AI and LLM Access with Twingate
Securely access remote AI models, LLM servers, and MCP servers using Twingate.
Use Cases
Twingate enables secure access to AI infrastructure for:
- Remote LLM Servers: Access private GPU servers running models like Ollama, vLLM, or other inference engines
- AI Coding Assistants: Connect tools like Continue.dev, Cursor, or Cody to your private LLM endpoints
- Model Context Protocol (MCP): Securely connect AI assistants to internal tools, data sources, and APIs
- Development Teams: Give distributed teams secure access to shared AI resources
- Cost Optimization: Run powerful models on centralized GPU infrastructure while maintaining security
Guides
Use the links below to explore specific use cases:
Remote LLM Access
Learn how to securely access remote Large Language Model servers running on private GPU infrastructure. This guide covers:
- Configuring LLM servers (like Ollama) for network access
- Setting up Twingate Resources for your GPU servers
- Connecting AI coding assistants to remote LLM endpoints
- Troubleshooting connectivity issues
Remote MCP Access
Learn how to securely access Model Context Protocol (MCP) servers that provide AI assistants with tools, resources, and prompts. This guide covers:
- Understanding the Model Context Protocol
- Deploying MCP servers on private networks
- Configuring AI assistants to connect through Twingate
- Security best practices for MCP deployments
Why Use Twingate for AI Infrastructure?
Security First
- Zero Trust Access: Only authorized users and devices can connect to your AI resources
- No Public Exposure: Keep LLM and MCP servers on private networks without public IP addresses
- Granular Controls: Use Groups and Security Policies to control who can access what
- Audit Trails: Monitor all connections through Twingate Analytics
Performance
- Low Latency: Optimized peer-to-peer connections for interactive AI experiences
- Split Tunneling: Only AI traffic goes through Twingate, other traffic uses direct internet
- Global Reach: Connect to AI resources from anywhere with minimal overhead
Simplicity
- Easy Setup: Deploy Connectors near your AI infrastructure in minutes
- No VPN Complexity: No client configuration files or network routing tables
- Works Everywhere: Compatible with all major AI tools and frameworks
Getting Started
To use Twingate with your AI infrastructure:
- Deploy a Connector on the same network as your AI servers
- Create Resources for your LLM or MCP server endpoints
- Grant Access to appropriate users or groups
- Configure your AI tools to connect through Twingate
- Monitor usage through the Twingate Admin Console
Additional Resources
- Twingate Architecture - Understand how Twingate works
- Security Policies - Implement advanced access controls
- Identity Firewall - Protocol-aware security for MCP and other services
- Service Accounts - Automate AI workloads with headless access
Have questions or need help? Check out the individual guides above or post on the Twingate Subreddit.
Last updated 13 days ago