5 Docker Containers You Need for a Home AI Lab

The first weekend goes well. A model runs locally, a chat window answers questions, and the laptop fan finally has a reason to exist. The third weekend is where home AI labs die. Three containers sit on ports nobody wrote down. A folder of PDFs never got indexed, a cron job lives in someone's shell history, and an update wiped the models because they were stored inside the container. Not one of those failures is an AI problem; every one of them is a lab problem.

A useful home lab is a small set of containers with clear jobs, not one giant AI box. KDnuggets took the same layered view in its look at the local AI stack. Model serving, an interface, retrieval memory, automation and management are separate layers, and each one is a container you can replace without touching the others. The five below are picked for the friction they remove rather than for how much AI is in the name. Two of them have no AI in them at all.

1. Ollama: The Model Runtime

Ollama is the engine room. It pulls and runs local models and exposes an HTTP endpoint on port 11434. Every other container in the lab talks to that endpoint, so nothing else needs to know how a model is loaded. The official Docker Hub image ships three routes: CPU only, NVIDIA GPUs through the NVIDIA Container Toolkit with --gpus=all, and AMD GPUs through the rocm tag.

The install detail that matters most is the volume. Models are large, and a container recreated without a named volume downloads them all again. Worse, it can lose a model you pulled from a tag that has since moved. The documented run command is:

docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama

Drop --gpus=all for the CPU route. One honest caveat: GPU pass-through depends on the host, not on Ollama. The documented Docker GPU routes are Linux and Windows with WSL2. Docker Desktop on macOS does not hand containers an NVIDIA GPU, so a Mac lab either runs Ollama natively for speed or accepts CPU inference inside Docker.