB
Markdown
---
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name: dgx-spark-llama-cpp
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description: Build llama.cpp with CUDA and serve models via an OpenAI-compatible API (Gemma 4 31B IT as example) — on NVIDIA DGX Spark. Use when setting up llama-cpp on Spark hardware.
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---
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<!-- GENERATED:BEGIN from nvidia/llama-cpp/README.md -->
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# Run models with llama.cpp on DGX Spark
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> Build llama.cpp with CUDA and serve models via an OpenAI-compatible API (Gemma 4 31B IT as example)
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[llama.cpp](https://github.com/ggml-org/llama.cpp) is a lightweight C/C++ inference stack for large language models. You build it with CUDA so tensor work runs on the DGX Spark GB10 GPU, then load GGUF weights and expose chat through `llama-server`’s OpenAI-compatible HTTP API.
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This playbook walks through that stack end to end. As the model example, it uses **Gemma 4 31B IT** - a frontier reasoning model built by Google DeepMind that llama.cpp supports, with strengths in coding, agentic workflows, and fine-tuning. The instructions download its **F16** GGUF from Hugging Face. The same build and server steps apply to other GGUFs (including other sizes in the support matrix below).
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**Outcome**: You will build llama.cpp with CUDA for GB10, download a Gemma 4 31B IT model checkpoint, and run **`llama-server`** with GPU offload. You get:
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- Local inference through llama.cpp (no separate Python inference framework required)
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- An OpenAI-compatible `/v1/chat/completions` endpoint for tools and apps
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- A concrete validation that **Gemma 4 31B IT** runs on this stack on DGX Spark
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**Full playbook**: `/Users/jkneen/Documents/GitHub/dgx-spark-playbooks/nvidia/llama-cpp/README.md`
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<!-- GENERATED:END -->
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