https://github.com/google-deepmind/alphafold3/issues/59
We ran the "2PV7" example from the docs on all GPU models available on our cluster with the following results:
gpuranking_scoredriver_vercuda
|
rtx_2080_ti
| -99.68
| 535.183.06
| 12.2
|
rtx_3090
| 0.67
| 550.127.05
| 12.4
|
rtx_4090
| 0.67
| 550.127.05
| 12.4
|
titan_rtx
| -99.78
| 550.127.05
| 12.4
|
quadro_rtx_6000
| -99.74
| 550.90.07
| 12.4
|
v100
| -99.78
| 550.127.05
| 12.4
|
a100_pcie_40gb
| 0.67
| 550.127.05
| 12.4
|
a100_80gb
| 0.67
| 550.127.05
| 12.4
|
Specifically, a ranking score of -99 corresponds to noise/explosion, and a ranking score of 0.67 corresponds to a visually compelling output structure.
Update (20.11): added driver/cuda versions reported by nvidia-smi.
These are the GPU capabilities (see https://developer.nvidia.com/cuda-gpus) for the GPUs mentioned:
rtx_2080_ti7.5(bad) rtx_3090 8.6 rtx_4090 8.9 titan_rtx7.5(bad) quadro_rtx_60007.5(bad) v100 7.0(bad) a100_pcie_40gb 8.0 a100_80gb8.0
