102424machinelearningtest Intel Core i9-12900K testing with a ASUS PRIME Z790-V AX (1802 BIOS) and ASUS NVIDIA w Dual GeForce RTX 3090 24GB w NVLink on Ubuntu 24.04 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2410281-NE-102424MAC72&gru .
102424machinelearningtest Processor Motherboard Chipset Memory Disk Graphics Audio Monitor Network OS Kernel Desktop Display Server Display Driver OpenGL OpenCL Compiler File-System Screen Resolution ASUS NVIDIA GeForce RTX 3090 Intel Core i9-12900K @ 5.10GHz (16 Cores / 24 Threads) ASUS PRIME Z790-V AX (1802 BIOS) Intel Raptor Lake-S PCH 96GB 2000GB Samsung SSD 970 EVO Plus 2TB ASUS NVIDIA GeForce RTX 3090 24GB Intel Raptor Lake HD Audio S24F350 Realtek RTL8111/8168/8211/8411 + Realtek Device b851 Ubuntu 24.04 6.8.0-47-generic (x86_64) GNOME Shell 46.0 X Server + Wayland NVIDIA 560.35.03 4.6.0 OpenCL 3.0 CUDA 12.6.65 GCC 13.2.0 + CUDA 12.5 ext4 1920x1080 OpenBenchmarking.org - Transparent Huge Pages: madvise - PRIMUS_libGLa=/usr/lib/nvidia-current/libGL.so.1:/usr/lib32/nvidia-current/libGL.so.1:/usr/lib/x86_64-linux-gnu/libGL.so.1:/usr/lib/i386-linux-gnu/libGL.so.1 PRIMUS_libGLd=/usr/$LIB/libGL.so.1:/usr/lib/$LIB/libGL.so.1:/usr/$LIB/mesa/libGL.so.1:/usr/lib/$LIB/mesa/libGL.so.1 - --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-backtrace --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-uJ7kn6/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-uJ7kn6/gcc-13-13.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v - Scaling Governor: intel_pstate powersave (EPP: balance_performance) - CPU Microcode: 0x37 - Thermald 2.5.6 - BAR1 / Visible vRAM Size: 32768 MiB - vBIOS Version: 94.02.4b.00.0b - GPU Compute Cores: 10496 - Python 3.12.3 - gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + reg_file_data_sampling: Mitigation of Clear Register File + retbleed: Not affected + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced / Automatic IBRS; IBPB: conditional; RSB filling; PBRSB-eIBRS: SW sequence; BHI: BHI_DIS_S + srbds: Not affected + tsx_async_abort: Not affected
102424machinelearningtest pytorch: CPU - 1 - ResNet-50 pytorch: CPU - 1 - ResNet-152 pytorch: CPU - 16 - ResNet-50 pytorch: CPU - 32 - ResNet-50 pytorch: CPU - 64 - ResNet-50 pytorch: CPU - 16 - ResNet-152 pytorch: CPU - 256 - ResNet-50 pytorch: CPU - 32 - ResNet-152 pytorch: CPU - 512 - ResNet-50 pytorch: CPU - 64 - ResNet-152 pytorch: CPU - 256 - ResNet-152 pytorch: CPU - 512 - ResNet-152 pytorch: CPU - 1 - Efficientnet_v2_l pytorch: CPU - 16 - Efficientnet_v2_l pytorch: CPU - 32 - Efficientnet_v2_l pytorch: CPU - 64 - Efficientnet_v2_l pytorch: CPU - 256 - Efficientnet_v2_l pytorch: CPU - 512 - Efficientnet_v2_l pytorch: NVIDIA CUDA GPU - 1 - ResNet-50 pytorch: NVIDIA CUDA GPU - 1 - ResNet-152 pytorch: NVIDIA CUDA GPU - 16 - ResNet-50 pytorch: NVIDIA CUDA GPU - 32 - ResNet-50 pytorch: NVIDIA CUDA GPU - 64 - ResNet-50 pytorch: NVIDIA CUDA GPU - 16 - ResNet-152 pytorch: NVIDIA CUDA GPU - 256 - ResNet-50 pytorch: NVIDIA CUDA GPU - 32 - ResNet-152 pytorch: NVIDIA CUDA GPU - 512 - ResNet-50 pytorch: NVIDIA CUDA GPU - 64 - ResNet-152 pytorch: NVIDIA CUDA GPU - 256 - ResNet-152 pytorch: NVIDIA CUDA GPU - 512 - ResNet-152 pytorch: NVIDIA CUDA GPU - 1 - Efficientnet_v2_l pytorch: NVIDIA CUDA GPU - 16 - Efficientnet_v2_l pytorch: NVIDIA CUDA GPU - 32 - Efficientnet_v2_l pytorch: NVIDIA CUDA GPU - 64 - Efficientnet_v2_l pytorch: NVIDIA CUDA GPU - 256 - Efficientnet_v2_l pytorch: NVIDIA CUDA GPU - 512 - Efficientnet_v2_l openvino: Face Detection FP16 - CPU openvino: Person Detection FP16 - CPU openvino: Person Detection FP32 - CPU openvino: Vehicle Detection FP16 - CPU openvino: Face Detection FP16-INT8 - CPU openvino: Face Detection Retail FP16 - CPU openvino: Road Segmentation ADAS FP16 - CPU openvino: Vehicle Detection FP16-INT8 - CPU openvino: Weld Porosity Detection FP16 - CPU openvino: Face Detection Retail FP16-INT8 - CPU openvino: Road Segmentation ADAS FP16-INT8 - CPU openvino: Machine Translation EN To DE FP16 - CPU openvino: Weld Porosity Detection FP16-INT8 - CPU openvino: Person Vehicle Bike Detection FP16 - CPU openvino: Noise Suppression Poconet-Like FP16 - CPU openvino: Handwritten English Recognition FP16 - CPU openvino: Person Re-Identification Retail FP16 - CPU openvino: Age Gender Recognition Retail 0013 FP16 - CPU openvino: Handwritten English Recognition FP16-INT8 - CPU openvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPU shoc: OpenCL - Triad shoc: OpenCL - Reduction shoc: OpenCL - Bus Speed Download shoc: OpenCL - Bus Speed Readback shoc: OpenCL - Texture Read Bandwidth shoc: OpenCL - S3D shoc: OpenCL - FFT SP shoc: OpenCL - GEMM SGEMM_N shoc: OpenCL - Max SP Flops shoc: OpenCL - MD5 Hash tensorflow: CPU - 1 - VGG-16 tensorflow: GPU - 1 - VGG-16 tensorflow: CPU - 1 - AlexNet tensorflow: CPU - 16 - VGG-16 tensorflow: CPU - 32 - VGG-16 tensorflow: CPU - 64 - VGG-16 tensorflow: GPU - 1 - AlexNet tensorflow: GPU - 16 - VGG-16 tensorflow: GPU - 32 - VGG-16 tensorflow: GPU - 64 - VGG-16 tensorflow: CPU - 16 - AlexNet tensorflow: CPU - 256 - VGG-16 tensorflow: CPU - 32 - AlexNet tensorflow: CPU - 512 - VGG-16 tensorflow: CPU - 64 - AlexNet tensorflow: GPU - 16 - AlexNet tensorflow: GPU - 256 - VGG-16 tensorflow: GPU - 32 - AlexNet tensorflow: GPU - 512 - VGG-16 tensorflow: GPU - 64 - AlexNet tensorflow: CPU - 1 - GoogLeNet tensorflow: CPU - 1 - ResNet-50 tensorflow: CPU - 256 - AlexNet tensorflow: CPU - 512 - AlexNet tensorflow: GPU - 1 - GoogLeNet tensorflow: GPU - 1 - ResNet-50 tensorflow: GPU - 256 - AlexNet tensorflow: GPU - 512 - AlexNet tensorflow: CPU - 16 - GoogLeNet tensorflow: CPU - 16 - ResNet-50 tensorflow: CPU - 32 - GoogLeNet tensorflow: CPU - 32 - ResNet-50 tensorflow: CPU - 64 - GoogLeNet tensorflow: CPU - 64 - ResNet-50 tensorflow: GPU - 16 - GoogLeNet tensorflow: GPU - 16 - ResNet-50 tensorflow: GPU - 32 - GoogLeNet tensorflow: GPU - 32 - ResNet-50 tensorflow: GPU - 64 - GoogLeNet tensorflow: GPU - 64 - ResNet-50 tensorflow: CPU - 256 - GoogLeNet tensorflow: CPU - 256 - ResNet-50 tensorflow: CPU - 512 - GoogLeNet tensorflow: CPU - 512 - ResNet-50 tensorflow: GPU - 256 - GoogLeNet tensorflow: GPU - 256 - ResNet-50 tensorflow: GPU - 512 - GoogLeNet tensorflow: GPU - 512 - ResNet-50 onnx: yolov4 - CPU - Parallel onnx: yolov4 - CPU - Standard onnx: ZFNet-512 - CPU - Parallel onnx: ZFNet-512 - CPU - Standard onnx: T5 Encoder - CPU - Parallel onnx: T5 Encoder - CPU - Standard onnx: CaffeNet 12-int8 - CPU - Parallel onnx: CaffeNet 12-int8 - CPU - Standard onnx: fcn-resnet101-11 - CPU - Parallel onnx: fcn-resnet101-11 - CPU - Standard onnx: ResNet50 v1-12-int8 - CPU - Parallel onnx: ResNet50 v1-12-int8 - CPU - Standard onnx: super-resolution-10 - CPU - Parallel onnx: super-resolution-10 - CPU - Standard onnx: ResNet101_DUC_HDC-12 - CPU - Parallel onnx: ResNet101_DUC_HDC-12 - CPU - Standard lczero: BLAS numpy: onnx: yolov4 - CPU - Parallel onnx: yolov4 - CPU - Standard onnx: ZFNet-512 - CPU - Parallel onnx: ZFNet-512 - CPU - Standard onnx: T5 Encoder - CPU - Parallel onnx: T5 Encoder - CPU - Standard onnx: CaffeNet 12-int8 - CPU - Parallel onnx: CaffeNet 12-int8 - CPU - Standard onnx: fcn-resnet101-11 - CPU - Parallel onnx: fcn-resnet101-11 - CPU - Standard onnx: ResNet50 v1-12-int8 - CPU - Parallel onnx: ResNet50 v1-12-int8 - CPU - Standard onnx: super-resolution-10 - CPU - Parallel onnx: super-resolution-10 - CPU - Standard onnx: ResNet101_DUC_HDC-12 - CPU - Parallel onnx: ResNet101_DUC_HDC-12 - CPU - Standard tensorflow-lite: SqueezeNet tensorflow-lite: Inception V4 tensorflow-lite: NASNet Mobile tensorflow-lite: Mobilenet Float tensorflow-lite: Mobilenet Quant tensorflow-lite: Inception ResNet V2 onednn: IP Shapes 1D - CPU onednn: IP Shapes 3D - CPU onednn: Convolution Batch Shapes Auto - CPU onednn: Deconvolution Batch shapes_1d - CPU onednn: Deconvolution Batch shapes_3d - CPU onednn: Recurrent Neural Network Training - CPU onednn: Recurrent Neural Network Inference - CPU mnn: nasnet mnn: mobilenetV3 mnn: squeezenetv1.1 mnn: resnet-v2-50 mnn: SqueezeNetV1.0 mnn: MobileNetV2_224 mnn: mobilenet-v1-1.0 mnn: inception-v3 ncnn: CPU - mobilenet ncnn: CPU-v2-v2 - mobilenet-v2 ncnn: CPU-v3-v3 - mobilenet-v3 ncnn: CPU - shufflenet-v2 ncnn: CPU - mnasnet ncnn: CPU - efficientnet-b0 ncnn: CPU - blazeface ncnn: CPU - googlenet ncnn: CPU - vgg16 ncnn: CPU - resnet18 ncnn: CPU - alexnet ncnn: CPU - resnet50 ncnn: CPU - yolov4-tiny ncnn: CPU - squeezenet_ssd ncnn: CPU - regnety_400m ncnn: CPU - vision_transformer ncnn: CPU - FastestDet ncnn: Vulkan GPU - mobilenet ncnn: Vulkan GPU-v2-v2 - mobilenet-v2 ncnn: Vulkan GPU-v3-v3 - mobilenet-v3 ncnn: Vulkan GPU - shufflenet-v2 ncnn: Vulkan GPU - mnasnet ncnn: Vulkan GPU - efficientnet-b0 ncnn: Vulkan GPU - blazeface ncnn: Vulkan GPU - googlenet ncnn: Vulkan GPU - vgg16 ncnn: Vulkan GPU - resnet18 ncnn: Vulkan GPU - alexnet ncnn: Vulkan GPU - resnet50 ncnn: Vulkan GPU - yolov4-tiny ncnn: Vulkan GPU - squeezenet_ssd ncnn: Vulkan GPU - regnety_400m ncnn: Vulkan GPU - vision_transformer ncnn: Vulkan GPU - FastestDet openvino: Face Detection FP16 - CPU openvino: Person Detection FP16 - CPU openvino: Person Detection FP32 - CPU openvino: Vehicle Detection FP16 - CPU openvino: Face Detection FP16-INT8 - CPU openvino: Face Detection Retail FP16 - CPU openvino: Road Segmentation ADAS FP16 - CPU openvino: Vehicle Detection FP16-INT8 - CPU openvino: Weld Porosity Detection FP16 - CPU openvino: Face Detection Retail FP16-INT8 - CPU openvino: Road Segmentation ADAS FP16-INT8 - CPU openvino: Machine Translation EN To DE FP16 - CPU openvino: Weld Porosity Detection FP16-INT8 - CPU openvino: Person Vehicle Bike Detection FP16 - CPU openvino: Noise Suppression Poconet-Like FP16 - CPU openvino: Handwritten English Recognition FP16 - CPU openvino: Person Re-Identification Retail FP16 - CPU openvino: Age Gender Recognition Retail 0013 FP16 - CPU openvino: Handwritten English Recognition FP16-INT8 - CPU openvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPU opencv: DNN - Deep Neural Network deepspeech: CPU rbenchmark: rnnoise: 26 Minute Long Talking Sample scikit-learn: GLM scikit-learn: SAGA scikit-learn: Tree scikit-learn: Lasso scikit-learn: Sparsify scikit-learn: Plot Ward scikit-learn: MNIST Dataset scikit-learn: Plot Neighbors scikit-learn: SGD Regression scikit-learn: SGDOneClassSVM scikit-learn: Isolation Forest scikit-learn: Text Vectorizers scikit-learn: Plot Hierarchical scikit-learn: Plot OMP vs. LARS scikit-learn: Feature Expansions scikit-learn: LocalOutlierFactor scikit-learn: TSNE MNIST Dataset scikit-learn: Isotonic / Logistic scikit-learn: Plot Incremental PCA scikit-learn: Hist Gradient Boosting scikit-learn: Plot Parallel Pairwise scikit-learn: Sample Without Replacement scikit-learn: Covertype Dataset Benchmark scikit-learn: Hist Gradient Boosting Adult scikit-learn: Isotonic / Perturbed Logarithm scikit-learn: Hist Gradient Boosting Threading scikit-learn: Hist Gradient Boosting Higgs Boson scikit-learn: 20 Newsgroups / Logistic Regression scikit-learn: Plot Polynomial Kernel Approximation scikit-learn: Hist Gradient Boosting Categorical Only scikit-learn: Kernel PCA Solvers / Time vs. N Samples scikit-learn: Kernel PCA Solvers / Time vs. N Components scikit-learn: Sparse Rand Projections / 100 Iterations whisper-cpp: ggml-base.en - 2016 State of the Union whisper-cpp: ggml-small.en - 2016 State of the Union whisper-cpp: ggml-medium.en - 2016 State of the Union xnnpack: FP32MobileNetV1 xnnpack: FP32MobileNetV2 xnnpack: FP32MobileNetV3Large xnnpack: FP32MobileNetV3Small xnnpack: FP16MobileNetV1 xnnpack: FP16MobileNetV2 xnnpack: FP16MobileNetV3Large xnnpack: FP16MobileNetV3Small xnnpack: QS8MobileNetV2 ASUS NVIDIA GeForce RTX 3090 49.49 18.80 30.07 30.27 29.52 11.49 29.33 10.16 30.14 11.66 11.45 11.46 10.55 6.97 7.92 7.82 6.48 7.26 469.71 172.05 414.29 413.19 413.29 162.59 413.12 162.45 414.51 162.69 162.68 162.35 88.41 85.74 84.09 85.37 85.57 86.27 4.33 40.98 41.47 296.62 15.61 1180.46 86.71 712.75 450.44 1948.78 283.34 51.69 1544.54 528.13 656.86 225.32 659.72 11244.68 266.87 29697.31 6.6057 406.411 6.6413 6.7652 2178.63 455.955 2510.98 8456.99 41982.2 45.4489 4.12 1.57 15.83 8.97 9.22 9.31 14.14 2.25 2.27 2.27 129.70 9.11 167.17 8.95 192.13 38.93 2.28 41.45 2.29 42.49 47.92 14.04 227.40 245.67 12.00 5.62 44.05 44.64 101.50 27.80 96.69 26.82 94.21 26.52 25.99 7.93 26.63 7.98 26.91 8.00 93.45 27.49 93.78 28.13 27.04 8.04 27.08 8.04 10.49253 11.1456 79.4345 92.4654 115.166 152.798 307.372 646.109 1.65652 2.49779 162.891 313.235 71.8081 81.7772 0.743436 0.943867 166 644.54 95.5591 89.7581 12.5924 10.8141 8.68614 6.54304 3.25179 1.54657 604.549 400.351 6.14425 3.19151 13.9393 12.2276 1345.66 1059.49 1904.01 26961.0 311542 1383.02 2317.67 128179 2.89253 9.32676 8.24915 4.47259 5.57785 2702.00 1452.76 6.956 1.012 1.836 14.361 2.935 1.882 2.084 21.367 16.46 5.53 8.28 11.05 5.46 19.35 5.95 19.65 28.60 7.50 5.58 22.09 18.40 13.62 82.63 70.73 6.47 17.34 5.72 7.93 10.51 5.43 18.73 6.00 19.43 28.61 7.29 5.49 22.13 18.17 14.25 84.85 70.75 6.56 1375.14 146.25 144.47 20.16 382.74 5.02 69.09 8.35 44.26 2.97 21.09 115.87 12.81 11.29 9.02 88.65 9.01 1.66 74.85 0.62 25552 54.62917 0.0970 7.245 183.476 521.305 42.771 258.724 85.975 43.615 53.539 96.755 63.791 196.466 147.516 38.906 153.056 45.622 120.329 35.645 188.326 1447.693 21.307 1120.037 332.194 84.273 317.462 1173.890 1469.791 140.749 190.582 9.614 133.120 195.295 69.634 25.585 587.844 209.01863 638.01092 2000.10000 1394 1125 1585 820 2197 1646 1834 928 904 OpenBenchmarking.org
PyTorch Device: CPU - Batch Size: 1 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 1 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 11 22 33 44 55 SE +/- 0.18, N = 3 49.49 MIN: 30.42 / MAX: 50.09
PyTorch Device: CPU - Batch Size: 1 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 1 - Model: ResNet-152 ASUS NVIDIA GeForce RTX 3090 5 10 15 20 25 SE +/- 0.44, N = 15 18.80 MIN: 15.08 / MAX: 19.89
PyTorch Device: CPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 16 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 7 14 21 28 35 SE +/- 0.12, N = 3 30.07 MIN: 28.1 / MAX: 30.36
PyTorch Device: CPU - Batch Size: 32 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 32 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 7 14 21 28 35 SE +/- 0.11, N = 3 30.27 MIN: 28.61 / MAX: 30.57
PyTorch Device: CPU - Batch Size: 64 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 64 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 7 14 21 28 35 SE +/- 0.32, N = 15 29.52 MIN: 25.74 / MAX: 30.64
PyTorch Device: CPU - Batch Size: 16 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 16 - Model: ResNet-152 ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.20, N = 12 11.49 MIN: 10.07 / MAX: 12.18
PyTorch Device: CPU - Batch Size: 256 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 256 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 7 14 21 28 35 SE +/- 0.38, N = 15 29.33 MIN: 25.91 / MAX: 30.51
PyTorch Device: CPU - Batch Size: 32 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 32 - Model: ResNet-152 ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.01, N = 3 10.16 MIN: 10.01 / MAX: 10.25
PyTorch Device: CPU - Batch Size: 512 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 512 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 7 14 21 28 35 SE +/- 0.24, N = 3 30.14 MIN: 18.07 / MAX: 30.74
PyTorch Device: CPU - Batch Size: 64 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 64 - Model: ResNet-152 ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.05, N = 3 11.66 MIN: 11.02 / MAX: 11.79
PyTorch Device: CPU - Batch Size: 256 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 256 - Model: ResNet-152 ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.19, N = 12 11.45 MIN: 10.05 / MAX: 12.11
PyTorch Device: CPU - Batch Size: 512 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 512 - Model: ResNet-152 ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.14, N = 12 11.46 MIN: 10.06 / MAX: 11.81
PyTorch Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.12, N = 3 10.55 MIN: 6.85 / MAX: 13.27
PyTorch Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.22, N = 9 6.97 MIN: 6.34 / MAX: 8.11
PyTorch Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.05, N = 3 7.92 MIN: 7.36 / MAX: 8.02
PyTorch Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.07, N = 3 7.82 MIN: 6.39 / MAX: 7.98
PyTorch Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.01, N = 3 6.48 MIN: 6.44 / MAX: 6.62
PyTorch Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.24, N = 9 7.26 MIN: 6.42 / MAX: 8.05
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 100 200 300 400 500 SE +/- 1.86, N = 3 469.71 MIN: 339.62 / MAX: 480.89
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152 ASUS NVIDIA GeForce RTX 3090 40 80 120 160 200 SE +/- 0.36, N = 3 172.05 MIN: 143.74 / MAX: 175.36
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 90 180 270 360 450 SE +/- 0.07, N = 3 414.29 MIN: 339.59 / MAX: 422.13
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 90 180 270 360 450 SE +/- 0.17, N = 3 413.19 MIN: 321.33 / MAX: 421.07
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 90 180 270 360 450 SE +/- 0.26, N = 3 413.29 MIN: 337.68 / MAX: 420.31
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152 ASUS NVIDIA GeForce RTX 3090 40 80 120 160 200 SE +/- 0.56, N = 3 162.59 MIN: 114.4 / MAX: 167.22
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 90 180 270 360 450 SE +/- 0.66, N = 3 413.12 MIN: 161.19 / MAX: 421.75
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152 ASUS NVIDIA GeForce RTX 3090 40 80 120 160 200 SE +/- 0.21, N = 3 162.45 MIN: 87.19 / MAX: 165.45
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 90 180 270 360 450 SE +/- 0.17, N = 3 414.51 MIN: 337.19 / MAX: 422.34
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152 ASUS NVIDIA GeForce RTX 3090 40 80 120 160 200 SE +/- 0.16, N = 3 162.69 MIN: 143.93 / MAX: 165.52
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152 ASUS NVIDIA GeForce RTX 3090 40 80 120 160 200 SE +/- 0.10, N = 3 162.68 MIN: 145.92 / MAX: 165.55
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152 ASUS NVIDIA GeForce RTX 3090 40 80 120 160 200 SE +/- 0.22, N = 3 162.35 MIN: 99.28 / MAX: 165.46
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_l ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.70, N = 3 88.41 MIN: 76.75 / MAX: 90.99
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_l ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.47, N = 3 85.74 MIN: 75.44 / MAX: 87.56
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_l ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.95, N = 3 84.09 MIN: 70.71 / MAX: 86.82
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_l ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.50, N = 3 85.37 MIN: 74.95 / MAX: 88.01
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_l ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.11, N = 3 85.57 MIN: 73.68 / MAX: 87.24
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_l ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.12, N = 3 86.27 MIN: 76.41 / MAX: 88.03
OpenVINO Model: Face Detection FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Face Detection FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 0.9743 1.9486 2.9229 3.8972 4.8715 SE +/- 0.03, N = 3 4.33 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Person Detection FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Person Detection FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 9 18 27 36 45 SE +/- 0.13, N = 3 40.98 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Person Detection FP32 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Person Detection FP32 - Device: CPU ASUS NVIDIA GeForce RTX 3090 9 18 27 36 45 SE +/- 0.20, N = 3 41.47 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Vehicle Detection FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Vehicle Detection FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 60 120 180 240 300 SE +/- 1.40, N = 3 296.62 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Face Detection FP16-INT8 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Face Detection FP16-INT8 - Device: CPU ASUS NVIDIA GeForce RTX 3090 4 8 12 16 20 SE +/- 0.05, N = 3 15.61 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Face Detection Retail FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Face Detection Retail FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 300 600 900 1200 1500 SE +/- 1.52, N = 3 1180.46 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Road Segmentation ADAS FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Road Segmentation ADAS FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.37, N = 3 86.71 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Vehicle Detection FP16-INT8 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Vehicle Detection FP16-INT8 - Device: CPU ASUS NVIDIA GeForce RTX 3090 150 300 450 600 750 SE +/- 0.27, N = 3 712.75 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Weld Porosity Detection FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Weld Porosity Detection FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 100 200 300 400 500 SE +/- 0.70, N = 3 450.44 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Face Detection Retail FP16-INT8 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Face Detection Retail FP16-INT8 - Device: CPU ASUS NVIDIA GeForce RTX 3090 400 800 1200 1600 2000 SE +/- 3.09, N = 3 1948.78 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Road Segmentation ADAS FP16-INT8 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Road Segmentation ADAS FP16-INT8 - Device: CPU ASUS NVIDIA GeForce RTX 3090 60 120 180 240 300 SE +/- 0.63, N = 3 283.34 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Machine Translation EN To DE FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Machine Translation EN To DE FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 12 24 36 48 60 SE +/- 0.18, N = 3 51.69 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Weld Porosity Detection FP16-INT8 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Weld Porosity Detection FP16-INT8 - Device: CPU ASUS NVIDIA GeForce RTX 3090 300 600 900 1200 1500 SE +/- 0.90, N = 3 1544.54 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Person Vehicle Bike Detection FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Person Vehicle Bike Detection FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 110 220 330 440 550 SE +/- 0.48, N = 3 528.13 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Noise Suppression Poconet-Like FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Noise Suppression Poconet-Like FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 140 280 420 560 700 SE +/- 0.48, N = 3 656.86 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Handwritten English Recognition FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Handwritten English Recognition FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 50 100 150 200 250 SE +/- 0.26, N = 3 225.32 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Person Re-Identification Retail FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Person Re-Identification Retail FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 140 280 420 560 700 SE +/- 0.35, N = 3 659.72 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 2K 4K 6K 8K 10K SE +/- 7.15, N = 3 11244.68 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Handwritten English Recognition FP16-INT8 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Handwritten English Recognition FP16-INT8 - Device: CPU ASUS NVIDIA GeForce RTX 3090 60 120 180 240 300 SE +/- 0.68, N = 3 266.87 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2024.0 Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU ASUS NVIDIA GeForce RTX 3090 6K 12K 18K 24K 30K SE +/- 14.75, N = 3 29697.31 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: Triad OpenBenchmarking.org GB/s, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: Triad ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.0011, N = 3 6.6057 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: Reduction OpenBenchmarking.org GB/s, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: Reduction ASUS NVIDIA GeForce RTX 3090 90 180 270 360 450 SE +/- 0.22, N = 3 406.41 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: Bus Speed Download OpenBenchmarking.org GB/s, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: Bus Speed Download ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.0002, N = 3 6.6413 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: Bus Speed Readback OpenBenchmarking.org GB/s, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: Bus Speed Readback ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.0000, N = 3 6.7652 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: Texture Read Bandwidth OpenBenchmarking.org GB/s, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: Texture Read Bandwidth ASUS NVIDIA GeForce RTX 3090 500 1000 1500 2000 2500 SE +/- 2.20, N = 3 2178.63 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: S3D OpenBenchmarking.org GFLOPS, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: S3D ASUS NVIDIA GeForce RTX 3090 100 200 300 400 500 SE +/- 0.72, N = 3 455.96 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: FFT SP OpenBenchmarking.org GFLOPS, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: FFT SP ASUS NVIDIA GeForce RTX 3090 500 1000 1500 2000 2500 SE +/- 35.21, N = 3 2510.98 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: GEMM SGEMM_N OpenBenchmarking.org GFLOPS, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: GEMM SGEMM_N ASUS NVIDIA GeForce RTX 3090 2K 4K 6K 8K 10K SE +/- 38.88, N = 3 8456.99 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: Max SP Flops OpenBenchmarking.org GFLOPS, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: Max SP Flops ASUS NVIDIA GeForce RTX 3090 9K 18K 27K 36K 45K SE +/- 208.39, N = 3 41982.2 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
SHOC Scalable HeterOgeneous Computing Target: OpenCL - Benchmark: MD5 Hash OpenBenchmarking.org GHash/s, More Is Better SHOC Scalable HeterOgeneous Computing 2020-04-17 Target: OpenCL - Benchmark: MD5 Hash ASUS NVIDIA GeForce RTX 3090 10 20 30 40 50 SE +/- 0.01, N = 3 45.45 1. (CXX) g++ options: -O2 -lSHOCCommonMPI -lSHOCCommonOpenCL -lSHOCCommon -lOpenCL -lrt -lmpi_cxx -lmpi
TensorFlow Device: CPU - Batch Size: 1 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 1 - Model: VGG-16 ASUS NVIDIA GeForce RTX 3090 0.927 1.854 2.781 3.708 4.635 SE +/- 0.00, N = 3 4.12
TensorFlow Device: GPU - Batch Size: 1 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 1 - Model: VGG-16 ASUS NVIDIA GeForce RTX 3090 0.3533 0.7066 1.0599 1.4132 1.7665 SE +/- 0.02, N = 15 1.57
TensorFlow Device: CPU - Batch Size: 1 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 1 - Model: AlexNet ASUS NVIDIA GeForce RTX 3090 4 8 12 16 20 SE +/- 0.02, N = 3 15.83
TensorFlow Device: CPU - Batch Size: 16 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 16 - Model: VGG-16 ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.06, N = 3 8.97
TensorFlow Device: CPU - Batch Size: 32 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 32 - Model: VGG-16 ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.02, N = 3 9.22
TensorFlow Device: CPU - Batch Size: 64 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 64 - Model: VGG-16 ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.01, N = 3 9.31
TensorFlow Device: GPU - Batch Size: 1 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 1 - Model: AlexNet ASUS NVIDIA GeForce RTX 3090 4 8 12 16 20 SE +/- 0.07, N = 3 14.14
TensorFlow Device: GPU - Batch Size: 16 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 16 - Model: VGG-16 ASUS NVIDIA GeForce RTX 3090 0.5063 1.0126 1.5189 2.0252 2.5315 SE +/- 0.00, N = 3 2.25
TensorFlow Device: GPU - Batch Size: 32 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 32 - Model: VGG-16 ASUS NVIDIA GeForce RTX 3090 0.5108 1.0216 1.5324 2.0432 2.554 SE +/- 0.00, N = 3 2.27
TensorFlow Device: GPU - Batch Size: 64 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 64 - Model: VGG-16 ASUS NVIDIA GeForce RTX 3090 0.5108 1.0216 1.5324 2.0432 2.554 SE +/- 0.00, N = 3 2.27
TensorFlow Device: CPU - Batch Size: 16 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 16 - Model: AlexNet ASUS NVIDIA GeForce RTX 3090 30 60 90 120 150 SE +/- 0.01, N = 3 129.70
TensorFlow Device: CPU - Batch Size: 256 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 256 - Model: VGG-16 ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.01, N = 3 9.11
TensorFlow Device: CPU - Batch Size: 32 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 32 - Model: AlexNet ASUS NVIDIA GeForce RTX 3090 40 80 120 160 200 SE +/- 0.14, N = 3 167.17
TensorFlow Device: CPU - Batch Size: 512 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 512 - Model: VGG-16 ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.06, N = 3 8.95
TensorFlow Device: CPU - Batch Size: 64 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 64 - Model: AlexNet ASUS NVIDIA GeForce RTX 3090 40 80 120 160 200 SE +/- 0.32, N = 3 192.13
TensorFlow Device: GPU - Batch Size: 16 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 16 - Model: AlexNet ASUS NVIDIA GeForce RTX 3090 9 18 27 36 45 SE +/- 0.43, N = 3 38.93
TensorFlow Device: GPU - Batch Size: 256 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 256 - Model: VGG-16 ASUS NVIDIA GeForce RTX 3090 0.513 1.026 1.539 2.052 2.565 SE +/- 0.00, N = 3 2.28
TensorFlow Device: GPU - Batch Size: 32 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 32 - Model: AlexNet ASUS NVIDIA GeForce RTX 3090 9 18 27 36 45 SE +/- 0.40, N = 3 41.45
TensorFlow Device: GPU - Batch Size: 512 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 512 - Model: VGG-16 ASUS NVIDIA GeForce RTX 3090 0.5153 1.0306 1.5459 2.0612 2.5765 SE +/- 0.00, N = 3 2.29
TensorFlow Device: GPU - Batch Size: 64 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 64 - Model: AlexNet ASUS NVIDIA GeForce RTX 3090 10 20 30 40 50 SE +/- 0.02, N = 3 42.49
TensorFlow Device: CPU - Batch Size: 1 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 1 - Model: GoogLeNet ASUS NVIDIA GeForce RTX 3090 11 22 33 44 55 SE +/- 0.60, N = 3 47.92
TensorFlow Device: CPU - Batch Size: 1 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 1 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 4 8 12 16 20 SE +/- 0.07, N = 3 14.04
TensorFlow Device: CPU - Batch Size: 256 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 256 - Model: AlexNet ASUS NVIDIA GeForce RTX 3090 50 100 150 200 250 SE +/- 0.07, N = 3 227.40
TensorFlow Device: CPU - Batch Size: 512 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 512 - Model: AlexNet ASUS NVIDIA GeForce RTX 3090 50 100 150 200 250 SE +/- 0.02, N = 3 245.67
TensorFlow Device: GPU - Batch Size: 1 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 1 - Model: GoogLeNet ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.21, N = 15 12.00
TensorFlow Device: GPU - Batch Size: 1 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 1 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 1.2645 2.529 3.7935 5.058 6.3225 SE +/- 0.05, N = 3 5.62
TensorFlow Device: GPU - Batch Size: 256 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 256 - Model: AlexNet ASUS NVIDIA GeForce RTX 3090 10 20 30 40 50 SE +/- 0.40, N = 3 44.05
TensorFlow Device: GPU - Batch Size: 512 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 512 - Model: AlexNet ASUS NVIDIA GeForce RTX 3090 10 20 30 40 50 SE +/- 0.42, N = 3 44.64
TensorFlow Device: CPU - Batch Size: 16 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 16 - Model: GoogLeNet ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.15, N = 3 101.50
TensorFlow Device: CPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 16 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 7 14 21 28 35 SE +/- 0.02, N = 3 27.80
TensorFlow Device: CPU - Batch Size: 32 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 32 - Model: GoogLeNet ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.06, N = 3 96.69
TensorFlow Device: CPU - Batch Size: 32 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 32 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 6 12 18 24 30 SE +/- 0.01, N = 3 26.82
TensorFlow Device: CPU - Batch Size: 64 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 64 - Model: GoogLeNet ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.26, N = 3 94.21
TensorFlow Device: CPU - Batch Size: 64 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 64 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 6 12 18 24 30 SE +/- 0.01, N = 3 26.52
TensorFlow Device: GPU - Batch Size: 16 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 16 - Model: GoogLeNet ASUS NVIDIA GeForce RTX 3090 6 12 18 24 30 SE +/- 0.06, N = 3 25.99
TensorFlow Device: GPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 16 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.01, N = 3 7.93
TensorFlow Device: GPU - Batch Size: 32 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 32 - Model: GoogLeNet ASUS NVIDIA GeForce RTX 3090 6 12 18 24 30 SE +/- 0.04, N = 3 26.63
TensorFlow Device: GPU - Batch Size: 32 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 32 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.01, N = 3 7.98
TensorFlow Device: GPU - Batch Size: 64 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 64 - Model: GoogLeNet ASUS NVIDIA GeForce RTX 3090 6 12 18 24 30 SE +/- 0.01, N = 3 26.91
TensorFlow Device: GPU - Batch Size: 64 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 64 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.00, N = 3 8.00
TensorFlow Device: CPU - Batch Size: 256 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 256 - Model: GoogLeNet ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.03, N = 3 93.45
TensorFlow Device: CPU - Batch Size: 256 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 256 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 6 12 18 24 30 SE +/- 0.18, N = 3 27.49
TensorFlow Device: CPU - Batch Size: 512 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 512 - Model: GoogLeNet ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.03, N = 3 93.78
TensorFlow Device: CPU - Batch Size: 512 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: CPU - Batch Size: 512 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 7 14 21 28 35 SE +/- 0.01, N = 3 28.13
TensorFlow Device: GPU - Batch Size: 256 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 256 - Model: GoogLeNet ASUS NVIDIA GeForce RTX 3090 6 12 18 24 30 SE +/- 0.05, N = 3 27.04
TensorFlow Device: GPU - Batch Size: 256 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 256 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.00, N = 3 8.04
TensorFlow Device: GPU - Batch Size: 512 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 512 - Model: GoogLeNet ASUS NVIDIA GeForce RTX 3090 6 12 18 24 30 SE +/- 0.04, N = 3 27.08
TensorFlow Device: GPU - Batch Size: 512 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.16.1 Device: GPU - Batch Size: 512 - Model: ResNet-50 ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.01, N = 3 8.04
ONNX Runtime Model: yolov4 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: yolov4 - Device: CPU - Executor: Parallel ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.14, N = 15 10.49 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: yolov4 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: yolov4 - Device: CPU - Executor: Standard ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.13, N = 4 11.15 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ZFNet-512 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: ZFNet-512 - Device: CPU - Executor: Parallel ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 1.14, N = 3 79.43 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ZFNet-512 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: ZFNet-512 - Device: CPU - Executor: Standard ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.61, N = 3 92.47 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: T5 Encoder - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: T5 Encoder - Device: CPU - Executor: Parallel ASUS NVIDIA GeForce RTX 3090 30 60 90 120 150 SE +/- 0.72, N = 15 115.17 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: T5 Encoder - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: T5 Encoder - Device: CPU - Executor: Standard ASUS NVIDIA GeForce RTX 3090 30 60 90 120 150 SE +/- 0.46, N = 3 152.80 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: CaffeNet 12-int8 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: CaffeNet 12-int8 - Device: CPU - Executor: Parallel ASUS NVIDIA GeForce RTX 3090 70 140 210 280 350 SE +/- 0.56, N = 3 307.37 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard ASUS NVIDIA GeForce RTX 3090 140 280 420 560 700 SE +/- 0.70, N = 3 646.11 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel ASUS NVIDIA GeForce RTX 3090 0.3727 0.7454 1.1181 1.4908 1.8635 SE +/- 0.01666, N = 15 1.65652 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: fcn-resnet101-11 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: fcn-resnet101-11 - Device: CPU - Executor: Standard ASUS NVIDIA GeForce RTX 3090 0.562 1.124 1.686 2.248 2.81 SE +/- 0.00202, N = 3 2.49779 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Parallel ASUS NVIDIA GeForce RTX 3090 40 80 120 160 200 SE +/- 1.41, N = 15 162.89 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard ASUS NVIDIA GeForce RTX 3090 70 140 210 280 350 SE +/- 0.91, N = 3 313.24 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: super-resolution-10 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: super-resolution-10 - Device: CPU - Executor: Parallel ASUS NVIDIA GeForce RTX 3090 16 32 48 64 80 SE +/- 0.62, N = 15 71.81 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: super-resolution-10 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: super-resolution-10 - Device: CPU - Executor: Standard ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.01, N = 3 81.78 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Parallel ASUS NVIDIA GeForce RTX 3090 0.1673 0.3346 0.5019 0.6692 0.8365 SE +/- 0.010713, N = 3 0.743436 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.19 Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Standard ASUS NVIDIA GeForce RTX 3090 0.2124 0.4248 0.6372 0.8496 1.062 SE +/- 0.002603, N = 3 0.943867 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
LeelaChessZero Backend: BLAS OpenBenchmarking.org Nodes Per Second, More Is Better LeelaChessZero 0.31.1 Backend: BLAS ASUS NVIDIA GeForce RTX 3090 40 80 120 160 200 SE +/- 1.83, N = 5 166 1. (CXX) g++ options: -flto -pthread
Numpy Benchmark OpenBenchmarking.org Score, More Is Better Numpy Benchmark ASUS NVIDIA GeForce RTX 3090 140 280 420 560 700 SE +/- 0.55, N = 3 644.54
ONNX Runtime Model: yolov4 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: yolov4 - Device: CPU - Executor: Parallel ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 1.36, N = 15 95.56 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: yolov4 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: yolov4 - Device: CPU - Executor: Standard ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 1.09, N = 4 89.76 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ZFNet-512 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: ZFNet-512 - Device: CPU - Executor: Parallel ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.18, N = 3 12.59 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ZFNet-512 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: ZFNet-512 - Device: CPU - Executor: Standard ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.07, N = 3 10.81 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: T5 Encoder - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: T5 Encoder - Device: CPU - Executor: Parallel ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.05490, N = 15 8.68614 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: T5 Encoder - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: T5 Encoder - Device: CPU - Executor: Standard ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.01981, N = 3 6.54304 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: CaffeNet 12-int8 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: CaffeNet 12-int8 - Device: CPU - Executor: Parallel ASUS NVIDIA GeForce RTX 3090 0.7317 1.4634 2.1951 2.9268 3.6585 SE +/- 0.00596, N = 3 3.25179 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard ASUS NVIDIA GeForce RTX 3090 0.348 0.696 1.044 1.392 1.74 SE +/- 0.00165, N = 3 1.54657 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel ASUS NVIDIA GeForce RTX 3090 130 260 390 520 650 SE +/- 6.22, N = 15 604.55 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: fcn-resnet101-11 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: fcn-resnet101-11 - Device: CPU - Executor: Standard ASUS NVIDIA GeForce RTX 3090 90 180 270 360 450 SE +/- 0.32, N = 3 400.35 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Parallel ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.05367, N = 15 6.14425 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard ASUS NVIDIA GeForce RTX 3090 0.7181 1.4362 2.1543 2.8724 3.5905 SE +/- 0.00923, N = 3 3.19151 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: super-resolution-10 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: super-resolution-10 - Device: CPU - Executor: Parallel ASUS NVIDIA GeForce RTX 3090 4 8 12 16 20 SE +/- 0.12, N = 15 13.94 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: super-resolution-10 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: super-resolution-10 - Device: CPU - Executor: Standard ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.00, N = 3 12.23 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Parallel OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Parallel ASUS NVIDIA GeForce RTX 3090 300 600 900 1200 1500 SE +/- 19.21, N = 3 1345.66 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.19 Model: ResNet101_DUC_HDC-12 - Device: CPU - Executor: Standard ASUS NVIDIA GeForce RTX 3090 200 400 600 800 1000 SE +/- 2.93, N = 3 1059.49 1. (CXX) g++ options: -O3 -march=native -ffunction-sections -fdata-sections -mtune=native -flto=auto -fno-fat-lto-objects -ldl -lrt
TensorFlow Lite Model: SqueezeNet OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2022-05-18 Model: SqueezeNet ASUS NVIDIA GeForce RTX 3090 400 800 1200 1600 2000 SE +/- 15.29, N = 15 1904.01
TensorFlow Lite Model: Inception V4 OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2022-05-18 Model: Inception V4 ASUS NVIDIA GeForce RTX 3090 6K 12K 18K 24K 30K SE +/- 395.13, N = 15 26961.0
TensorFlow Lite Model: NASNet Mobile OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2022-05-18 Model: NASNet Mobile ASUS NVIDIA GeForce RTX 3090 70K 140K 210K 280K 350K SE +/- 11554.76, N = 15 311542
TensorFlow Lite Model: Mobilenet Float OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2022-05-18 Model: Mobilenet Float ASUS NVIDIA GeForce RTX 3090 300 600 900 1200 1500 SE +/- 17.51, N = 15 1383.02
TensorFlow Lite Model: Mobilenet Quant OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2022-05-18 Model: Mobilenet Quant ASUS NVIDIA GeForce RTX 3090 500 1000 1500 2000 2500 SE +/- 20.94, N = 3 2317.67
TensorFlow Lite Model: Inception ResNet V2 OpenBenchmarking.org Microseconds, Fewer Is Better TensorFlow Lite 2022-05-18 Model: Inception ResNet V2 ASUS NVIDIA GeForce RTX 3090 30K 60K 90K 120K 150K SE +/- 4137.26, N = 15 128179
oneDNN Harness: IP Shapes 1D - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.6 Harness: IP Shapes 1D - Engine: CPU ASUS NVIDIA GeForce RTX 3090 0.6508 1.3016 1.9524 2.6032 3.254 SE +/- 0.00897, N = 3 2.89253 MIN: 2.72 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl
oneDNN Harness: IP Shapes 3D - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.6 Harness: IP Shapes 3D - Engine: CPU ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.00039, N = 3 9.32676 MIN: 9.1 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl
oneDNN Harness: Convolution Batch Shapes Auto - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.6 Harness: Convolution Batch Shapes Auto - Engine: CPU ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.00332, N = 3 8.24915 MIN: 7.76 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl
oneDNN Harness: Deconvolution Batch shapes_1d - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.6 Harness: Deconvolution Batch shapes_1d - Engine: CPU ASUS NVIDIA GeForce RTX 3090 1.0063 2.0126 3.0189 4.0252 5.0315 SE +/- 0.00491, N = 3 4.47259 MIN: 4.02 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl
oneDNN Harness: Deconvolution Batch shapes_3d - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.6 Harness: Deconvolution Batch shapes_3d - Engine: CPU ASUS NVIDIA GeForce RTX 3090 1.255 2.51 3.765 5.02 6.275 SE +/- 0.00815, N = 3 5.57785 MIN: 5.37 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl
oneDNN Harness: Recurrent Neural Network Training - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.6 Harness: Recurrent Neural Network Training - Engine: CPU ASUS NVIDIA GeForce RTX 3090 600 1200 1800 2400 3000 SE +/- 1.60, N = 3 2702.00 MIN: 2689.78 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl
oneDNN Harness: Recurrent Neural Network Inference - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.6 Harness: Recurrent Neural Network Inference - Engine: CPU ASUS NVIDIA GeForce RTX 3090 300 600 900 1200 1500 SE +/- 2.94, N = 3 1452.76 MIN: 1432.32 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -fcf-protection=full -pie -ldl
Mobile Neural Network Model: nasnet OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.9.b11b7037d Model: nasnet ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.004, N = 3 6.956 MIN: 6.61 / MAX: 53.04 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -pthread -ldl
Mobile Neural Network Model: mobilenetV3 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.9.b11b7037d Model: mobilenetV3 ASUS NVIDIA GeForce RTX 3090 0.2277 0.4554 0.6831 0.9108 1.1385 SE +/- 0.003, N = 3 1.012 MIN: 0.93 / MAX: 24.1 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -pthread -ldl
Mobile Neural Network Model: squeezenetv1.1 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.9.b11b7037d Model: squeezenetv1.1 ASUS NVIDIA GeForce RTX 3090 0.4131 0.8262 1.2393 1.6524 2.0655 SE +/- 0.006, N = 3 1.836 MIN: 1.71 / MAX: 31.7 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -pthread -ldl
Mobile Neural Network Model: resnet-v2-50 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.9.b11b7037d Model: resnet-v2-50 ASUS NVIDIA GeForce RTX 3090 4 8 12 16 20 SE +/- 0.08, N = 3 14.36 MIN: 13.6 / MAX: 59.49 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -pthread -ldl
Mobile Neural Network Model: SqueezeNetV1.0 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.9.b11b7037d Model: SqueezeNetV1.0 ASUS NVIDIA GeForce RTX 3090 0.6604 1.3208 1.9812 2.6416 3.302 SE +/- 0.049, N = 3 2.935 MIN: 2.7 / MAX: 34.14 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -pthread -ldl
Mobile Neural Network Model: MobileNetV2_224 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.9.b11b7037d Model: MobileNetV2_224 ASUS NVIDIA GeForce RTX 3090 0.4235 0.847 1.2705 1.694 2.1175 SE +/- 0.011, N = 3 1.882 MIN: 1.79 / MAX: 33.11 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -pthread -ldl
Mobile Neural Network Model: mobilenet-v1-1.0 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.9.b11b7037d Model: mobilenet-v1-1.0 ASUS NVIDIA GeForce RTX 3090 0.4689 0.9378 1.4067 1.8756 2.3445 SE +/- 0.015, N = 3 2.084 MIN: 1.99 / MAX: 26.25 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -pthread -ldl
Mobile Neural Network Model: inception-v3 OpenBenchmarking.org ms, Fewer Is Better Mobile Neural Network 2.9.b11b7037d Model: inception-v3 ASUS NVIDIA GeForce RTX 3090 5 10 15 20 25 SE +/- 0.04, N = 3 21.37 MIN: 20.43 / MAX: 62.25 1. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -pthread -ldl
NCNN Target: CPU - Model: mobilenet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: mobilenet ASUS NVIDIA GeForce RTX 3090 4 8 12 16 20 SE +/- 0.47, N = 9 16.46 MIN: 9.33 / MAX: 40.48 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU-v2-v2 - Model: mobilenet-v2 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU-v2-v2 - Model: mobilenet-v2 ASUS NVIDIA GeForce RTX 3090 1.2443 2.4886 3.7329 4.9772 6.2215 SE +/- 0.07, N = 9 5.53 MIN: 4.11 / MAX: 12.43 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU-v3-v3 - Model: mobilenet-v3 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU-v3-v3 - Model: mobilenet-v3 ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.34, N = 9 8.28 MIN: 4.71 / MAX: 21.8 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: shufflenet-v2 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: shufflenet-v2 ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.58, N = 9 11.05 MIN: 4.48 / MAX: 28.4 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: mnasnet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: mnasnet ASUS NVIDIA GeForce RTX 3090 1.2285 2.457 3.6855 4.914 6.1425 SE +/- 0.05, N = 9 5.46 MIN: 4.13 / MAX: 13.57 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: efficientnet-b0 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: efficientnet-b0 ASUS NVIDIA GeForce RTX 3090 5 10 15 20 25 SE +/- 0.78, N = 9 19.35 MIN: 7.5 / MAX: 39.35 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: blazeface OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: blazeface ASUS NVIDIA GeForce RTX 3090 1.3388 2.6776 4.0164 5.3552 6.694 SE +/- 0.11, N = 9 5.95 MIN: 2.83 / MAX: 16.59 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: googlenet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: googlenet ASUS NVIDIA GeForce RTX 3090 5 10 15 20 25 SE +/- 0.62, N = 9 19.65 MIN: 9.41 / MAX: 46.01 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: vgg16 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: vgg16 ASUS NVIDIA GeForce RTX 3090 7 14 21 28 35 SE +/- 0.01, N = 9 28.60 MIN: 27.69 / MAX: 31.26 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: resnet18 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: resnet18 ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.04, N = 9 7.50 MIN: 5.96 / MAX: 14.47 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: alexnet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: alexnet ASUS NVIDIA GeForce RTX 3090 1.2555 2.511 3.7665 5.022 6.2775 SE +/- 0.05, N = 9 5.58 MIN: 5.23 / MAX: 13.31 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: resnet50 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: resnet50 ASUS NVIDIA GeForce RTX 3090 5 10 15 20 25 SE +/- 0.34, N = 9 22.09 MIN: 13.37 / MAX: 48.04 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: yolov4-tiny OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: yolov4-tiny ASUS NVIDIA GeForce RTX 3090 5 10 15 20 25 SE +/- 0.31, N = 9 18.40 MIN: 14.15 / MAX: 42.77 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: squeezenet_ssd OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: squeezenet_ssd ASUS NVIDIA GeForce RTX 3090 4 8 12 16 20 SE +/- 0.29, N = 9 13.62 MIN: 8.06 / MAX: 30.15 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: regnety_400m OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: regnety_400m ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.75, N = 9 82.63 MIN: 22.37 / MAX: 120.26 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: vision_transformer OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: vision_transformer ASUS NVIDIA GeForce RTX 3090 16 32 48 64 80 SE +/- 0.06, N = 9 70.73 MIN: 67.43 / MAX: 80.71 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: FastestDet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: FastestDet ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.13, N = 9 6.47 MIN: 4.73 / MAX: 17.1 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: mobilenet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: mobilenet ASUS NVIDIA GeForce RTX 3090 4 8 12 16 20 SE +/- 0.37, N = 9 17.34 MIN: 9.39 / MAX: 40.25 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 ASUS NVIDIA GeForce RTX 3090 1.287 2.574 3.861 5.148 6.435 SE +/- 0.13, N = 9 5.72 MIN: 4.2 / MAX: 19.44 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.60, N = 9 7.93 MIN: 4.48 / MAX: 21.46 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: shufflenet-v2 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: shufflenet-v2 ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.50, N = 9 10.51 MIN: 4.55 / MAX: 29.19 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: mnasnet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: mnasnet ASUS NVIDIA GeForce RTX 3090 1.2218 2.4436 3.6654 4.8872 6.109 SE +/- 0.03, N = 9 5.43 MIN: 4.16 / MAX: 9.81 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: efficientnet-b0 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: efficientnet-b0 ASUS NVIDIA GeForce RTX 3090 5 10 15 20 25 SE +/- 0.83, N = 9 18.73 MIN: 7.31 / MAX: 37.64 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: blazeface OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: blazeface ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.11, N = 8 6.00 MIN: 2.81 / MAX: 17.23 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: googlenet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: googlenet ASUS NVIDIA GeForce RTX 3090 5 10 15 20 25 SE +/- 0.55, N = 9 19.43 MIN: 9.05 / MAX: 43.99 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: vgg16 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: vgg16 ASUS NVIDIA GeForce RTX 3090 7 14 21 28 35 SE +/- 0.01, N = 9 28.61 MIN: 27.79 / MAX: 31.77 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: resnet18 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: resnet18 ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.07, N = 9 7.29 MIN: 5.96 / MAX: 11.88 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: alexnet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: alexnet ASUS NVIDIA GeForce RTX 3090 1.2353 2.4706 3.7059 4.9412 6.1765 SE +/- 0.01, N = 8 5.49 MIN: 5.21 / MAX: 6.7 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: resnet50 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: resnet50 ASUS NVIDIA GeForce RTX 3090 5 10 15 20 25 SE +/- 0.46, N = 9 22.13 MIN: 13.15 / MAX: 49.98 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: yolov4-tiny OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: yolov4-tiny ASUS NVIDIA GeForce RTX 3090 4 8 12 16 20 SE +/- 0.19, N = 9 18.17 MIN: 14.35 / MAX: 48.07 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: squeezenet_ssd OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: squeezenet_ssd ASUS NVIDIA GeForce RTX 3090 4 8 12 16 20 SE +/- 0.48, N = 9 14.25 MIN: 7.96 / MAX: 30.38 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: regnety_400m OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: regnety_400m ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.69, N = 9 84.85 MIN: 22.37 / MAX: 121 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: vision_transformer OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: vision_transformer ASUS NVIDIA GeForce RTX 3090 16 32 48 64 80 SE +/- 0.03, N = 9 70.75 MIN: 67.65 / MAX: 81.35 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: Vulkan GPU - Model: FastestDet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: Vulkan GPU - Model: FastestDet ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.07, N = 9 6.56 MIN: 5.01 / MAX: 14.9 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
OpenVINO Model: Face Detection FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Face Detection FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 300 600 900 1200 1500 SE +/- 6.34, N = 3 1375.14 MIN: 1104.54 / MAX: 1761.35 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Person Detection FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Person Detection FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 30 60 90 120 150 SE +/- 0.46, N = 3 146.25 MIN: 120.28 / MAX: 197.97 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Person Detection FP32 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Person Detection FP32 - Device: CPU ASUS NVIDIA GeForce RTX 3090 30 60 90 120 150 SE +/- 0.71, N = 3 144.47 MIN: 72.51 / MAX: 211.86 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Vehicle Detection FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Vehicle Detection FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 5 10 15 20 25 SE +/- 0.10, N = 3 20.16 MIN: 7.21 / MAX: 40.97 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Face Detection FP16-INT8 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Face Detection FP16-INT8 - Device: CPU ASUS NVIDIA GeForce RTX 3090 80 160 240 320 400 SE +/- 0.21, N = 3 382.74 MIN: 223.52 / MAX: 881.7 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Face Detection Retail FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Face Detection Retail FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 1.1295 2.259 3.3885 4.518 5.6475 SE +/- 0.01, N = 3 5.02 MIN: 2.23 / MAX: 27.19 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Road Segmentation ADAS FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Road Segmentation ADAS FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 15 30 45 60 75 SE +/- 0.30, N = 3 69.09 MIN: 27.97 / MAX: 92.16 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Vehicle Detection FP16-INT8 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Vehicle Detection FP16-INT8 - Device: CPU ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.00, N = 3 8.35 MIN: 4.12 / MAX: 34.48 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Weld Porosity Detection FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Weld Porosity Detection FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 10 20 30 40 50 SE +/- 0.07, N = 3 44.26 MIN: 25.84 / MAX: 77.3 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Face Detection Retail FP16-INT8 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Face Detection Retail FP16-INT8 - Device: CPU ASUS NVIDIA GeForce RTX 3090 0.6683 1.3366 2.0049 2.6732 3.3415 SE +/- 0.00, N = 3 2.97 MIN: 1.4 / MAX: 22.5 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Road Segmentation ADAS FP16-INT8 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Road Segmentation ADAS FP16-INT8 - Device: CPU ASUS NVIDIA GeForce RTX 3090 5 10 15 20 25 SE +/- 0.05, N = 3 21.09 MIN: 9.8 / MAX: 54.63 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Machine Translation EN To DE FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Machine Translation EN To DE FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 30 60 90 120 150 SE +/- 0.40, N = 3 115.87 MIN: 60.69 / MAX: 196.8 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Weld Porosity Detection FP16-INT8 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Weld Porosity Detection FP16-INT8 - Device: CPU ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.00, N = 3 12.81 MIN: 7.26 / MAX: 52.97 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Person Vehicle Bike Detection FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Person Vehicle Bike Detection FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.01, N = 3 11.29 MIN: 5.5 / MAX: 26.39 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Noise Suppression Poconet-Like FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Noise Suppression Poconet-Like FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.01, N = 3 9.02 MIN: 6.21 / MAX: 25.89 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Handwritten English Recognition FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Handwritten English Recognition FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.10, N = 3 88.65 MIN: 56.64 / MAX: 150.06 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Person Re-Identification Retail FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Person Re-Identification Retail FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.01, N = 3 9.01 MIN: 4.38 / MAX: 37.43 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU ASUS NVIDIA GeForce RTX 3090 0.3735 0.747 1.1205 1.494 1.8675 SE +/- 0.00, N = 3 1.66 MIN: 0.78 / MAX: 17.39 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Handwritten English Recognition FP16-INT8 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Handwritten English Recognition FP16-INT8 - Device: CPU ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.19, N = 3 74.85 MIN: 56.4 / MAX: 150.26 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenVINO Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2024.0 Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU ASUS NVIDIA GeForce RTX 3090 0.1395 0.279 0.4185 0.558 0.6975 SE +/- 0.00, N = 3 0.62 MIN: 0.31 / MAX: 13.6 1. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl
OpenCV Test: DNN - Deep Neural Network OpenBenchmarking.org ms, Fewer Is Better OpenCV 4.7 Test: DNN - Deep Neural Network ASUS NVIDIA GeForce RTX 3090 5K 10K 15K 20K 25K SE +/- 635.50, N = 13 25552 1. (CXX) g++ options: -fsigned-char -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections -msse -msse2 -msse3 -fvisibility=hidden -O3 -ldl -lm -lpthread -lrt
DeepSpeech Acceleration: CPU OpenBenchmarking.org Seconds, Fewer Is Better DeepSpeech 0.6 Acceleration: CPU ASUS NVIDIA GeForce RTX 3090 12 24 36 48 60 SE +/- 0.08, N = 3 54.63
R Benchmark OpenBenchmarking.org Seconds, Fewer Is Better R Benchmark ASUS NVIDIA GeForce RTX 3090 0.0218 0.0436 0.0654 0.0872 0.109 SE +/- 0.0004, N = 3 0.0970
RNNoise Input: 26 Minute Long Talking Sample OpenBenchmarking.org Seconds, Fewer Is Better RNNoise 0.2 Input: 26 Minute Long Talking Sample ASUS NVIDIA GeForce RTX 3090 2 4 6 8 10 SE +/- 0.014, N = 3 7.245 1. (CC) gcc options: -O2 -pedantic -fvisibility=hidden
Scikit-Learn Benchmark: GLM OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: GLM ASUS NVIDIA GeForce RTX 3090 40 80 120 160 200 SE +/- 0.31, N = 3 183.48 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: SAGA OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: SAGA ASUS NVIDIA GeForce RTX 3090 110 220 330 440 550 SE +/- 0.32, N = 3 521.31 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Tree OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Tree ASUS NVIDIA GeForce RTX 3090 10 20 30 40 50 SE +/- 0.38, N = 15 42.77 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Lasso OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Lasso ASUS NVIDIA GeForce RTX 3090 60 120 180 240 300 SE +/- 1.16, N = 3 258.72 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Sparsify OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Sparsify ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.25, N = 3 85.98 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Plot Ward OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Ward ASUS NVIDIA GeForce RTX 3090 10 20 30 40 50 SE +/- 0.60, N = 3 43.62 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: MNIST Dataset OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: MNIST Dataset ASUS NVIDIA GeForce RTX 3090 12 24 36 48 60 SE +/- 0.03, N = 3 53.54 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Plot Neighbors OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Neighbors ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.18, N = 3 96.76 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: SGD Regression OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: SGD Regression ASUS NVIDIA GeForce RTX 3090 14 28 42 56 70 SE +/- 0.12, N = 3 63.79 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: SGDOneClassSVM OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: SGDOneClassSVM ASUS NVIDIA GeForce RTX 3090 40 80 120 160 200 SE +/- 1.90, N = 6 196.47 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Isolation Forest OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Isolation Forest ASUS NVIDIA GeForce RTX 3090 30 60 90 120 150 SE +/- 0.04, N = 3 147.52 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Text Vectorizers OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Text Vectorizers ASUS NVIDIA GeForce RTX 3090 9 18 27 36 45 SE +/- 0.03, N = 3 38.91 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Plot Hierarchical OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Hierarchical ASUS NVIDIA GeForce RTX 3090 30 60 90 120 150 SE +/- 0.14, N = 3 153.06 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Plot OMP vs. LARS OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot OMP vs. LARS ASUS NVIDIA GeForce RTX 3090 10 20 30 40 50 SE +/- 0.29, N = 3 45.62 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Feature Expansions OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Feature Expansions ASUS NVIDIA GeForce RTX 3090 30 60 90 120 150 SE +/- 0.09, N = 3 120.33 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: LocalOutlierFactor OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: LocalOutlierFactor ASUS NVIDIA GeForce RTX 3090 8 16 24 32 40 SE +/- 0.32, N = 15 35.65 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: TSNE MNIST Dataset OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: TSNE MNIST Dataset ASUS NVIDIA GeForce RTX 3090 40 80 120 160 200 SE +/- 0.44, N = 3 188.33 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Isotonic / Logistic OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Isotonic / Logistic ASUS NVIDIA GeForce RTX 3090 300 600 900 1200 1500 SE +/- 0.80, N = 3 1447.69 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Plot Incremental PCA OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Incremental PCA ASUS NVIDIA GeForce RTX 3090 5 10 15 20 25 SE +/- 0.18, N = 15 21.31 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Hist Gradient Boosting OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Hist Gradient Boosting ASUS NVIDIA GeForce RTX 3090 200 400 600 800 1000 SE +/- 3.91, N = 3 1120.04 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Plot Parallel Pairwise OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Parallel Pairwise ASUS NVIDIA GeForce RTX 3090 70 140 210 280 350 SE +/- 1.13, N = 3 332.19 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Sample Without Replacement OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Sample Without Replacement ASUS NVIDIA GeForce RTX 3090 20 40 60 80 100 SE +/- 0.15, N = 3 84.27 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Covertype Dataset Benchmark OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Covertype Dataset Benchmark ASUS NVIDIA GeForce RTX 3090 70 140 210 280 350 SE +/- 0.13, N = 3 317.46 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Hist Gradient Boosting Adult OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Hist Gradient Boosting Adult ASUS NVIDIA GeForce RTX 3090 300 600 900 1200 1500 SE +/- 4.93, N = 3 1173.89 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Isotonic / Perturbed Logarithm OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Isotonic / Perturbed Logarithm ASUS NVIDIA GeForce RTX 3090 300 600 900 1200 1500 SE +/- 0.23, N = 3 1469.79 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Hist Gradient Boosting Threading OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Hist Gradient Boosting Threading ASUS NVIDIA GeForce RTX 3090 30 60 90 120 150 SE +/- 1.02, N = 3 140.75 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Hist Gradient Boosting Higgs Boson OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Hist Gradient Boosting Higgs Boson ASUS NVIDIA GeForce RTX 3090 40 80 120 160 200 SE +/- 2.11, N = 5 190.58 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: 20 Newsgroups / Logistic Regression OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: 20 Newsgroups / Logistic Regression ASUS NVIDIA GeForce RTX 3090 3 6 9 12 15 SE +/- 0.072, N = 3 9.614 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Plot Polynomial Kernel Approximation OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Plot Polynomial Kernel Approximation ASUS NVIDIA GeForce RTX 3090 30 60 90 120 150 SE +/- 0.20, N = 3 133.12 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Hist Gradient Boosting Categorical Only OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Hist Gradient Boosting Categorical Only ASUS NVIDIA GeForce RTX 3090 40 80 120 160 200 SE +/- 0.28, N = 3 195.30 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Kernel PCA Solvers / Time vs. N Samples OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Kernel PCA Solvers / Time vs. N Samples ASUS NVIDIA GeForce RTX 3090 15 30 45 60 75 SE +/- 0.35, N = 3 69.63 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Kernel PCA Solvers / Time vs. N Components OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Kernel PCA Solvers / Time vs. N Components ASUS NVIDIA GeForce RTX 3090 6 12 18 24 30 SE +/- 0.23, N = 3 25.59 1. (F9X) gfortran options: -O0
Scikit-Learn Benchmark: Sparse Random Projections / 100 Iterations OpenBenchmarking.org Seconds, Fewer Is Better Scikit-Learn 1.2.2 Benchmark: Sparse Random Projections / 100 Iterations ASUS NVIDIA GeForce RTX 3090 130 260 390 520 650 SE +/- 1.77, N = 3 587.84 1. (F9X) gfortran options: -O0
Whisper.cpp Model: ggml-base.en - Input: 2016 State of the Union OpenBenchmarking.org Seconds, Fewer Is Better Whisper.cpp 1.6.2 Model: ggml-base.en - Input: 2016 State of the Union ASUS NVIDIA GeForce RTX 3090 50 100 150 200 250 SE +/- 0.74, N = 3 209.02 1. (CXX) g++ options: -O3 -std=c++11 -fPIC -pthread -msse3 -mssse3 -mavx -mf16c -mfma -mavx2
Whisper.cpp Model: ggml-small.en - Input: 2016 State of the Union OpenBenchmarking.org Seconds, Fewer Is Better Whisper.cpp 1.6.2 Model: ggml-small.en - Input: 2016 State of the Union ASUS NVIDIA GeForce RTX 3090 140 280 420 560 700 SE +/- 0.42, N = 3 638.01 1. (CXX) g++ options: -O3 -std=c++11 -fPIC -pthread -msse3 -mssse3 -mavx -mf16c -mfma -mavx2
Whisper.cpp Model: ggml-medium.en - Input: 2016 State of the Union OpenBenchmarking.org Seconds, Fewer Is Better Whisper.cpp 1.6.2 Model: ggml-medium.en - Input: 2016 State of the Union ASUS NVIDIA GeForce RTX 3090 400 800 1200 1600 2000 SE +/- 2.12, N = 3 2000.10 1. (CXX) g++ options: -O3 -std=c++11 -fPIC -pthread -msse3 -mssse3 -mavx -mf16c -mfma -mavx2
XNNPACK Model: FP32MobileNetV1 OpenBenchmarking.org us, Fewer Is Better XNNPACK b7b048 Model: FP32MobileNetV1 ASUS NVIDIA GeForce RTX 3090 300 600 900 1200 1500 SE +/- 27.87, N = 12 1394 1. (CXX) g++ options: -O3 -lrt -lm
XNNPACK Model: FP32MobileNetV2 OpenBenchmarking.org us, Fewer Is Better XNNPACK b7b048 Model: FP32MobileNetV2 ASUS NVIDIA GeForce RTX 3090 200 400 600 800 1000 SE +/- 17.99, N = 12 1125 1. (CXX) g++ options: -O3 -lrt -lm
XNNPACK Model: FP32MobileNetV3Large OpenBenchmarking.org us, Fewer Is Better XNNPACK b7b048 Model: FP32MobileNetV3Large ASUS NVIDIA GeForce RTX 3090 300 600 900 1200 1500 SE +/- 66.95, N = 12 1585 1. (CXX) g++ options: -O3 -lrt -lm
XNNPACK Model: FP32MobileNetV3Small OpenBenchmarking.org us, Fewer Is Better XNNPACK b7b048 Model: FP32MobileNetV3Small ASUS NVIDIA GeForce RTX 3090 200 400 600 800 1000 SE +/- 29.73, N = 12 820 1. (CXX) g++ options: -O3 -lrt -lm
XNNPACK Model: FP16MobileNetV1 OpenBenchmarking.org us, Fewer Is Better XNNPACK b7b048 Model: FP16MobileNetV1 ASUS NVIDIA GeForce RTX 3090 500 1000 1500 2000 2500 SE +/- 74.47, N = 12 2197 1. (CXX) g++ options: -O3 -lrt -lm
XNNPACK Model: FP16MobileNetV2 OpenBenchmarking.org us, Fewer Is Better XNNPACK b7b048 Model: FP16MobileNetV2 ASUS NVIDIA GeForce RTX 3090 400 800 1200 1600 2000 SE +/- 50.43, N = 12 1646 1. (CXX) g++ options: -O3 -lrt -lm
XNNPACK Model: FP16MobileNetV3Large OpenBenchmarking.org us, Fewer Is Better XNNPACK b7b048 Model: FP16MobileNetV3Large ASUS NVIDIA GeForce RTX 3090 400 800 1200 1600 2000 SE +/- 29.31, N = 12 1834 1. (CXX) g++ options: -O3 -lrt -lm
XNNPACK Model: FP16MobileNetV3Small OpenBenchmarking.org us, Fewer Is Better XNNPACK b7b048 Model: FP16MobileNetV3Small ASUS NVIDIA GeForce RTX 3090 200 400 600 800 1000 SE +/- 16.32, N = 12 928 1. (CXX) g++ options: -O3 -lrt -lm
XNNPACK Model: QS8MobileNetV2 OpenBenchmarking.org us, Fewer Is Better XNNPACK b7b048 Model: QS8MobileNetV2 ASUS NVIDIA GeForce RTX 3090 200 400 600 800 1000 SE +/- 35.09, N = 12 904 1. (CXX) g++ options: -O3 -lrt -lm
Phoronix Test Suite v10.8.5