Hyperbolic AI Cloud

Manage GPU instances from Hyperbolic directly within your VS Code / Cursor editor.

Create instances, connect via SSH, run Jupyter Notebook servers, and serve large language models with vLLM seamlessly.

Check out the demos on X

Setup

Prerequisites

Before using this extension, you need to install these required extensions:

1. Jupyter Extension - For running Jupyter notebooks

   • Install from VSCode/Cursor marketplace: ms-toolsai.jupyter

2. Remote - SSH Extension - For SSH connections to GPU instances

   • For VSCode: Install ms-vscode-remote.remote-ssh
   • For Cursor: Install anysphere.remote-ssh

Getting a Hyperbolic Account and API Token

1. Register for a Hyperbolic account:

   • Visit https://app.hyperbolic.xyz/
   • Create an account or log in to your existing account
   • Verify your email address

2. Deposit funds into your account:

   • Log in to your Hyperbolic application
   • Navigate to the "Billing" tab
   • Select how much you want to deposit (we suggest starting with $25)
   • Click Pay Now
   • Follow the instructions to add funds to your account
   • Note that you will need sufficient funds to rent GPU instances

3. Generate an API token:

   • In your Hyperbolic dashboard, navigate to "Settings"
   • Navigate to the API Key section
   • Copy the generated token and keep it secure

4. Add your SSH public key:

   • Generate an SSH key pair if you don't already have one
   • In https://app.hyperbolic.xyz/, navigate to the "Settings" section
   • Scroll down to the SSH Public Key section
   • Paste your public key (usually from ~/.ssh/id_rsa.pub or similar)
   • Click the save icon

Extension Configuration

After installing the extension, configure the following settings in VSCode/Cursor:

1. Open Command Palette (Ctrl+Shift+P / Cmd+Shift+P)
2. Type "Preferences: Open Settings (UI)"
3. Search for "hyperbolic"
4. Configure your API key, SSH Private Key path, and Huggingface token.

Features

• Instance Management: List available GPU machines, create new instances, and terminate existing ones
• SSH Integration: Connect to instances directly via SSH with automatic configuration
• Jupyter Support: Run Jupyter Notebook servers on instances with automatic tunnel setup
• vLLM Model Serving: Deploy and serve large language models using vLLM on your GPU instances
• Sidebar Integration: Dedicated Hyperbolic sidebar showing account details, available machines, and your instances
• Seamless Workflow: Manage everything from within VSCode/Cursor without switching contexts

Usage

Command Palette Access and Sidebar Integration

Access all commands via Command Palette (Ctrl+Shift+P / Cmd+Shift+P) or through the extension sidebar

Jupyter Notebook Support

When creating a new instance, you can choose to expose a port for Jupyter. This will automatically configure the instance with a public URL for accessing Jupyter.

The extension handles all the necessary setup, including:

• Automatic installation of Python and pip if needed
• Jupyter Notebook installation and configuration
• Port management and process cleanup
• Secure access via SSH tunnel or public URL

vLLM Model Serving

The extension includes comprehensive support for serving large language models using vLLM:

Features:

• Model Selection: Choose any Hugging Face model compatible with vLLM
• Gated Model Support: Automatic authentication with Hugging Face for gated models
• Custom Tokenizers: Optional tokenizer mode configuration (e.g., "mistral" for Mistral models)
• Setup Options: Choose between live terminal setup (with real-time monitoring) or automated setup
• Public URLs: Automatic public URL generation for easy API access

How to Use vLLM:

1. Select an active GPU instance or create a new one with exposed ports
2. Run Hyperbolic: Serve vLLM Model from the Command Palette
3. Enter the Hugging Face model name (e.g., deepseek-ai/DeepSeek-R1-0528)
4. Configure optional tokenizer mode if needed
5. Specify if the model is gated (requires Hugging Face authentication)
6. Choose your setup method (live terminal or automated)
7. The extension will handle all installation and configuration

Example API Usage: Once vLLM is running, you can access it via REST API:

# Test the server
curl http://your-instance-url/v1/models

# Generate text
curl http://your-instance-url/v1/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "your-model-name",
        "prompt": "San Francisco is a",
        "max_tokens": 7,
        "temperature": 0
    }'

Security Settings

The extension provides configurable SSH security options:

{
  "hyperbolic.sshStrictHostKeyChecking": false
}

Important Security Note: By default, SSH strict host key checking is disabled for convenience. This means SSH connections will automatically accept unknown hosts without verification.

To enable strict host key checking for better security:

1. Open VSCode/Cursor settings (Cmd+, or Ctrl+,)
2. Search for "hyperbolic ssh"
3. Enable "SSH Strict Host Key Checking"

Recommendation:

• For development/testing environments: Keep disabled for convenience
• For production or sensitive work: Enable strict host key checking

Requirements

• VSCode 1.85.0 or higher
• A valid Hyperbolic API key
• SSH client installed on your system
• SSH private key configured
• Jupyter Extension installed
• Remote - SSH Extension installed
• Python 3.x (on the remote instance, will be installed automatically if needed)
• Hugging Face account and token (optional, for gated models)

License

Proprietary