The first time I containerized a fine-tuned Llama model for a client's internal search tool, the build finished at 38 gigabytes. I remember staring at the terminal thinking there was no way that was right. It was right. The image included a CUDA base, PyTorch with every backend compiled in, model weights baked directly into the layer, and a pip cache that had not been cleaned. Pushing that to our registry took eleven minutes on a good connection. Pulling it onto a fresh node during an autoscale event took even longer, and by the time the pod was ready, the traffic spike it was supposed to handle had already passed. That's the moment I stopped treating LLM containers like regular application containers, because they are not the same animal at all.