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

Table of Contents

• Overview
  • What You'll Accomplish
  • Prerequisites
  • Time & risk
• Instructions
• Troubleshooting

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Overview

Basic idea

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.
This guide uses Ollama with GPT-OSS 120B to provide easy deployment of a coding assistant to VSCode. Included is advanced instructions to allow DGX Spark and Ollama to provide the coding assistant to be available over your local network. This guide is also written on a fresh installation of the OS. If your OS is not freshly installed and you have issues, see the troubleshooting tab.

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 and VSCode integration.
• Hosting large LLMs like GPT-OSS 120B using unified memory.

Prerequisites

• DGX Spark (128GB unified memory recommended)
• Ollama and an LLM of your choice (e.g., gpt-oss:120b)
• VSCode
• Continue VSCode extension
• Internet access for model downloads
• Basic familiarity with opening the Linux terminal, copying and pasting commands.
• Having sudo access.
• Optional: firewall control for remote access configuration

Time & risk

• Duration: About 30 minutes
• Risks: Data download slowness or failure due to network issues
• Rollback: No permanent system changes made during normal usage.
• Last Updated: 10/21/2025
  • First publication

Instructions

Step 1. Install Ollama

Install the latest version of Ollama using the following command:

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

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), 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

Verify that the workstation can connect to your DGX Spark's Ollama server:

curl -v http://YOUR_SPARK_IP:11434/api/version

Replace YOUR_SPARK_IP with your DGX Spark's IP address. If the connection fails please see the Troubleshooting tab.

Step 3. Install VSCode

For DGX Spark (ARM-based), download and install VSCode: Navigate to https://code.visualstudio.com/download and download the Linux ARM64 version of VSCode. After the download completes note the downloaded package name. Use it in the next command in place of DOWNLOADED_PACKAGE_NAME.

sudo dpkg -i DOWNLOADED_PACKAGE_NAME

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:

• Go to Extensions view in VSCode
• Search for Continue published by Continue.dev and install the extension. After installation, click the Continue icon on the right-hand bar.

Step 5. Local Inference Setup

• Click Or, configure your own models
• Click Click here to view more providers
• Choose Ollama as the Provider
• For Model, select Autodetect
• Test inference by sending a test prompt.

Your downloaded model will now be the default (e.g., gpt-oss:120b) for inference.

Step 6. Setting up a Workstation to Connect to the DGX Spark' Ollama Server

To connect a workstation running VSCode to a remote DGX Spark instance the following must be completed on that workstation:

• Install Continue as instructed in Step 4
• Click on the Continue icon on the left pane
• Click Or, configure your own models
• Click Click here to view more providers
• Select Ollama as the Provider
• Select Autodetect as the Model.

Continue will fail to detect the model as it is attempting to connect to a locally hosted Ollama server.

• Find the gear icon in the upper right corner of the Continue window and click on it.
• On the left pane, click Models
• Next to the first dropdown menu under Chat click the gear icon.
• Continue's config.yaml will open. Take note of your DGX Spark's IP address.
• Replace the configuration with the following. YOUR_SPARK_IP should be replaced with your DGX Spark's IP.

name: Config
version: 1.0.0
schema: v1

assistants:
  - name: default
    model: OllamaSpark

models:
  - name: OllamaSpark
    provider: ollama
    model: gpt-oss:120b
    apiBase: http://YOUR_SPARK_IP:11434
    title: gpt-oss:120b
    roles:
      - chat
      - edit
      - autocomplete

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

Symptom	Cause	Fix
Ollama not starting	GPU drivers may not be installed correctly	Run nvidia-smi in the terminal. If the command fails check DGX Dashboard for updates to your DGX Spark.
Continue can't connect over the network	Port 11434 may not be open or accessible	Run command ss -tuln | grep 11434. If the output does not reflect tcp LISTEN 0 4096 *:11434 *:* , go back to step 2 and run the ufw command.
Continue can't detect a locally running Ollama model	Configuration not properly set or detected	Check OLLAMA_HOST and OLLAMA_ORIGINS in /etc/systemd/system/ollama.service.d/override.conf file. If OLLAMA_HOST and OLLAMA_ORIGINS are set correctly, add these lines to your ~/.bashrc file.
High memory usage	Model size too big	Confirm no other large models or containers are running with nvidia-smi. Use smaller models such as gpt-oss:20b for lightweight usage.

Note

DGX Spark uses a Unified Memory Architecture (UMA), which enables dynamic memory sharing between the GPU and CPU. With many applications still updating to take advantage of UMA, you may encounter memory issues even when within the memory capacity of DGX Spark. If that happens, manually flush the buffer cache with:

sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches'