GPU Go - Remote GPU Management for VS Code
Use GPU Like NFS - Seamlessly manage AI/ML development environments with remote GPU access directly from VS Code.
✨ Features
🖥️ Studio Environments
Create and manage AI development studio environments with one-click remote GPU access:
- One-Click Creation - Spin up GPU-powered environments instantly
- SSH Integration - Connect directly from VS Code terminal
- Pre-configured Images - PyTorch, TensorFlow, Jupyter, and more
- Automatic SSH Config - No manual configuration needed
🔧 GPU Workers
View and manage GPU workers across your infrastructure:
- List all available workers and their status
- Monitor active connections in real-time
- Create new workers via CLI or web dashboard
- Quick access to worker details and logs
📊 Device Management
Comprehensive view of all your GPU devices:
- View GPU specifications (model, VRAM, driver version)
- Monitor device availability and utilization
- Quick access to detailed device information
- Multi-GPU support
🚀 Getting Started
Step 1: Install the Extension
Install from the VS Code Marketplace or search for "GPU Go" in VS Code Extensions.
Step 2: Login
- Click on the GPU Go icon in the Activity Bar
- Click "Login to GPU Go"
- Generate a Personal Access Token (PAT) from the dashboard
- Paste the token in VS Code
Step 3: Setup Your GPU Server
On your GPU server, install and start the ggo agent:
# Install ggo CLI
curl -fsSL https://cdn.tensor-fusion.ai/gpugo/install.sh | sh
# Login to your account
ggo login
# Start the agent
ggo agent start
Step 4: Create a Worker
Create a worker to share GPU resources:
ggo worker create --agent-id <your-agent-id> --name my-worker --gpu-ids 0
Step 5: Create a Studio Environment
Use the "Create Studio" button in the extension to create a new development environment with remote GPU access.
📋 Commands
| Command |
Description |
GPU Go: Login |
Login to GPU Go platform |
GPU Go: Logout |
Logout from GPU Go platform |
GPU Go: Create Studio Environment |
Create a new studio environment |
GPU Go: Refresh Studio |
Refresh studio list |
GPU Go: Refresh Workers |
Refresh workers list |
GPU Go: Refresh Devices |
Refresh devices list |
GPU Go: Open Dashboard |
Open GPU Go web dashboard |
⚙️ Configuration
| Setting |
Default |
Description |
gpugo.serverUrl |
https://tensor-fusion.ai |
GPU Go API server URL |
gpugo.dashboardUrl |
https://go.tensor-fusion.ai |
GPU Go dashboard URL |
gpugo.cliPath |
(auto-detected) |
Path to ggo CLI binary |
gpugo.autoRefreshInterval |
30 |
Auto-refresh interval in seconds (0 to disable) |
gpugo.autoDownloadCli |
true |
Automatically download CLI on first install |
📦 Requirements
- VS Code 1.85.0 or higher
- ggo CLI - Auto-downloaded or install manually
- Internet connection for API access
🔗 Links
📄 License
Proprietary - Copyright © 2026 NexusGPU PTE. LTD. All rights reserved.
See LICENSE for details.
