Small Engineering Team Self-Hosts AI Coding Assistant on Shared GPU to Cut Costs

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A ten-person engineering team built an in-house AI coding assistant by running the open-source Qwen model on a single shared GPU workstation they already owned, avoiding per-seat cloud API fees and keeping their codebase off third-party servers. The setup uses Ollama to serve the model, Tailscale to create a secure private mesh network connecting all developer laptops, and the Qwen Code CLI as the terminal interface on each machine. The GPU box handles only inference compute and never accesses developers' local files, with each engineer working in their own repository as usual. The team encountered practical challenges such as silent request queuing under heavy load, accidental model evictions when someone swapped models, and the need for a defined service startup order after reboots. Their key operational lesson was standardizing a single curl-based health check command, which allowed anyone to quickly diagnose connectivity issues without escalating to the system administrator.

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