Introducing Disaggregated Inference on AWS powered by llm-d

Amazon AWS AI Blog / 3/17/2026

💬 OpinionDeveloper Stack & InfrastructureTools & Practical Usage

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Key Points

The post introduces disaggregated inference concepts—disaggregated serving, intelligent request scheduling, and expert parallelism—and explains how they can boost LLM inference performance and resource efficiency.
It explains how to implement these concepts on Amazon SageMaker HyperPod with EKS to achieve higher throughput and better resource utilization.
The article highlights llm-d as the enabling technology behind disaggregated inference and describes expected operational benefits.
It provides practical deployment guidance and steps, including configuration tips and example workflows to test and validate the approach.

In this blog post, we introduce the concepts behind next-generation inference capabilities, including disaggregated serving, intelligent request scheduling, and expert parallelism. We discuss their benefits and walk through how you can implement them on Amazon SageMaker HyperPod EKS to achieve significant improvements in inference performance, resource utilization, and operational efficiency.