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
RTX 4070 SUPER,
"PyTorch 2.1 - Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_l",
Higher Results Are Better
"RTX 4070 SUPER",103.17086360604
"ProjectPhysX OpenCL-Benchmark 1.2 - Operation: Memory Bandwidth Coalesced Write",
Higher Results Are Better
"NVIDIA RTX 4070 SUPER",455.09,454.74,455.2
"ProjectPhysX OpenCL-Benchmark 1.2 - Operation: Memory Bandwidth Coalesced Read",
Higher Results Are Better
"NVIDIA RTX 4070 SUPER",464.87,464.84,464.86
"ProjectPhysX OpenCL-Benchmark 1.2 - Operation: INT8 Compute",
Higher Results Are Better
"NVIDIA RTX 4070 SUPER",14.304,14.388,14.229
"ProjectPhysX OpenCL-Benchmark 1.2 - Operation: INT16 Compute",
Higher Results Are Better
"NVIDIA RTX 4070 SUPER",17.166,17.17,17.175
"ProjectPhysX OpenCL-Benchmark 1.2 - Operation: INT32 Compute",
Higher Results Are Better
"NVIDIA RTX 4070 SUPER",19.892,19.886,19.89
"ProjectPhysX OpenCL-Benchmark 1.2 - Operation: INT64 Compute",
Higher Results Are Better
"NVIDIA RTX 4070 SUPER",4.192,4.207,4.243
"ProjectPhysX OpenCL-Benchmark 1.2 - Operation: FP32 Compute",
Higher Results Are Better
"NVIDIA RTX 4070 SUPER",38.532,38.624,38.625
"ProjectPhysX OpenCL-Benchmark 1.2 - Operation: FP64 Compute",
Higher Results Are Better
"NVIDIA RTX 4070 SUPER",0.622,0.621,0.621
"PyTorch 2.1 - Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152",
Higher Results Are Better
"RTX 4070 SUPER",195.72387968301,193.44408664711
"PyTorch 2.1 - Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_l",
Higher Results Are Better
"RTX 4070 SUPER",102.59918087262
"PyTorch 2.1 - Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_l",
Higher Results Are Better
"RTX 4070 SUPER",103.57095408734
"PyTorch 2.1 - Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50",
Higher Results Are Better
"RTX 4070 SUPER",505.65741375953,508.71811992404,507.97130629644
"PyTorch 2.1 - Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_l",
Higher Results Are Better
"RTX 4070 SUPER",106.3705051719
"PyTorch 2.1 - Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152",
Higher Results Are Better
"RTX 4070 SUPER",195.38787181133
"PyTorch 2.1 - Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50",
Higher Results Are Better
"RTX 4070 SUPER",499.83942473782,508.69919302359
"PyTorch 2.1 - Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152",
Higher Results Are Better
"RTX 4070 SUPER",201.93643942088
"PyTorch 2.1 - Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50",
Higher Results Are Better
"RTX 4070 SUPER",509.44719996986
"PyTorch 2.1 - Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_l",
Higher Results Are Better
"RTX 4070 SUPER",
"PyTorch 2.1 - Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50",
Higher Results Are Better
"RTX 4070 SUPER",
"PyTorch 2.1 - Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152",
Higher Results Are Better
"RTX 4070 SUPER",
"PyTorch 2.1 - Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152",
Higher Results Are Better
"RTX 4070 SUPER",
"PyTorch 2.1 - Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50",
Higher Results Are Better
"NVIDIA RTX 4070 SUPER",
"RTX 4070 SUPER",
"PyTorch 2.1 - Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_l",
Higher Results Are Better
"RTX 4070 SUPER",
"PyTorch 2.1 - Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152",
Higher Results Are Better
"RTX 4070 SUPER",
"PyTorch 2.1 - Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50",
Higher Results Are Better
"RTX 4070 SUPER",