<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>TomVDB</author><text>Even after the edit, it&amp;#x27;s a bit confusing in that, without looking at the table, you get the impression that FP16 is slower than FP32.</text><parent_chain><item><author>jakear</author><text>That second table is aa good example of why always including units (or even just a &amp;quot;higher is better&amp;quot;) is a good idea... I have no clue what I&amp;#x27;m looking at.&lt;p&gt;Edit: It&amp;#x27;s been edited, thx Evolution :) (or I totally glossed over it the first time around... but I don&amp;#x27;t think so)</text></item></parent_chain></comment><story><title>Benchmarking TensorFlow on Nvidia GeForce RTX 3090</title><url>https://www.evolution.ai/post/benchmarking-deep-learning-workloads-with-tensorflow-on-the-nvidia-geforce-rtx-3090</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>hughes</author><text>Even after being edited, it&amp;#x27;s still wrong. It shows the significantly lower Inception4 performance as a &amp;quot;40% speedup&amp;quot; instead of 40% of baseline images&amp;#x2F;sec.</text><parent_chain><item><author>jakear</author><text>That second table is aa good example of why always including units (or even just a &amp;quot;higher is better&amp;quot;) is a good idea... I have no clue what I&amp;#x27;m looking at.&lt;p&gt;Edit: It&amp;#x27;s been edited, thx Evolution :) (or I totally glossed over it the first time around... but I don&amp;#x27;t think so)</text></item></parent_chain></comment><story><title>Benchmarking TensorFlow on Nvidia GeForce RTX 3090</title><url>https://www.evolution.ai/post/benchmarking-deep-learning-workloads-with-tensorflow-on-the-nvidia-geforce-rtx-3090</url></story>