Local LLMs automate GNS3 network topologies

Running coding-specific LLMs locally is becoming the standard for sensitive infrastructure automation where cost predictability and data privacy are paramount.

• –Qwen2.5-Coder 7B/14B excels at understanding hierarchical network configs and generating Python-based Netmiko or Nornir scripts.
• –RTX 4070 Ti users should target 4-bit quantized versions of 14B models to keep inference entirely on VRAM, avoiding the performance hit of system RAM offloading.
• –Agentic frameworks like Open Interpreter can turn these models into autonomous "network operators" that configure and verify GNS3 nodes in real-time.
• –Mixture-of-Experts (MoE) models like DeepSeek-Coder-V2-Lite provide a high-logic, low-VRAM alternative for complex multi-vendor environments.
• –32GB of system RAM is the optimal range for running a local LLM alongside a medium-sized GNS3 lab with several virtual routers.