NVIDIA Dynamo is an open-source inference framework for deploying large-scale generative AI models across multi-node, multi-GPU environments. Disaggregated serving architecture separates prompt processing (prefill) and token generation (decode) phases across different GPU workers, enabling independent scaling and optimization of each phase based on their distinct computational characteristics. This guide covers building disaggregated inference deployments with NVIDIA Dynamo using AIConfigurator for optimal configuration discovery, deploying on Kubernetes with RDMA, and validating performance. Disaggregated architecture can achieve up to 1.7x throughput improvement over aggregated serving for large models (32B+) with long input contexts; for smaller models, AIConfigurator automatically determines when aggregated serving is more efficient.…