何时使用 Encode-Prefill-Decode 解耦加速多模态模型服务
When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving
NVIDIA 详解多模态推理优化技术 Encode-Prefill-Decode（EPD）解耦：将视觉编码阶段与 prefill、decode 阶段分离，在图像密集提示、短到中等长度输出以及量化 MoE 模型上收益最大。结合 NVIDIA Dynamo 使用，最多可实现 5 倍加速。
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…
来源：NVIDIA Developer Blog · developer.nvidia.com