Intel Core i9-13900K testing with a ASUS TUF GAMING Z790-PRO WIFI (1401 BIOS) and MSI NVIDIA GeForce RTX 4070 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 2401299-NE-2401275NE83
RTX 4070 SUPER
Intel Core i9-13900K testing with a ASUS TUF GAMING Z790-PRO WIFI (1401 BIOS) and MSI NVIDIA GeForce RTX 4070 12GB on EndeavourOS rolling via the Phoronix Test Suite.
,,"NVIDIA RTX 4070 SUPER","NVIDIA RTX 4070"
Processor,,Intel Core i9-13900K @ 5.50GHz (24 Cores / 32 Threads),Intel Core i9-13900K @ 5.50GHz (24 Cores / 32 Threads)
Motherboard,,ASUS TUF GAMING Z790-PRO WIFI (1401 BIOS),ASUS TUF GAMING Z790-PRO WIFI (1401 BIOS)
Chipset,,Intel Device 7a27,Intel Device 7a27
Memory,,32GB,32GB
Disk,,4001GB Seagate ZP4000GP304001,4001GB Seagate ZP4000GP304001
Graphics,,ASUS NVIDIA GeForce RTX 4070 SUPER 12GB,MSI NVIDIA GeForce RTX 4070 12GB
Audio,,Realtek ALC1220,Realtek ALC1220
Monitor,,ARZOPA,ARZOPA
Network,,Intel I226-V + Intel Device 7a70,Intel I226-V + Intel Device 7a70
OS,,EndeavourOS rolling,EndeavourOS rolling
Kernel,,6.7.1-arch1-1 (x86_64),6.7.1-arch1-1 (x86_64)
Desktop,,KDE Plasma 5.27.10,KDE Plasma 5.27.10
Display Server,,X Server 1.21.1.11,X Server 1.21.1.11
Display Driver,,NVIDIA 550.40.07,NVIDIA 550.40.07
OpenGL,,4.6.0,4.6.0
OpenCL,,OpenCL 3.0 CUDA 12.4.74,OpenCL 3.0 CUDA 12.4.74
Compiler,,GCC 13.2.1 20230801,GCC 13.2.1 20230801 + CUDA 12.3
File-System,,ext4,ext4
Screen Resolution,,1920x1080,1920x1080
,,"NVIDIA RTX 4070 SUPER","NVIDIA RTX 4070"
"TensorFlow - Device: GPU - Batch Size: 64 - Model: VGG-16 (images/sec)",HIB,,1.50
"TensorFlow - Device: GPU - Batch Size: 32 - Model: VGG-16 (images/sec)",HIB,1.50,1.5
"TensorFlow - Device: GPU - Batch Size: 512 - Model: AlexNet (images/sec)",HIB,35.10,35.21
"TensorFlow - Device: GPU - Batch Size: 64 - Model: ResNet-50 (images/sec)",HIB,5.55,5.55
"TensorFlow - Device: GPU - Batch Size: 16 - Model: VGG-16 (images/sec)",HIB,1.48,1.50
"NCNN - Target: Vulkan GPU - Model: googlenet (ms)",LIB,11.04,6.06
"NCNN - Target: Vulkan GPU - Model: blazeface (ms)",LIB,0.84,0.84
"NCNN - Target: Vulkan GPU - Model: FastestDet (ms)",LIB,2.86,2.34
"NCNN - Target: Vulkan GPU - Model: vision_transformer (ms)",LIB,844.61,281.56
"NCNN - Target: Vulkan GPU - Model: regnety_400m (ms)",LIB,11.11,6.21
"NCNN - Target: Vulkan GPU - Model: squeezenet_ssd (ms)",LIB,6.86,5.18
"NCNN - Target: Vulkan GPU - Model: yolov4-tiny (ms)",LIB,63.82,20.74
"NCNN - Target: Vulkan GPU - Model: resnet50 (ms)",LIB,46.26,8.24
"NCNN - Target: Vulkan GPU - Model: alexnet (ms)",LIB,16.17,5.78
"NCNN - Target: Vulkan GPU - Model: resnet18 (ms)",LIB,8.97,5.11
"NCNN - Target: Vulkan GPU - Model: vgg16 (ms)",LIB,117.81,45.52
"NCNN - Target: Vulkan GPU - Model: efficientnet-b0 (ms)",LIB,5.07,3.46
"NCNN - Target: Vulkan GPU - Model: mnasnet (ms)",LIB,3.85,2.22
"NCNN - Target: Vulkan GPU - Model: shufflenet-v2 (ms)",LIB,2.31,2.08
"NCNN - Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 (ms)",LIB,2.25,2.15
"NCNN - Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 (ms)",LIB,3.03,2.48
"NCNN - Target: Vulkan GPU - Model: mobilenet (ms)",LIB,8.62,7.20
"TensorFlow - Device: GPU - Batch Size: 32 - Model: ResNet-50 (images/sec)",HIB,5.51,5.55
"TensorFlow - Device: GPU - Batch Size: 256 - Model: AlexNet (images/sec)",HIB,34.16,
"TensorFlow - Device: GPU - Batch Size: 64 - Model: GoogLeNet (images/sec)",HIB,15.52,15.54
"TensorFlow - Device: GPU - Batch Size: 16 - Model: ResNet-50 (images/sec)",HIB,5.46,5.49
"TensorFlow - Device: GPU - Batch Size: 32 - Model: GoogLeNet (images/sec)",HIB,15.61,15.63
"GpuOwl - Exponent: 77936867 (Iterations / Second)",HIB,646.41,530.32
"GpuOwl - Exponent: 332220523 (Iterations / Second)",HIB,137.44,112.61
"OctaneBench - Total Score (Score)",HIB,720.973789,647.997867
"TensorFlow - Device: GPU - Batch Size: 64 - Model: AlexNet (images/sec)",HIB,33.97,33.93
"GpuOwl - Exponent: 57885161 (Iterations / Second)",HIB,869.07,714.80
"TensorFlow - Device: GPU - Batch Size: 16 - Model: GoogLeNet (images/sec)",HIB,15.67,15.66
"VkFFT - Test: FFT + iFFT C2C Bluestein benchmark in double precision (Benchmark Score)",HIB,4451,3886
"VkFFT - Test: FFT + iFFT C2C multidimensional in single precision (Benchmark Score)",HIB,50299,47212
"vkpeak - (GFLOPS)",HIB,,
"FAHBench - (Ns/Day)",HIB,366.0576,317.1952
"TensorFlow - Device: GPU - Batch Size: 32 - Model: AlexNet (images/sec)",HIB,33.4,33.32
"VkFFT - Test: FFT + iFFT R2C / C2R (Benchmark Score)",HIB,54794,47097
"VkResample - Upscale: 2x - Precision: Double (ms)",LIB,339.593,415.160
"IndigoBench - Acceleration: OpenCL GPU - Scene: Bedroom (M samples/s)",HIB,19.801,18.203
"LuxCoreRender - Scene: LuxCore Benchmark - Acceleration: GPU (M samples/sec)",HIB,12.82,10.92
"LuxCoreRender - Scene: DLSC - Acceleration: GPU (M samples/sec)",HIB,13.59,11.74
"IndigoBench - Acceleration: OpenCL GPU - Scene: Supercar (M samples/s)",HIB,52.813,48.517
"VkFFT - Test: FFT + iFFT C2C 1D batched in double precision (Benchmark Score)",HIB,24317,22390
"VkFFT - Test: FFT + iFFT C2C Bluestein in single precision (Benchmark Score)",HIB,15166,13714
"LuxCoreRender - Scene: Orange Juice - Acceleration: GPU (M samples/sec)",HIB,11.72,10.40
"LuxCoreRender - Scene: Danish Mood - Acceleration: GPU (M samples/sec)",HIB,10.56,8.89
"Blender - Blend File: Barbershop - Compute: NVIDIA OptiX (sec)",LIB,51.30,58.44
"VkFFT - Test: FFT + iFFT C2C 1D batched in single precision (Benchmark Score)",HIB,73929,77774
"VkFFT - Test: FFT + iFFT C2C 1D batched in single precision, no reshuffling (Benchmark Score)",HIB,75078,79057
"TensorFlow - Device: GPU - Batch Size: 256 - Model: VGG-16 (images/sec)",HIB,,
"TensorFlow - Device: GPU - Batch Size: 1 - Model: VGG-16 (images/sec)",HIB,1.35,1.36
"Libplacebo - Test: av1_grain_lap (FPS)",HIB,4171.00,4103.40
"Libplacebo - Test: hdr_lut (FPS)",HIB,3905.98,3927.11
"Libplacebo - Test: hdr_peakdetect (FPS)",HIB,3292.37,3310.02
"Libplacebo - Test: polar_nocompute (FPS)",HIB,2327.55,1968.37
"Libplacebo - Test: deband_heavy (FPS)",HIB,2186.70,1843.26
"TensorFlow - Device: GPU - Batch Size: 512 - Model: VGG-16 (images/sec)",HIB,,
"RealSR-NCNN - Scale: 4x - TAA: Yes (sec)",LIB,34.885,42.852
"TensorFlow - Device: GPU - Batch Size: 16 - Model: AlexNet (images/sec)",HIB,31.59,31.45
"NAMD CUDA - ATPase Simulation - 327,506 Atoms (days/ns)",LIB,0.06791,0.07498
"VkFFT - Test: FFT + iFFT C2C 1D batched in half precision (Benchmark Score)",HIB,131705,137762
"Blender - Blend File: Fishy Cat - Compute: NVIDIA OptiX (sec)",LIB,9.45,11.03
"PyTorch - Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_l (batches/sec)",HIB,102.60,101.55
"ViennaCL - Test: CPU BLAS - dGEMM-TT (GFLOPs/s)",HIB,122,118
"ViennaCL - Test: CPU BLAS - dGEMM-TN (GFLOPs/s)",HIB,115,121
"ViennaCL - Test: CPU BLAS - dGEMM-NT (GFLOPs/s)",HIB,117,122
"ViennaCL - Test: CPU BLAS - dGEMM-NN (GFLOPs/s)",HIB,119,122
"ViennaCL - Test: CPU BLAS - dGEMV-T (GB/s)",HIB,109,109
"ViennaCL - Test: CPU BLAS - dGEMV-N (GB/s)",HIB,102,103
"ViennaCL - Test: CPU BLAS - dDOT (GB/s)",HIB,96.8,96.7
"ViennaCL - Test: CPU BLAS - dAXPY (GB/s)",HIB,87.2,86.8
"ViennaCL - Test: CPU BLAS - dCOPY (GB/s)",HIB,70.8,71.0
"ViennaCL - Test: CPU BLAS - sDOT (GB/s)",HIB,165,166
"ViennaCL - Test: CPU BLAS - sAXPY (GB/s)",HIB,156,153
"ViennaCL - Test: CPU BLAS - sCOPY (GB/s)",HIB,132,131
"ViennaCL - Test: OpenCL BLAS - dGEMM-TT (GFLOPs/s)",HIB,613,502
"ViennaCL - Test: OpenCL BLAS - dGEMM-TN (GFLOPs/s)",HIB,599,494
"ViennaCL - Test: OpenCL BLAS - dGEMM-NT (GFLOPs/s)",HIB,584,477
"ViennaCL - Test: OpenCL BLAS - dGEMM-NN (GFLOPs/s)",HIB,577,473
"ViennaCL - Test: OpenCL BLAS - dGEMV-T (GB/s)",HIB,389,387
"ViennaCL - Test: OpenCL BLAS - dGEMV-N (GB/s)",HIB,210,209
"ViennaCL - Test: OpenCL BLAS - dDOT (GB/s)",HIB,458,456
"ViennaCL - Test: OpenCL BLAS - dAXPY (GB/s)",HIB,437,455
"ViennaCL - Test: OpenCL BLAS - dCOPY (GB/s)",HIB,423,423
"ViennaCL - Test: OpenCL BLAS - sDOT (GB/s)",HIB,370,362
"ViennaCL - Test: OpenCL BLAS - sAXPY (GB/s)",HIB,392,389
"ViennaCL - Test: OpenCL BLAS - sCOPY (GB/s)",HIB,334,330
"RealSR-NCNN - Scale: 4x - TAA: No (sec)",LIB,6.323,7.092
"TensorFlow - Device: GPU - Batch Size: 1 - Model: ResNet-50 (images/sec)",HIB,4.35,4.34
"PyTorch - Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_l (batches/sec)",HIB,103.17,101.24
"PyTorch - Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_l (batches/sec)",HIB,103.57,101.43
"Blender - Blend File: BMW27 - Compute: NVIDIA OptiX (sec)",LIB,5.57,6.21
"Blender - Blend File: Pabellon Barcelona - Compute: NVIDIA OptiX (sec)",LIB,14.29,16.55
"PyTorch - Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152 (batches/sec)",HIB,196.07,186.63
"Blender - Blend File: Classroom - Compute: NVIDIA OptiX (sec)",LIB,12.60,14.86
"PyTorch - Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152 (batches/sec)",HIB,195.30,187.51
"ProjectPhysX OpenCL-Benchmark - Operation: Memory Bandwidth Coalesced Write (GB/s)",HIB,455.01,459.43
"ProjectPhysX OpenCL-Benchmark - Operation: Memory Bandwidth Coalesced Read (GB/s)",HIB,464.86,465.18
"ProjectPhysX OpenCL-Benchmark - Operation: INT8 Compute (TIOPs/s)",HIB,14.307,12.116
"ProjectPhysX OpenCL-Benchmark - Operation: INT16 Compute (TIOPs/s)",HIB,17.170,14.284
"ProjectPhysX OpenCL-Benchmark - Operation: INT32 Compute (TIOPs/s)",HIB,19.889,16.377
"ProjectPhysX OpenCL-Benchmark - Operation: INT64 Compute (TIOPs/s)",HIB,4.214,3.443
"ProjectPhysX OpenCL-Benchmark - Operation: FP32 Compute (TFLOPs/s)",HIB,38.594,31.768
"ProjectPhysX OpenCL-Benchmark - Operation: FP64 Compute (TFLOPs/s)",HIB,0.621,0.510
"PyTorch - Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152 (batches/sec)",HIB,194.58,187.27
"PyTorch - Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152 (batches/sec)",HIB,195.40,187.26
"PyTorch - Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_l (batches/sec)",HIB,106.37,107.59
"PyTorch - Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_l (batches/sec)",HIB,102.60,102.90
"TensorFlow - Device: GPU - Batch Size: 1 - Model: GoogLeNet (images/sec)",HIB,12.62,12.78
"VkResample - Upscale: 2x - Precision: Single (ms)",LIB,18.489,18.016
"PyTorch - Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_l (batches/sec)",HIB,,103.68
"TensorFlow - Device: GPU - Batch Size: 1 - Model: AlexNet (images/sec)",HIB,13.92,14.04
"PyTorch - Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50 (batches/sec)",HIB,507.45,458.36
"clpeak - OpenCL Test: Double-Precision Double (GFLOPS)",HIB,630.11,515.17
"PyTorch - Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50 (batches/sec)",HIB,504.67,459.93
"LuxCoreRender - Scene: Rainbow Colors and Prism - Acceleration: GPU (M samples/sec)",HIB,27.67,23.26
"Hashcat - Benchmark: SHA1 (H/s)",HIB,22132600000,18202466667
"Hashcat - Benchmark: SHA-512 (H/s)",HIB,3232733333,2673300000
"Hashcat - Benchmark: MD5 (H/s)",HIB,67583033333,56147866667
"PyTorch - Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152 (batches/sec)",HIB,201.94,198.18
"PyTorch - Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50 (batches/sec)",HIB,509.45,458.39
"PyTorch - Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152 (batches/sec)",HIB,195.39,187.69
"PyTorch - Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50 (batches/sec)",HIB,501.50,459.94
"PyTorch - Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50 (batches/sec)",HIB,504.27,459.27
"PlaidML - FP16: No - Mode: Training - Network: Mobilenet - Device: OpenCL (Examples/sec)",HIB,,
"Hashcat - Benchmark: TrueCrypt RIPEMD160 + XTS (H/s)",HIB,802967,660967
"Rodinia - Test: OpenCL Particle Filter (sec)",LIB,3.480,4.098
"cl-mem - Benchmark: Copy (GB/s)",HIB,331.8,330.3
"cl-mem - Benchmark: Read (GB/s)",HIB,446.2,446.3
"cl-mem - Benchmark: Write (GB/s)",HIB,407.5,406.7
"PyTorch - Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50 (batches/sec)",HIB,557.73,546.76
"PlaidML - FP16: No - Mode: Inference - Network: DenseNet 201 - Device: OpenCL (Examples/sec)",HIB,,
"Hashcat - Benchmark: 7-Zip (H/s)",HIB,1176467,976967
"Waifu2x-NCNN Vulkan - Scale: 2x - Denoise: 3 - TAA: Yes (sec)",LIB,2.855,3.168
"Caffe - Model: GoogleNet - Acceleration: NVIDIA CUDA - Iterations: 1000 (ms)",LIB,,
"Caffe - Model: AlexNet - Acceleration: NVIDIA CUDA - Iterations: 1000 (ms)",LIB,,
"Caffe - Model: AlexNet - Acceleration: NVIDIA CUDA - Iterations: 100 (ms)",LIB,,
"Caffe - Model: GoogleNet - Acceleration: NVIDIA CUDA - Iterations: 200 (ms)",LIB,,
"Caffe - Model: AlexNet - Acceleration: NVIDIA CUDA - Iterations: 200 (ms)",LIB,,
"Caffe - Model: GoogleNet - Acceleration: NVIDIA CUDA - Iterations: 100 (ms)",LIB,,
"clpeak - OpenCL Test: Global Memory Bandwidth (GBPS)",HIB,437.65,437.21
"MandelGPU - OpenCL Device: GPU (Samples/sec)",HIB,587219538.2,516770131.2
"PlaidML - FP16: No - Mode: Inference - Network: Mobilenet - Device: OpenCL (Examples/sec)",HIB,,
"FinanceBench - Benchmark: Black-Scholes OpenCL (ms)",LIB,5.912,6.906
"Waifu2x-NCNN Vulkan - Scale: 2x - Denoise: 3 - TAA: No (sec)",LIB,,
"PlaidML - FP16: Yes - Mode: Inference - Network: Mobilenet - Device: OpenCL (Examples/sec)",HIB,,
"PlaidML - FP16: No - Mode: Inference - Network: IMDB LSTM - Device: OpenCL (Examples/sec)",HIB,,
"clpeak - OpenCL Test: Integer Compute INT (GIOPS)",HIB,18170.54,14555.19
"clpeak - OpenCL Test: Single-Precision Float (GFLOPS)",HIB,35492.69,28479.39
"NeatBench - Acceleration: GPU (FPS)",HIB,4070,4070
"LeelaChessZero - Backend: OpenCL (Nodes/s)",HIB,,
"Libplacebo - (FPS)",HIB,,
"ArrayFire - Test: Conjugate Gradient OpenCL ()",LIB,,
"NCNN - Target: Vulkan GPU (ms)",LIB,,