Local Coding Agent

Basic idea

Use Ollama on DGX Station (NVIDIA GB300) to run a local coding model and connect a CLI coding agent. This playbook uses Claude Code with ollama launch so you can work without external cloud APIs.

The DGX Station GPU (reported as NVIDIA GB300 in nvidia-smi) provides ample memory to run qwen3.6:27b with Ollama for local coding-agent workflows.

CLI agent

This playbook uses Claude Code as the CLI agent, connected to a local Ollama model for inference.

What you'll accomplish

You will run qwen3.6:27b on your DGX Station (NVIDIA GB300) with Ollama, connect Claude Code to it, and complete a small coding task end-to-end.

What to know before starting

• Comfort with Linux command line basics
• Experience running terminal-based tools and editors
• Familiarity with Python for the short coding task

Prerequisites

• DGX Station with NVIDIA GB300 (Grace Blackwell) and NVIDIA driver; nvidia-smi typically shows "NVIDIA GB300"
• Internet access to download model weights
• Ollama 0.15.0 or newer
• GPU memory on GB300 supports the recommended qwen3.6:27b model
• Disk space for the qwen3.6:27b model download

Time & risk

• Duration: ~20–30 minutes (includes model download)
• Risk level: Low
  • Large model downloads can fail if network connectivity is unstable
  • Older Ollama versions will not load newer models
• Rollback: Stop Ollama and delete the downloaded model from ~/.ollama/models
• Last Updated: 06/12/2026
  • Model path set to qwen3.6:27b with ollama launch; Python task now uses a virtual environment