CKG-NVIDIA- AI - Nvidia Developer Stack As A Compressed Knowledge Graph - 20 Domains, 998 Nodes, Agents Traverse Type Dependency Graphs

Name: CKG-NVIDIA- AI - Nvidia Developer Stack As A Compressed Knowledge Graph - 20 Domains, 998 Nodes, Agents Traverse Type Dependency Graphs
Author: Yarmoluk

@Yarmoluk

Repository

About CKG-NVIDIA- AI - Nvidia Developer Stack As A Compressed Knowledge Graph - 20 Domains, 998 Nodes, Agents Traverse Type Dependency Graphs

NVIDIA AI developer stack as a Compressed Knowledge Graph (CKG) — 20 domains, 998 nodes, every prerequisite chain declared as typed edges. Agents traverse REQUIRES/ENABLES relationships instead of scanning docs. 4× F1 vs RAG, 11× fewer tokens. No API key required

#nvidia #knowledge-graph #mcp #ai-agents #cuda #nim #tensorrt

Basic information

Runtime

python

Transports

stdio

Publisher

Yarmoluk

Submitted by

Daniel Yarmoluk

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "nvidia-ai": {
      "command": "uvx",
      "args": [
        "ckg-nvidia-ai"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is CKG-NVIDIA-AI?

CKG-NVIDIA-AI is a read-only MCP server that provides a Compressed Knowledge Graph (CKG) covering the full NVIDIA AI developer stack across 20 domains and 998 nodes. It enables AI agents to traverse typed dependency graphs instead of using retrieval, achieving higher accuracy with drastically fewer tokens.

How to use CKG-NVIDIA-AI?

Install with pip install ckg-nvidia-ai and run the MCP server via uvx ckg-nvidia-ai. Configure it in Claude Desktop, Claude Code, Cursor, or other MCP clients using the provided JSON snippets. Tools include list_domains(), search_concepts(), query_ckg(), get_prerequisites(), and ask_nvidia() (the last requires Ollama with qwen2.5:14b).

Key features of CKG-NVIDIA-AI

4× F1 and 11× fewer tokens vs RAG
998 nodes across 20 NVIDIA AI domains
Deterministic graph traversal – no hallucination
Read-only: never writes, mutates, or executes
Typed edges (REQUIRES, ENABLES, RELATES_TO, IMPLEMENTS)
Three-state confidence (high, null, low) for every edge

Use cases of CKG-NVIDIA-AI

Deploying a real-time speech AI pipeline on NVIDIA Jetson
Mapping the full prerequisite chain for TensorRT-LLM on Hopper GPUs
Understanding Clara Parabricks dependencies for whole-genome sequencing
Building layered architecture maps with shared prerequisites across domains
Auditing deployment requirements before cold-start provisioning

FAQ from CKG-NVIDIA-AI

What is a Compressed Knowledge Graph (CKG) and how does it differ from retrieval?

A CKG is a layer that converts domain documentation into structured, agent-traversable knowledge. Instead of retrieval, the agent traverses declared relationships, which costs fewer tokens and does not guess.

Is CKG-NVIDIA-AI read-only?

Yes. All tools are read-only. The server never writes, mutates, or executes anything. Every response is a declared graph traversal.

What are the dependencies and runtime requirements?

The core server only requires the mcp[cli] package. For the ask_nvidia() tool,

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