Deploying a fine-tuned model involves several steps. After training your model, you'd use a framework like PyTorch to create an inference pipeline, which includes loading model weights, preprocessing inputs, building the inference graph, and generating predictions. For deployment, you can use serverless GPU services like Replicate to handle the necessary infrastructure, avoiding the need to run GPUs 24/7.