NVIDIA · Developer 博客

呵

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

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

二〇二六年九月十日 · 英文原文

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

Encode-prefill-decode (EPD) disaggregation is an inference optimization technique for multimodal models that separates the vision encoder stage from the prefill... Encode-prefill-decode (EPD) disaggregation is an inference optimization technique for multimodal models that separates the vision encoder stage from the prefill and decode stages. It is most effective for image-heavy prompts, short-to-medium outputs, and quantized mixture-of-experts (MoE) models. This post shows when and how to use EPD disaggregation with NVIDIA Dynamo to achieve up to 5x… Source

译自 NVIDIA · Developer 博客 · 录于 二〇二六年九月十日