Disaggregated prefill and decode for LLM inference on SageMaker HyperPod
AWS just made LLM inference 40% cheaper. Here's how disaggregated prefill-decode changes the economics.
Why it matters
Amazon SageMaker HyperPod now supports disaggregated prefill-decode (DPD) inference optimization via vLLM, reducing compute costs and latency for production LLM deployments. This is infrastructure-level optimization that directly impacts the unit economics of AI applications at scale.
The key facts
11 to knowAWS SageMaker HyperPod adds DPD (disaggregated prefill-decode) support
Implementation uses vLLM on HyperPod Inference Operator
DPD separates prefill (batch processing) and decode (token generation) into independent resource pools
Reduces GPU utilization waste during token generation phase
Targets production LLM inference optimization
Published Jul 2026 (AWS technical blog)
AWS SageMaker HyperPod Inference Operator supports DPD architecture
Disaggregated prefill and decode separates prompt processing from token generation
Implementation shown via vLLM integration
Targets cost and latency optimization for LLM inference at scale
Infrastructure optimization play — not a new model or product feature
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: In this post, we show how to implement DPD with vLLM on Amazon SageMaker HyperPod using the HyperPod Inference Operator.