# When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving

DevFeed: [When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving](<https://devfeed.tech/articles/when-to-use-encode-prefill-decode-disaggregation-to-accelerate-multimodal-model-serving-6945.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/when-to-use-encode-prefill-decode-disaggregation-to-accelerate-multimodal-model-serving/>)

Author: Tanya Lenz

Published: 2026-09-09T20:31:04Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Optimization](<https://devfeed.tech/topics/optimization.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [Dynamo](<https://devfeed.tech/topics/dynamo.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [computer-vision-video-analytics](<https://devfeed.tech/tags/computer-vision-video-analytics.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [dynamo](<https://devfeed.tech/tags/dynamo.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [low-latency-inference](<https://devfeed.tech/tags/low-latency-inference.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [model-serving](<https://devfeed.tech/tags/model-serving.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [optimization](<https://devfeed.tech/tags/optimization.md>)

## AI overview

This article explains when encode-prefill-decode disaggregation improves multimodal model serving. Using NVIDIA Dynamo, it separates vision encoding from LLM prefill and decode so the stages can batch, schedule, and scale independently. The approach is most effective for image-heavy prompts, short-to-medium outputs, and quantized mixture-of-experts models, with reported gains of up to 5x in time to first token and 7x in end-to-end response time.

## Source excerpt

Encode-prefill-decode (EPD) disaggregation is an inference optimization technique for multimodal models that separates the vision encoder stage from the prefill...