---
title: "Next-Level Inference: Why Your Single-Node vLLM Setup Needs Prefill-Decode Disaggregation"
url: https://daily.dev/posts/next-level-inference-why-your-single-node-vllm-setup-needs-prefill-decode-disaggregation-ru0djcjyz
source_url: https://vllm.ai/blog/moriio-kv-connector
type: article
source: "vLLM"
published: 2026-05-10T09:49:06.167Z
updated: 2026-05-10T09:49:39.371Z
tags: ["ai-inference", "vllm"]
reading_time: 18
upvotes: 0
comments: 0
language: en
---

> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Next-Level Inference: Why Your Single-Node vLLM Setup Needs Prefill-Decode Disaggregation

**[vLLM](https://daily.dev/sources/vllm)** · 18 min read · 0 upvotes · 0 comments

## Summary

Prefill and decode phases in LLM inference compete for the same GPU resources, causing inter-token latency (ITL) spikes under load. This post demonstrates how to implement Prefill-Decode (PD) disaggregation on a single 8-GPU AMD Instinct MI300X node using AMD's MORI-IO RDMA-based KV cache connector in vLLM. By dedicating 4 GPUs to prefill and 4 to decode, the setup achieves 2.5x higher goodput compared to standard collocated serving, with ITL violations eliminated entirely. Two transfer modes are covered: read mode (decode pulls KV cache serially) and write mode (prefill pushes KV cache concurrently, reducing TTFT overhead). Benchmarks use Qwen3-235B-A22B-FP8 at 8 req/s with 2000-token prompts and 1000-token outputs. The post includes architecture details, trade-off analysis, setup instructions, and full reproducible configurations.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://vllm.ai/blog/moriio-kv-connector>

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---

Tags: [#ai-inference](https://daily.dev/tags/ai-inference), [#vllm](https://daily.dev/tags/vllm)

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