算力商业
developer.nvidia.com ·
@Tanya Lenz
何时使用编码-预填充-解码分离来加速多模态模型服务
NVIDIA 官方博客介绍编码-预填充-解码（EPD）分离技术，该技术将视觉编码器阶段与预填充和解码阶段分离，适用于图像密集型提示、短到中等输出和量化 MoE 模型。文章展示了如何使用 NVIDIA Dynamo 实现该技术，并声称最高可提升 5 倍性能。
AI推荐理由 NVIDIA 官方博客，介绍 EPD 分离技术用于多模态模型推理优化，提供具体适用场景和性能提升数据（最高 5 倍），信息密度高，对 AI 推理优化有实际参考价值。
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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…
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