NVIDIA 不同显卡对应的GPU计算能力

• Fermi (CUDA 3.2 until CUDA 8) (deprecated from CUDA 9):
  • SM20 or SM_20, compute_30 – Older cards such as GeForce 400, 500, 600, GT-630
• Kepler (CUDA 5 and later):
  • SM30 or SM_30, compute_30 – Kepler architecture (generic – Tesla K40/K80, GeForce 700, GT-730)
    Adds support for unified memory programming
  • SM35 or SM_35, compute_35 – More specific Tesla K40
    Adds support for dynamic parallelism. Shows no real benefit over SM30 in my experience.
  • SM37 or SM_37, compute_37 – More specific Tesla K80
    Adds a few more registers. Shows no real benefit over SM30 in my experience
• Maxwell (CUDA 6 and later):
  • SM50 or SM_50, compute_50 – Tesla/Quadro M series
  • SM52 or SM_52, compute_52 – Quadro M6000 , GeForce 900, GTX-970, GTX-980, GTX Titan X
  • SM53 or SM_53, compute_53 – Tegra (Jetson) TX1 / Tegra X1
• Pascal (CUDA 8 and later)
  • SM60 or SM_60, compute_60 – GP100/Tesla P100 – DGX-1 (Generic Pascal)
  • SM61 or SM_61, compute_61 – GTX 1080, GTX 1070, GTX 1060, GTX 1050, GTX 1030, Titan Xp, Tesla P40, Tesla P4, Discrete GPU on the NVIDIA Drive PX2
  • SM62 or SM_62, compute_62 – Integrated GPU on the NVIDIA Drive PX2, Tegra (Jetson) TX2
• Volta (CUDA 9 and later)
  • SM70 or SM_70, compute_70 – Tesla V100, GTX 1180 (GV104)
  • SM71 or SM_71, compute_71 – probably not implemented
  • SM72 or SM_72, compute_72 – currently unknown
• Turing (CUDA 10 and later)
  • SM75 or SM_75, compute_75 – RTX 2080, Titan RTX, Quadro R8000

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内置Jupyter、Pycharm的GPU服务器，访问淘宝店

参考网址：http://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/