dgx-spark-playbooks/nvidia/vibe-coding

Vibe Coding in VS Code

Use DGX Spark as a local or remote Vibe Coding assistant with Ollama and Continue.dev

Table of Contents

• Overview
  • What You'll Accomplish
  • Prerequisites
  • Requirements
• Instructions
• Troubleshooting

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Overview

DGX Spark Vibe Coding

This playbook walks you through setting up DGX Spark as a Vibe Coding assistant — locally or as a remote coding companion for VSCode with Continue.dev.
While NVIDIA NIMs are not yet widely supported, this guide uses Ollama with GPT-OSS 120B to provide a high-performance local LLM environment.

What You'll Accomplish

You'll have a fully configured DGX Spark system capable of:

• Running local code assistance through Ollama.
• Serving models remotely for Continue.dev and VSCode integration.
• Hosting large LLMs like GPT-OSS 120B using unified memory.

Prerequisites

• DGX Spark (128GB unified memory recommended)
• Internet access for model downloads
• Basic familiarity with the terminal
• Optional: firewall control for remote access configuration

Requirements

• Ollama and an LLM of your choice (e.g., gpt-oss:120b)
• VSCode
• Continue.dev VSCode extension

Instructions

Step 1. Install Ollama

Install the latest version of Ollama using the following command:

curl -fsSL https://ollama.com/install.sh | sh

Start the Ollama service:

ollama serve

Once the service is running, pull the desired model:

ollama pull gpt-oss:120b

Step 2. (Optional) Enable Remote Access

To allow remote connections (e.g., from a workstation using VSCode and Continue.dev), modify the Ollama systemd service:

sudo systemctl edit ollama

Add the following lines beneath the commented section:

[Service]
Environment="OLLAMA_HOST=0.0.0.0:11434"
Environment="OLLAMA_ORIGINS=*"

Reload and restart the service:

sudo systemctl daemon-reload
sudo systemctl restart ollama

If using a firewall, open port 11434:

sudo ufw allow 11434/tcp

Step 3. Install VSCode

For DGX Spark (ARM-based), download and install VSCode:

wget https://code.visualstudio.com/sha/download?build=stable&os=linux-deb-arm64 -O vscode-arm64.deb
sudo apt install ./vscode-arm64.deb

If using a remote workstation, install VSCode appropriate for your system architecture.

Step 4. Install Continue.dev Extension

Open VSCode and install Continue.dev from the Marketplace.
After installation, click the Continue icon on the right-hand bar.

Skip login and open the manual configuration via the gear (⚙️) icon.
This opens config.yaml, which controls model settings.

Step 5. Local Inference Setup

• In the Continue chat window, use Ctrl/Cmd + L to focus the chat.
• Click Select Model → + Add Chat Model
• Choose Ollama as the provider.
• Set Install Provider to default.
• For Model, select Autodetect.
• Click Connect.

You can now select your downloaded model (e.g., gpt-oss:120b) for local inference.

Step 6. Remote Setup for DGX Spark

To connect Continue.dev to a remote DGX Spark instance, edit config.yaml in Continue and add:

models:
  - model: gpt-oss:120b
    title: gpt-oss:120b
    apiBase: http://YOUR_SPARK_IP:11434/
    provider: ollama

Replace YOUR_SPARK_IP with the IP address of your DGX Spark.
Add additional model entries for any other Ollama models you wish to host remotely.

Troubleshooting

Common Issues

1. Ollama not starting

• Verify Docker and GPU drivers are installed correctly.
• Run ollama serve manually to view errors.

2. VSCode can't connect

• Ensure port 11434 is open and accessible from your workstation.
• Check OLLAMA_HOST and OLLAMA_ORIGINS in /etc/systemd/system/ollama.service.d/override.conf.

3. High memory usage

• Use smaller models such as gpt-oss:20b for lightweight usage.
• Confirm no other large models or containers are running with nvidia-smi.