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p620 — AMD Workstation

The primary machine: daily development, local AI inference, and the binary cache that speeds up the other hosts.

Profile workstation
GPU AMD Radeon RX 7900 — amdgpu driver, ROCm acceleration
Displays DP-1 and DP-2, both 2560×1440@120
NFS exports /extdisk to 192.168.1.*
Boot standard
Source hosts/p620/

Why this host is shaped the way it is

p620 has the only GPU in the fleet suitable for ML work (Radeon RX 7900 + ROCm), so it is the local AI host: it runs Ollama against the GPU and fronts it with a LiteLLM router. It is also powerful and always-on, which makes it the natural binary cache server — the other hosts pull builds from it instead of rebuilding.

What runs here

AI stack

  • Ollama server with ROCm acceleration — local model inference.
  • LiteLLM router — a unified OpenAI-compatible endpoint in front of local and cloud models.
  • AI providers — OpenAI, Anthropic, and Gemini (keys via agenix), with Ollama as the local provider.
  • claude-router CLI, claude-hooks, and the PARR protocol tooling.

Development & virtualisation

  • Full development environment (editors, languages, git tooling).
  • Docker virtualisation enabled.
  • Syncthing for file sync.
  • Email with mbsync and AI-assisted features + notifications.

Desktop & extras

  • Desktop environment (COSMIC disabled; Hyprland/GNOME stack).
  • glance dashboard, scrcpy over Wi-Fi, temperature dashboard.
  • Media tooling including the yt-x terminal YouTube browser.

Networking

  • Tailscale mesh; the local firewall is left to Tailscale's trust model.
  • NFS export of /extdisk to the LAN.

Hardware notes

p620 carries a few hardware-specific tweaks under hosts/p620/nixos/:

  • AMD/ROCm setup (amd.nix), CPU and memory tuning, power.
  • An AQC107 NIC link pin and a USB power fix.
  • VFIO setup and DNS fallback for resilience.

For the exact files, see the p620 manifest.

Deploy

just test-host p620      # build without switching
just quick-deploy p620   # deploy only if the build changed
just p620                # optimised deploy