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Split-GPU Architecture

Run LLM on GPU 0 + Graphistry on GPU 1.

Architecture

flowchart LR
  A[GPU 0: llama-server<br/>GGUF inference] --> B[GPU 1: RAPIDS + Graphistry<br/>Visualization]

Configuration

from llcuda.graphistry import SplitGPUManager, GraphWorkload

# Assign GPUs (Kaggle dual T4 defaults)
manager = SplitGPUManager()
manager.assign_llm(gpu_id=0)
manager.assign_graph(gpu_id=1)

# LLM workload (GPU 0)
llm_env = manager.get_llm_env()

# Graph workload (GPU 1)
graph_env = manager.get_graph_env()
workload = GraphWorkload(gpu_id=1)

Use Cases

  1. Knowledge Graph Extraction
  2. LLM generates entities/relationships

  3. Graphistry visualizes graphs

  4. Interactive Analysis

  5. LLM answers questions

  6. Graphistry shows data patterns

  7. Multi-Modal Workflows

  8. Text generation (GPU 0)
  9. Graph analytics (GPU 1)