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Comparing GPUs and their performance with reference to a various tasks

An important topic raised by many of our visitors and customers - which GPU to choose for a certain type of tasks? Which video cards would be appropriate for the specific requirements in regards to the actual task?

Choosing a right GPU can be a tricky decision to take. In this guide, we will provide a comprehensive overview of the available GPUs for modern high-performance tasks, which will help you to figure out which choice is the best one for your current needs.

Nvidia Tesla P100, P4, P40, K80

Advantages:

  • Provides high performance
  • Suitable for both single and double precision calculations
  • Applicable for a variety of scientific fields (financial calculations, CFD modeling, data analysis, climate and weather forecasting, etc.)

Disadvantages:

  • High price

Nvidia Tesla M6, M60

Advantages:

  • Applicable for enterprise desktop virtualization
  • Provides high performance
  • Suitable for both single and double precision calculations

Disadvantages:

  • High price

Nvidia Geforce GTX 1080, 1080 Ti

Advantages:

  • High performance
  • A wide variety of applications (machine learning, rendering, scientific calculations, etc.)
  • Reasonably priced

Disadvantages:

  • Poorly suitable for double precision calculations

Nvidia Quadro (GP100, P6000, P5000, P4000)

Advantages:

  • Distinct solution for designers, architects, gamers, etc.
  • Highly accurate and reliable for calculations
  • Large amount of memory
  • Supports VR

Disadvantages:

  • Less productive in comparison to other video cards from Nvidia

Nvidia Quadro (P2000, P1000, P600, P400)

Advantages:

  • Distinct solution for designers, architects, gamers, etc.
  • Advanced accuracy and reliability of calculations
  • Large amount of memory

Disadvantages:

  • Less productive in comparison to other video cards from Nvidia
  • Does not have VR support

What would be a better choice for a machine learning: Tesla P100, Geforce GTX 1080 or GTX 1080 Ti?

Geforce GTX 1080 Ti is the distinct solution for machine learning. According to benchmarks results, this card produce a performance gain of about 20% in neural network training tasks (compared to the GTX 1080).

If you require a large amount of memory for machine learning, then you can use the Tesla P100.

Single or double precision calculations?

Double precision calculations are vital in order to exclude errors in these areas where they are unacceptable. For example, this kind of accuracy in calculations is important for a variety of scientific tasks, video editing, virtual reality modeling, etc.

Single-precision calculations required for creation of game physics and simulation.

Half-precision calculations used for deep learning (full support for FP16 expected to be implemented in the Volta GPU).

Which video card is preferable?

  • Machine learning, deep learning, training of neural networks - Geforce GTX 1080 Ti, Geforce GTX 1080
  • High-performance scientific computing - Nvidia Tesla P100, Geforce GTX 1080 Ti
  • Rendering, editing video - Nvidia Quadro, Geforce GTX 1080 Ti

You may always enquire for rent of a server with the desired GPUs in our Leader GPU store. 

Still have questions? Write to us!