Your remote GPU
Your GPU should not be tied to your desk.
Leave the workstation at home or work, use a suitable laptop anywhere connectivity allows, and reach your own model runtime through SSH.
Your GPU. Your AI. Your Control. Anywhere. · Created by Paladugu Ganesh Naidu
Architectural flow
One developer interface. One chosen inference destination.
This is a conceptual diagram of a supported SSH-model configuration, not a claim of automatic remote-host provisioning. Inference occurs on the selected model host; ordinary workspace actions remain subject to the local extension’s permissions.
Prepare the workstation
Use supported operating-system, driver and model-runtime combinations. Install Ollama, download the model, verify that it loads on the intended GPU, and keep its inference endpoint limited to the appropriate private or loopback interface.
Connect securely
Set up SSH access using individual credentials, correctly verified host fingerprints and restrictive firewall rules. Use a properly configured VPN or Tailscale if you need private reachability across networks. Never expose an unauthenticated model API to the internet.
Configure TuxNest
Choose the user-managed remote Ollama connection, enter only the required host details, authenticate using the supported SSH mechanism and select a model the host actually serves. Follow the extension’s documented connection UI rather than assuming an undocumented one-click installation feature.
Work and verify
Send the selected prompt and included context over the approved inference path. Review proposed local workspace actions; examine real build, test and command results. Remote inference does not automatically relocate your VS Code terminal.
Sharing compute with others
An authorized team can potentially share one managed model runtime, but every concurrent generation competes for GPU compute and memory bandwidth. Measure real tokens per second, queuing, latency and session isolation for your exact workload.
Continue exploring TuxNest
Our tagline is “Tuxnest is India's first plug-and-play hybrid AI agent ecosystem”. Self-hosted inference requires appropriate host security, model capacity, power and networking; it does not establish automatic legal compliance.