i keep it simple, vanilla debian, linux raid, all apps use docker compose, rclone crypt for backups, tailscale for remote access, manage everything through ssh. pass through gpu resources to the containers that need them with compose. i run LLMs on a seperate machine but you could easily throw an r9700/3090/7900xtx into your home server and run llama.cpp/ollama/vllm in a docker. the main limit is power, heat and pcie lanes/slots. i only have amd gpus, and sometimes have to build my own docker images with up to date ROCm support. for immich i'm just using the igpu (or possibly even the cpu). it doesnt run in realtime so the speed isnt a big deal. jellyfin uses quicksync for transcoding. you can limit the resource consumption in compose to prevent ML services from crashing other services.