实测推翻假设：端侧 VLM 能耗大头是「说话」 Seeing is Free, Speaking is Not: Uncovering the True Energy Bottleneck in Edge VLM Inference

精读摘要

视觉-语言模型（VLM）是具身智能的感知骨干，但它们在边缘硬件上的能耗分布一直缺乏系统研究。此前效率优化大多聚焦减少视觉 token，默认视觉处理是能耗大头。这项研究首次对端侧 VLM 推理做系统性能耗剖析，覆盖五个模型、三种架构家族、四档输入分辨率和两种硬件平台（NVIDIA RTX 3070 与 Jetson Orin NX），结果推翻了默认假设：文本生成（说话）才是真正的能耗瓶颈。 VLM efficiency efforts have focused on reducing visual tokens, implicitly treating visual processing as the dominant energy cost on edge hardware. This first systematic energy profiling of on-device VLM inference spans five models, three architecture families, four input resolutions, and two hardware platforms (NVIDIA RTX 3070 and Jetson Orin NX). It overturns the assumption: text generation, not vision, is the true energy bottleneck.