Deploying Disaggregated LLM Inference Workloads on Kubernetes

In large language model (LLM) inference workloads, a single monolithic serving process can hit its limits due to different compute profiles for prefill and decode stages. Disaggregated serving splits the pipeline into distinct stages to better utilize GPU resources and scale more flexibly on Kubernetes. Different ecosystem solutions like NVIDIA Dynamo and llm-d implement this pattern to optimize inference performance.