Trust boundaries
Know where your code context goes.
Model location is part of privacy. The complete development toolchain—not simply SSH—determines where information travels.
Your GPU. Your AI. Your Control. Anywhere. · Created by Paladugu Ganesh Naidu
Three model destinations
A local Ollama server processes included context on your own machine. An SSH-managed remote Ollama host processes selected prompts on the host you choose. Any compatible external endpoint you configure may send data to its operator under that service’s terms.
Other network paths still matter
Web search, MCP servers, extensions, model downloads, telemetry, crash reports, logging systems and external tools can transfer information independently of the selected model endpoint. Evaluate each enabled integration.
Security practices
Use trusted machines, SSH key protection, host verification, least-privilege accounts, firewall restrictions, OS updates, controlled backups and clear prompt-retention policies. Remove secrets and unnecessary customer data from prompts.
A realistic privacy promise
Properly configured private inference can reduce exposure to third-party model providers. It cannot establish absolute secrecy, an air gap or compliance without verification of every endpoint, tool and storage location.
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.