Having optimized a diffusion model for inference, the next step involves constructing the environment where it will operate efficiently at scale. This chapter concentrates on building the necessary infrastructure. We will cover packaging models and their dependencies using containers like Docker, managing deployments and scaling using orchestrators such as Kubernetes, and configuring cloud resources, including specialized hardware like GPUs and serverless compute options.

Key topics include managing GPU resources within containers, implementing autoscaling based on inference demand, and addressing storage considerations for large models and generated data. You will learn to design and implement systems capable of handling variable loads while managing computational resources, specifically addressing the requirements of GPU-intensive diffusion models. Practical exercises will guide you through deploying a containerized model on a Kubernetes cluster.