# Disaggregated Prefill and Decode for LLM Inference on SageMaker HyperPod

> **Platform:** [SPIDITS AI](https://spidits.com/) — Real-Time AI News & Market Intelligence  
> **Published:** 2026-07-10T15:20:16.000Z  
> **Category:** PRODUCT_LAUNCH  
> **Impact Score:** 140/100  
> **Primary Source:** [AWS ML Blog](https://aws.amazon.com/blogs/machine-learning/disaggregated-prefill-and-decode-for-llm-inference-on-sagemaker-hyperpod)  
> **Canonical Citation:** [https://spidits.com/timeline/disaggregated-prefill-and-decode-for-llm-inference-on-sagemaker-hyperpod](https://spidits.com/timeline/disaggregated-prefill-and-decode-for-llm-inference-on-sagemaker-hyperpod)

## Executive Summary
In this post, we show how to implement DPD with vLLM on Amazon SageMaker HyperPod using the HyperPod Inference Operator.

## Why It Matters (Strategic Analysis)
Crucially, this shifts the paradigm for LLM inference on SageMaker HyperPod, enabling organizations to tackle complex, high-concurrency workloads with improved time to first token (TTFT) and inter-token latency (ITL).

## Referenced Coverage & Sources
- **[AWS ML Blog](https://aws.amazon.com/blogs/machine-learning/disaggregated-prefill-and-decode-for-llm-inference-on-sagemaker-hyperpod)**: Disaggregated prefill and decode for LLM inference on SageMaker HyperPod — _In this post, we show how to implement DPD with vLLM on Amazon SageMaker HyperPod using the HyperPod Inference Operator._

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