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    onboarding

    • Connect Two DGX Stations for Distributed Workloads
    • MIG on DGX Station

    data science

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    tools

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    use case

    • Run NemoClaw with a Local LLM
    • DGX Station AI Skills and dgx-assist
    • Secure AI Agents with OpenShell
    • Local Coding Agent
    • Profiler-Driven Kernel Optimization for Fine-Tuning
    • Local Healthcare Agent on DGX Station

    inference

    • Serve LLMs with vLLM
    • Generate Images and Videos with ComfyUI
    • Isaac GR00T N1.6 Fine-Tuning
    • Serve LLMs with SGLang

    Local Coding Agent

    30 MINS

    Run local CLI coding agents with Claude Code and Ollama on DGX Station (NVIDIA GB300) using qwen3.6:27b

    • Claude Code
    • Coding
    • DGX Station
    • GB300
    • LLM
    • Ollama
    OverviewOverviewClaude CodeClaude CodeTroubleshootingTroubleshooting

    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

    Resources

    • Ollama Documentation
    • Qwen3.6 27B
    • Claude Code + Ollama Guide
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