Answers.Body: <p>Deep models are very tolerant to arithmetic underflow. You can hope for neglectable differences in prediction accuracy between FP32 and FP16 models. Check this <a href="https://arxiv.org/pdf/1710.03740.pdf" rel="nofollow noreferrer">paper</a> for concrete results.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-02-13T11:05:08.520Z
Answers.LastActivityDate: 2019-02-14T09:03:08.523Z
Answers.LastEditDate: 2019-02-14T09:03:08.523Z
Answers.UserDisplayName: ssegvic
Body: <p>I am purchasing Titan RTX GPU. Everything seems fine with that except float32 &amp; float64 performance which seems lower vis-a-vis some of its counter parts. I wanted to understand if single precision and double precision performance of GPU affect deep learning training or efficiency ? We work mostly with images, however not limited to that.</p>
  
Tags: <deep-learning><hardware><hardware-evaluation><gpu>
Title: Does fp32 & fp64 performance of GPU affect deep learning model training?
UserDisplayName: Ruchit Dalwadi
ThreadText: # Does fp32 & fp64 performance of GPU affect deep learning model training?
  
  (Asked by: username_0 on 2019-02-13)
  
  I am purchasing Titan RTX GPU. Everything seems fine with that except float32 & float64 performance which seems lower vis-a-vis some of its counter parts. I wanted to understand if single precision and double precision performance of GPU affect deep learning training or efficiency ? We work mostly with images, however not limited to that.
  
  **Tags:** deep-learning, hardware, hardware-evaluation, gpu
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-02-13. Score: 1
  
  Deep models are very tolerant to arithmetic underflow. You can hope for neglectable differences in prediction accuracy between FP32 and FP16 models. Check this [paper](https://arxiv.org/pdf/1710.03740.pdf) for concrete results.
  
  
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