Text to Knowledge Graph on DGX Station

Basic idea

This playbook demonstrates how to build and deploy a comprehensive knowledge graph generation and visualization solution that serves as a reference for knowledge graph extraction. The GB300 Ultra's massive GPU memory enables running the Llama 3.1 405B model, producing the highest-quality knowledge graphs and delivering superior downstream GraphRAG performance.

This txt2kg playbook transforms unstructured text documents into structured knowledge graphs using:

• Knowledge Triple Extraction: Using Ollama with GPU acceleration for local LLM inference to extract subject-predicate-object relationships
• Graph Database Storage: ArangoDB for storing and querying knowledge triples with relationship traversal
• GPU-Accelerated Visualization: Three.js WebGPU rendering for interactive 2D/3D graph exploration

Future Enhancements: Vector embeddings and GraphRAG capabilities are planned enhancements.

What you'll accomplish

You will have a fully functional system capable of processing documents, generating and editing knowledge graphs, and providing querying, accessible through an interactive web interface. The setup includes:

• Local LLM Inference: Ollama for GPU-accelerated LLM inference with no API keys required
• Graph Database: ArangoDB for storing and querying triples with relationship traversal
• Interactive Visualization: GPU-accelerated graph rendering with Three.js WebGPU
• Modern Web Interface: Next.js frontend with document management and query interface
• Fully Containerized: Reproducible deployment with Docker Compose and GPU support

What to know before starting

• Basic Docker container usage
• Familiarity with command line operations
• Understanding of knowledge graphs (helpful but not required)

Prerequisites

• NVIDIA DGX Station with GB300 Ultra Blackwell GPU
• Docker installed and configured with NVIDIA Container Toolkit
• Docker Compose
• Network access for container image downloads

Ancillary files

All required assets are in the playbook directory nvidia/station-txt2kg/assets (see Instructions, Step 1). Key files:

• start.sh - Launch script for all services
• stop.sh - Stop script to shut down services
• deploy/compose/ - Docker Compose configurations

Time & risk

• Duration:

  • 2-3 minutes for initial setup and container deployment
  • 5-10 minutes for Ollama model download (depending on model size)
  • Immediate document processing and knowledge graph generation

• Risks:

  • GPU memory requirements depend on chosen Ollama model size
  • Document processing time scales with document size and complexity

• Rollback: Stop and remove Docker containers, delete downloaded models if needed

• Last Updated: 03/02/2026
  • First Publication