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Cake day: July 23rd, 2023

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  • I’ve been testing Ollama in Docker/WSL with the idea that if I like it I’ll eventually move my GPU into my home server and get an upgrade for my gaming pc. When you run a model it has to load the whole thing into VRAM. I use the 8gb models so it takes 20-40 seconds to load the model and then each response is really fast after that and the GPU hit is pretty small. After I think five minutes by default it will unload the model to free up VRAM.

    Basically this means that you either need to wait a bit for the model to warm up or you need to extend that timeout so that it stays warm longer. That means that I cannot really use my GPU for anything else while the LLM is loaded.

    I haven’t tracked power usage, but besides the VRAM requirements it doesn’t seem too intensive on resources, but maybe I just haven’t done anything complex enough yet.















  • In my opinion trying to set up a highly available fault tolerant homelab adds a large amount of unnecessary complexity without an equivalent benefit. It’s good to have redundancy for essential services like DNS, but otherwise I think it’s better to focus on a robust backup and restore process so that if anything goes wrong you can just restore from a backup or start containers on another node.

    I configure and deploy all my applications with Ansible roles. It can programmatically create config files, pass secrets, build or start containers, cycle containers automatically after config changes, basically everything you could need.

    Sure it would be neat if services could fail over automatically but things only ever tend to break when I’m making changes anyway.