Large language models (LLMs) are everywhere — powering chatbots, copilots, and AI-driven apps across industries. But if you’ve ever tried to run one outside of a managed service, you know the pain: gigabytes of model weights, conflicting Python dependencies, fragile CUDA versions, and a GPU setup that only seems to work on your machine.

This is where Docker shines. By packaging the entire environment — code, libraries, and drivers — into a container, you can run an LLM anywhere, whether it’s your laptop, a cloud GPU node, or a Kubernetes cluster. Containers give you reproducibility, portability, and isolation: exactly what’s needed for the messy world of LLMOps.