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Gpu to arch
Fermi† Kepler† Maxwell‡ Pascal Volta Turing Ampere Lovelace* Hopper**
sm_20 sm_30 sm_50 sm_60 sm_70 sm_75 sm_80 sm_90? sm_100c?
sm_35 sm_52 sm_61 sm_72 sm_86
sm_37 sm_53 sm_62

† Fermi and Kepler are deprecated from CUDA 9 and 11 onwards ‡ Maxwell is deprecated from CUDA 12 onwards * Lovelace is the microarchitecture replacing Ampere (AD102) ** Hopper is NVIDIA’s rumored “tesla-next” series, with a 5nm process.

Fermi cards (CUDA 3.2 until CUDA 8)

Deprecated from CUDA 9, support completely dropped from CUDA 10.

  • SM20 or SM_20, compute_30 – GeForce 400, 500, 600, GT-630. *Completely dropped from CUDA 10 onwards.***

Kepler cards (CUDA 5 until CUDA 10)

Deprecated from CUDA 11.

  • SM30 or SM_30, compute_30 – Kepler architecture (e.g. generic Kepler, GeForce 700, GT-730). Adds support for unified memory programming Completely dropped from CUDA 11 onwards.
  • SM35 or SM_35, compute_35 – Tesla K40. Adds support for dynamic parallelism. Deprecated from CUDA 11, will be dropped in future versions.
  • SM37 or SM_37, compute_37 – Tesla K80. Adds a few more registers. Deprecated from CUDA 11, will be dropped in future versions, strongly suggest replacing with a 32GB PCIe Tesla V100.

Maxwell cards (CUDA 6 until CUDA 11)

  • SM50 or SM_50, compute_50 – Tesla/Quadro M series. Deprecated from CUDA 11, will be dropped in future versions, strongly suggest replacing with a Quadro RTX 4000.
  • 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, Drive CX, Drive PX, Jetson Nano.

Pascal (CUDA 8 and later)

  • SM60 or SM_60, compute_60 – Quadro GP100, Tesla P100, DGX-1 (Generic Pascal)
  • SM61 or SM_61, compute_61– GTX 1080, GTX 1070, GTX 1060, GTX 1050, GTX 1030 (GP108), GT 1010 (GP108) 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 – DGX-1 with Volta, Tesla V100, GTX 1180 (GV104), Titan V, Quadro GV100
  • SM72 or SM_72, compute_72 – Jetson AGX Xavier, Drive AGX Pegasus, Xavier NX

Turing (CUDA 10 and later)

  • SM75 or SM_75, compute_75 – GTX/RTX Turing – GTX 1660 Ti, RTX 2060, RTX 2070, RTX 2080, Titan RTX, Quadro RTX 4000, Quadro RTX 5000, Quadro RTX 6000, Quadro RTX 8000, Quadro T1000/T2000, Tesla T4

Ampere (CUDA 11.1 and later)

  • SM80 or SM_80, compute_80 – NVIDIA A100 (the name “Tesla” has been dropped – GA100), NVIDIA DGX-A100
  • *SM86 or SM_86, compute_86* – (from CUDA 11.1 onwards) Tesla GA10x cards, RTX Ampere – RTX 3080, GA102 – RTX 3090, RTX A2000, A3000, A4000, A5000, A6000, NVIDIA A40, GA106 – RTX 3060, GA104 – RTX 3070, GA107 – RTX 3050, Quadro A10, Quadro A16, Quadro A40, A2 Tensor Core GPU

“Devices of compute capability 8.6 have 2x more FP32 operations per cycle per SM than devices of compute capability 8.0. While a binary compiled for 8.0 will run as is on 8.6, it is recommended to compile explicitly for 8.6 to benefit from the increased FP32 throughput.“

https://docs.nvidia.com/cuda/ampere-tuning-guide/index.html#improved_fp32

Hopper (CUDA 12 [planned] and later)

  • SM90 or SM_90, compute_90 – NVIDIA H100 (GH100)