List of NVIDIA Desktop Graphics Card Models for Building Deep Learning AI System

If you are doing deep learning AI research and/or development with GPUs, big chance you will be using graphics card from NVIDIA to perform the deep learning tasks. A vantage point with GPU computing is related with the fact that the graphics card occupies the PCI / PCIe slot. From the frugality point of view, it may be a brilliant idea to scavenge unused graphics cards from the fading PC world and line them up on another unused desktop motherboard to create a somewhat powerful compute node for AI tasks. Maybe not.

With the increasing popularity of container-based deployment, a system architect may consider creating several containers with each running different AI tasks. This means that that the underlying GPU resources should then be shared among the containers. NVIDIA provides a utility called NVIDIA Docker or nvidia-docker2 that enables the containerization of a GPU-accelerated machine. As the name suggests, the utility targets Docker container type.

This utility, however, cannot be immediately usable for all NVIDIA graphics card models. Only graphics card having GPUs with architecture newer than Fermi can benefit from this feature. This post summarizes the list of NVIDIA desktop GPU models that serve as a better fit for building a deep learning AI system, especially through containerization support of AI tasks. The list is sourced from the Wikipedia entry of NVIDIA GPUs.

SeriesModel NameArchitectureCode NameBus InterfaceLaunch Date
GeForce 600GeForce GT 630KeplerGK107PCIe 2.0 x16April 24, 2012
KeplerGK208-301-A1PCIe 2.0 x8May 29, 2013
GeForce GT 635KeplerGK208PCIe 3.0 x8February 19, 2013
GeForce GT 640KeplerGK107-301-A2 PCIe 3.0 x16April 24, 2012
KeplerGK107PCIe 3.0 x16June 5, 2012
KeplerGK208-400-A1PCIe 2.0 x8May 29, 2013
GeForce GTX 645KeplerGK106PCIe 3.0 x16April 22, 2013
GeForce GTX 650KeplerGK107-450-A2PCIe 3.0 x16September 13, 2012
GeForce GTX 650 TiKeplerGK106-220-A1PCIe 3.0 x16October 9, 2012
GeForce GTX 650 Ti BoostKeplerGK106-240-A1PCIe 3.0 x16March 26, 2013
GeForce GTX 660KeplerGK106-400-A1PCIe 3.0 x16September 13, 2012
KeplerGK104-200-KD-A2PCIe 3.0 x16August 22, 2012
GeForce GTX 660 TiKeplerGK104-300-KD-A2PCIe 3.0 x16August 16, 2012
GeForce GTX 670KeplerGK104-325-A2PCIe 3.0 x16May 10, 2012
GeForce GTX 680KeplerGK104-400-A2PCIe 3.0 x16March 22, 2013
GeForce GTX 690Kepler2× GK104-355-A2PCIe 3.0 x16April 29, 2012
GeForce 700GeForce GT 710KeplerGK208-301-A1PCIe 2.0 x8March 27, 2014
KeplerGK208-203-B1PCIe 2.0 x8January 26, 2016
GeForce GT 720KeplerGK208-201-B1PCIe 2.0 x8March 27, 2014
GeForce GT 730KeplerGK208-301-A1PCIe 2.0 x8June 18, 2014
KeplerGK208-400-A1PCIe 2.0 x8June 18, 2014
GeForce GT 740KeplerGK107-425-A2PCIe 3.0 x16May 29, 2014
GeForce GTX 745MaxwellGM107-300-A2PCIe 3.0 x16February 18, 2014
GeForce GTX 750MaxwellGM107-300-A2PCIe 3.0 x16February 18, 2014
GeForce GTX 750 TiMaxwellGM107-400-A2PCIe 3.0 x16February 18, 2014
GeForce GTX 760 192-bitKeplerGK104-200-KD-A2PCIe 3.0 x16October 17, 2013
GeForce GTX 760KeplerGK104-225-A2PCIe 3.0 x16June 25, 2013
GeForce GTX 760 TiKeplerGK104PCIe 3.0 x16N/A
GeForce GTX 770KeplerGK104-425-A2PCIe 3.0 x16May 30, 2013
GeForce GTX 780KeplerGK110-300-A1PCIe 3.0 x16May 23, 2013
GeForce GTX 780 TiKeplerGK110-425-B1PCIe 3.0 x16November 7, 2013
GeForce GTX TITANKeplerGK110-400-A1PCIe 3.0 x16February 21, 2013
GeForce GTX TITAN BlackKeplerGK110-430-B1PCIe 3.0 x16February 18, 2014
GeForce GTX TITAN ZKepler2× GK110PCIe 3.0 x16March 25, 2014
GeForce 900GeForce GT 945AMaxwellGM108-?PCIe 3.0 x8February, 2016
GeForce GTX 950MaxwellGM206-250PCIe 3.0 x16August 20, 2015
GeForce GTX 960MaxwellGM206-300PCIe 3.0 x16January 22, 2015
GeForce GTX 970MaxwellGM204-200PCIe 3.0 x16September 18, 2014
GeForce GTX 980MaxwellGM204-400PCIe 3.0 x16September 18, 2014
GeForce GTX 980 TiMaxwellGM200-310PCIe 3.0 x16June 1, 2015
GeForce GTX TITAN XMaxwellGM200-400PCIe 3.0 x16March 17, 2015
GeForce 10GeForce GT 1030PascalGP108-300PCIe 3.0 x4May 17, 2017
GeForce GTX 1050PascalGP107-300PCIe 3.0 x16October 25, 2016
GeForce GTX 1050 TiPascalGP107-400PCIe 3.0 x16October 25, 2016
GeForce GTX 1060PascalGP106-300PCIe 3.0 x16August 18, 2016
GeForce GTX 1070PascalGP104-200PCIe 3.0 x16June 10, 2016
GeForce GTX 1070 TiPascalGP104-300PCIe 3.0 x16November 2, 2017
GeForce GTX 1080PascalGP104-400PCIe 3.0 x16May 27, 2016
GeForce GTX 1080 TiPascalGP102-350PCIe 3.0 x16March 5, 2017
Nvidia TITAN XPascalGP102-400PCIe 3.0 x16August 2, 2016
Nvidia TITAN XpPascalGP102-450PCIe 3.0 x16April 6, 2017
VoltaNvidia TITAN VVoltaGV100-400-A1PCIe 3.0 x16December 7, 2017

What about non-desktop GPUs such as those on workstation or laptop? Can we also use them for deep learning? NVIDIA provides the list for all products with CUDA support on this page: https://developer.nvidia.com/cuda-gpus.

CUDA-enabled vs Deep-Learning Ready GPUs

It is important to note that not all CUDA-enabled GPUs can perform deep learning tasks. NVIDIA introduced a terminology called CUDA compute capability that refers to the general specifications and available features of a CUDA-enabled GPU. The GPUs built with Fermi architecture has a maximum compute capability of 2.1, while the Kepler architecture has a minimum compute capability of 3.0.

The deep learning frameworks that rely on CUDA for GPU computing operate by invoking CUDA-specific GPU-accelerated deep learning methods to speed up the computation. These methods are provided in the cuDNN library. The library itself is compatible with CUDA-enabled GPUs with compute capability at least 3.0. It is suffice to say that only GPUs with Kepler architecture or newer are capable of accomplishing the deep learning tasks, or in other words, deep-learning ready.

One thought on “List of NVIDIA Desktop Graphics Card Models for Building Deep Learning AI System

  1. Pingback: Guide: Installing Cuda Toolkit 9.1 on Ubuntu 16.04 « Amikelive | Technology Blog

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