title: Using CPU vs GPU to train a model - Speed vs memory
body: <p>I am trying to train the model found at 
  <a href="https://github.com/silicon-valley-data-science/RNN-Tutorial" rel="nofollow noreferrer">https://github.com/silicon-valley-data-science/RNN-Tutorial</a> With a dataset generated through <a href="https://github.com/jupiter126/Create_Speech_Dataset" rel="nofollow noreferrer">https://github.com/jupiter126/Create_Speech_Dataset</a> (around 340000 small wav audio samples with transcripts).<br /><br />
  When I train with GPU the training goes relatively fast, however I can't set batch_train_size above 25 without reaching OOM.<br />
  When I train with CPU, training is much slower, but I can easily set batch_train_size to 250 (probably up to 700 but didn't try yet).<br /><br /></p>
  
  <p>I am confused on how the small batch size limit on GPU might affect training quality, or if raising the raising the number of epoch might cancel out that effect...<br />  In other words, 25 samples on 10000 epoch or 500 samples on 500 epoch? <br /> <br />
  GPU is GTX 1060 with 6Gb ram, CPU is dual XEON 2630l v4 (2*10 hyperthreaded cores at 1.7Ghz) with 128Gb ram.<br /><br /></p>