<p>From their published paper at <a href="https://arxiv.org/pdf/1801.04381.pdf" rel="nofollow noreferrer">MobileNetV2: Inverted Residuals and Linear Bottlenecks</a>,</p>

<p>under subtopic number 5: Implementation Notes, 5.1. Memory efficient inference;</p>

<blockquote>
  <p>The inverted residual bottleneck layers allow a particularly
  memory efficient implementation which is very
  important for mobile applications. (and more in paper)</p>
</blockquote>

<p>According to TensorFlow team, it's optimized smaller in size can also be used as TF Lite. As far as we know TF Lite is indeed for mobile use. It's much slower on desktop GPU probably V2 has more conv layers compared to V1 which make sense if the training tooks more times to finish. For now, we didn't do the training and inferencing of data on mobile because of computational speed hunger which lead to power hunger as well. </p>

<p>Hope I answer the question.</p>
Why the MobileNetV2 is faster than MobileNetV1 only at mobile device?
<p>I am studying about Google's brandnew <code>MobileNetV2</code> architecture.</p>

<p>During studying, I've read this string at Tensorflow model zoo Github</p>

<p>'For example Mobilenet V2 is faster on mobile devices than Mobilenet V1, but is slightly slower on desktop GPU.'</p>

<p>So, my question is,</p>

<p>How that could be possible? I really want to know why.</p>