RTX 4070 SUPER

Intel Core i9-13900K testing with a ASUS TUF GAMING Z790-PRO WIFI (1401 BIOS) and ASUS NVIDIA GeForce RTX 4070 SUPER 12GB on EndeavourOS rolling via the Phoronix Test Suite.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2401252-NE-RTX4070SU41
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NVIDIA RTX 4070 SUPER
January 25
  5 Minutes
RTX 4070 SUPER
January 26
  5 Minutes
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RTX 4070 SUPER Suite 1.0.0 System Test suite extracted from RTX 4070 SUPER. pts/pytorch-1.0.1 cuda 256 efficientnet_v2_l Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_l pts/opencl-benchmark-1.0.0 Operation: Memory Bandwidth Coalesced Write pts/opencl-benchmark-1.0.0 Operation: Memory Bandwidth Coalesced Read pts/opencl-benchmark-1.0.0 Operation: INT8 Compute pts/opencl-benchmark-1.0.0 Operation: INT16 Compute pts/opencl-benchmark-1.0.0 Operation: INT32 Compute pts/opencl-benchmark-1.0.0 Operation: INT64 Compute pts/opencl-benchmark-1.0.0 Operation: FP32 Compute pts/opencl-benchmark-1.0.0 Operation: FP64 Compute pts/pytorch-1.0.1 cuda 256 resnet152 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152 pts/pytorch-1.0.1 cuda 32 efficientnet_v2_l Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_l pts/pytorch-1.0.1 cuda 512 efficientnet_v2_l Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_l pts/pytorch-1.0.1 cuda 64 resnet50 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50 pts/pytorch-1.0.1 cuda 1 efficientnet_v2_l Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_l pts/pytorch-1.0.1 cuda 32 resnet152 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152 pts/pytorch-1.0.1 cuda 512 resnet50 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50 pts/pytorch-1.0.1 cuda 1 resnet152 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152 pts/pytorch-1.0.1 cuda 16 resnet50 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50 pts/pytorch-1.0.1 cuda 64 efficientnet_v2_l Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_l pts/pytorch-1.0.1 cuda 256 resnet50 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50 pts/pytorch-1.0.1 cuda 512 resnet152 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152 pts/pytorch-1.0.1 cuda 64 resnet152 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152 pts/pytorch-1.0.1 cuda 1 resnet50 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50 pts/pytorch-1.0.1 cuda 16 efficientnet_v2_l Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_l pts/pytorch-1.0.1 cuda 16 resnet152 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152 pts/pytorch-1.0.1 cuda 32 resnet50 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50