Jetson Nano Developer Kit

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  Test
  Duration
Jetson TX1 Max-P
March 17 2019
  1 Hour, 20 Minutes
Jetson TX2 Max-Q
March 16 2019
  7 Hours, 23 Minutes
Jetson TX2 Max-P
March 15 2019
  6 Hours, 25 Minutes
Jetson AGX Xavier
March 15 2019
  4 Hours, 1 Minute
Jetson Nano
March 17 2019
  7 Hours, 18 Minutes
Raspberry Pi 3 Model B+
March 16 2019
  4 Hours, 32 Minutes
ASUS TinkerBoard
March 16 2019
  7 Hours, 20 Minutes
ODROID-XU4
March 17 2019
  4 Hours, 21 Minutes
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  5 Hours, 20 Minutes

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Jetson Nano Developer KitProcessorMotherboardMemoryDiskGraphicsMonitorNetworkOSKernelDesktopDisplay ServerDisplay DriverOpenGLVulkanCompilerFile-SystemScreen ResolutionJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QRaspberry Pi 3 Model B+ASUS TinkerBoardJetson TX1 Max-PODROID-XU4Jetson NanoARMv8 rev 0 @ 2.27GHz (8 Cores)jetson-xavier16384MB31GB HBG4a2NVIDIA Tegra XavierVE228Ubuntu 18.044.9.108-tegra (aarch64)Unity 7.5.0X Server 1.19.6NVIDIA 31.0.24.6.01.1.76GCC 7.3.0 + CUDA 10.0ext41920x1080ARMv8 rev 3 @ 2.04GHz (4 Cores / 6 Threads)quill8192MB31GB 032G34NVIDIA TEGRAUbuntu 16.044.4.38-tegra (aarch64)Unity 7.4.0X Server 1.18.4NVIDIA 28.2.14.5.0GCC 5.4.0 20160609 + CUDA 9.0ARMv8 rev 3 @ 1.27GHz (4 Cores / 6 Threads)ARMv7 rev 4 @ 1.40GHz (4 Cores)BCM2835 Raspberry Pi 3 Model B Plus Rev 1.3926MB32GB GB2MWBCM2708Raspbian 9.64.19.23-v7+ (armv7l)LXDEX Server 1.19.2GCC 6.3.0 20170516656x416ARMv7 rev 1 @ 1.80GHz (4 Cores)Rockchip (Device Tree)2048MB32GB GB1QTDebian 9.04.4.16-00006-g4431f98-dirty (armv7l)X Server 1.18.41024x768ARMv8 rev 1 @ 1.73GHz (4 Cores)jetson_tx14096MB16GB 016G32NVIDIA Tegra X1VE228Ubuntu 16.044.4.38-tegra (aarch64)Unity 7.4.5NVIDIA 28.1.04.5.01.0.8GCC 5.4.0 201606091920x1080ARMv7 rev 3 @ 1.50GHz (8 Cores)ODROID-XU4 Hardkernel Odroid XU42048MB16GB AJTD4Rllvmpipe 2GBUbuntu 18.044.14.37-135 (armv7l)X Server 1.19.63.3 Mesa 18.0.0-rc5 (LLVM 6.0 128 bits)GCC 7.3.0ARMv8 rev 1 @ 1.43GHz (4 Cores)jetson-nano4096MB32GB GB1QTNVIDIA TEGRARealtek RTL8111/8168/84114.9.140-tegra (aarch64)Unity 7.5.0NVIDIA 1.0.01.1.85GCC 7.3.0 + CUDA 10.0OpenBenchmarking.orgCompiler Details- Jetson AGX Xavier: --build=aarch64-linux-gnu --disable-libquadmath --disable-libquadmath-support --disable-werror --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-fix-cortex-a53-843419 --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++ --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-nls --enable-plugin --enable-shared --enable-threads=posix --host=aarch64-linux-gnu --program-prefix=aarch64-linux-gnu- --target=aarch64-linux-gnu --with-default-libstdcxx-abi=new --with-gcc-major-version-only -v - Jetson TX2 Max-P: --build=aarch64-linux-gnu --disable-browser-plugin --disable-libquadmath --disable-werror --enable-checking=release --enable-clocale=gnu --enable-fix-cortex-a53-843419 --enable-gnu-unique-object --enable-gtk-cairo --enable-java-awt=gtk --enable-java-home --enable-languages=c,ada,c++,java,go,d,fortran,objc,obj-c++ --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-nls --enable-plugin --enable-shared --enable-threads=posix --host=aarch64-linux-gnu --target=aarch64-linux-gnu --with-arch-directory=aarch64 --with-default-libstdcxx-abi=new -v - Jetson TX2 Max-Q: --build=aarch64-linux-gnu --disable-browser-plugin --disable-libquadmath --disable-werror --enable-checking=release --enable-clocale=gnu --enable-fix-cortex-a53-843419 --enable-gnu-unique-object --enable-gtk-cairo --enable-java-awt=gtk --enable-java-home --enable-languages=c,ada,c++,java,go,d,fortran,objc,obj-c++ --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-nls --enable-plugin --enable-shared --enable-threads=posix --host=aarch64-linux-gnu --target=aarch64-linux-gnu --with-arch-directory=aarch64 --with-default-libstdcxx-abi=new -v - Raspberry Pi 3 Model B+: --build=arm-linux-gnueabihf --disable-browser-plugin --disable-libitm --disable-libquadmath --disable-sjlj-exceptions --enable-checking=release --enable-clocale=gnu --enable-gnu-unique-object --enable-gtk-cairo --enable-java-awt=gtk --enable-java-home --enable-languages=c,ada,c++,java,go,d,fortran,objc,obj-c++ --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-nls --enable-objc-gc=auto --enable-plugin --enable-shared --enable-threads=posix --host=arm-linux-gnueabihf --program-prefix=arm-linux-gnueabihf- --target=arm-linux-gnueabihf --with-arch-directory=arm --with-arch=armv6 --with-default-libstdcxx-abi=new --with-float=hard --with-fpu=vfp --with-target-system-zlib -v - ASUS TinkerBoard: --build=arm-linux-gnueabihf --disable-browser-plugin --disable-libitm --disable-libquadmath --disable-sjlj-exceptions --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-gtk-cairo --enable-java-awt=gtk --enable-java-home --enable-languages=c,ada,c++,java,go,d,fortran,objc,obj-c++ --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-nls --enable-objc-gc=auto --enable-plugin --enable-shared --enable-threads=posix --host=arm-linux-gnueabihf --program-prefix=arm-linux-gnueabihf- --target=arm-linux-gnueabihf --with-arch-directory=arm --with-arch=armv7-a --with-default-libstdcxx-abi=new --with-float=hard --with-fpu=vfpv3-d16 --with-mode=thumb --with-target-system-zlib -v - Jetson TX1 Max-P: --build=aarch64-linux-gnu --disable-browser-plugin --disable-libquadmath --disable-werror --enable-checking=release --enable-clocale=gnu --enable-fix-cortex-a53-843419 --enable-gnu-unique-object --enable-gtk-cairo --enable-java-awt=gtk --enable-java-home --enable-languages=c,ada,c++,java,go,d,fortran,objc,obj-c++ --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-nls --enable-plugin --enable-shared --enable-threads=posix --host=aarch64-linux-gnu --target=aarch64-linux-gnu --with-arch-directory=aarch64 --with-default-libstdcxx-abi=new -v - ODROID-XU4: --build=arm-linux-gnueabihf --disable-libitm --disable-libquadmath --disable-libquadmath-support --disable-sjlj-exceptions --disable-werror --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++ --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-multilib --enable-nls --enable-objc-gc=auto --enable-plugin --enable-shared --enable-threads=posix --host=arm-linux-gnueabihf --program-prefix=arm-linux-gnueabihf- --target=arm-linux-gnueabihf --with-arch=armv7-a --with-default-libstdcxx-abi=new --with-float=hard --with-fpu=vfpv3-d16 --with-gcc-major-version-only --with-mode=thumb --with-target-system-zlib -v - Jetson Nano: --build=aarch64-linux-gnu --disable-libquadmath --disable-libquadmath-support --disable-werror --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-fix-cortex-a53-843419 --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++ --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-nls --enable-plugin --enable-shared --enable-threads=posix --host=aarch64-linux-gnu --program-prefix=aarch64-linux-gnu- --target=aarch64-linux-gnu --with-default-libstdcxx-abi=new --with-gcc-major-version-only -v Processor Details- Jetson AGX Xavier: Scaling Governor: tegra_cpufreq schedutil- Jetson TX2 Max-P: Scaling Governor: tegra_cpufreq schedutil- Jetson TX2 Max-Q: Scaling Governor: tegra_cpufreq schedutil- Raspberry Pi 3 Model B+: Scaling Governor: BCM2835 Freq ondemand- ASUS TinkerBoard: Scaling Governor: cpufreq-dt interactive- Jetson TX1 Max-P: Scaling Governor: tegra-cpufreq interactive- ODROID-XU4: Scaling Governor: cpufreq-dt ondemand- Jetson Nano: Scaling Governor: tegra-cpufreq schedutilPython Details- Jetson AGX Xavier: Python 2.7.15rc1 + Python 3.6.7- Jetson TX2 Max-P: Python 2.7.12 + Python 3.5.2- Jetson TX2 Max-Q: Python 2.7.12 + Python 3.5.2- Raspberry Pi 3 Model B+: Python 2.7.13 + Python 3.5.3- ASUS TinkerBoard: Python 2.7.13 + Python 3.5.3- Jetson TX1 Max-P: Python 2.7.12 + Python 3.5.2- ODROID-XU4: Python 2.7.15rc1 + Python 3.6.7- Jetson Nano: Python 2.7.15rc1 + Python 3.6.7Kernel Details- ODROID-XU4: usbhid.quirks=0x0eef:0x0005:0x0004Graphics Details- ODROID-XU4: EXA

Jetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QRaspberry Pi 3 Model B+ASUS TinkerBoardJetson TX1 Max-PODROID-XU4Jetson NanoLogarithmic Result OverviewPhoronix Test Suite7-Zip CompressionTTSIOD 3D RendererPyBenchFLAC Audio EncodingRust Prime BenchmarkC-Ray

Jetson Nano Developer Kitencode-flac: WAV To FLACtesseract-ocr: Time To OCR 7 Imagestensorrt-inference: GoogleNet - INT8 - 4 - Disabledlczero: BLAStensorrt-inference: AlexNet - FP16 - 32 - Disabledlczero: CUDA + cuDNNtensorrt-inference: VGG19 - FP16 - 4 - Disabledtensorrt-inference: ResNet152 - FP16 - 4 - Disabledtensorrt-inference: VGG16 - FP16 - 4 - Disabledtensorrt-inference: VGG19 - FP16 - 32 - Disabledtensorrt-inference: VGG16 - FP16 - 32 - Disabledtensorrt-inference: GoogleNet - FP16 - 4 - Disabledtensorrt-inference: AlexNet - FP16 - 4 - Disabledtensorrt-inference: ResNet50 - FP16 - 4 - Disabledlczero: CUDA + cuDNN FP16rust-prime: Prime Number Test To 200,000,000compress-7zip: Compress Speed Testcompress-zstd: Compressing ubuntu-16.04.3-server-i386.img, Compression Level 19c-ray: Total Time - 4K, 16 Rays Per Pixelttsiod-renderer: Phong Rendering With Soft-Shadow Mappingopencv-bench: pybench: Total For Average Test Timestensorrt-inference: ResNet50 - INT8 - 4 - Disabledtensorrt-inference: ResNet152 - INT8 - 32 - Disabledtensorrt-inference: AlexNet - INT8 - 32 - Disabledtensorrt-inference: ResNet152 - FP16 - 32 - Disabledtensorrt-inference: AlexNet - INT8 - 4 - Disabledtensorrt-inference: GoogleNet - INT8 - 32 - Disabledtensorrt-inference: VGG19 - INT8 - 32 - Disabledtensorrt-inference: GoogleNet - FP16 - 32 - Disabledtensorrt-inference: VGG16 - INT8 - 32 - Disabledtensorrt-inference: ResNet50 - INT8 - 32 - Disabledtensorrt-inference: VGG19 - INT8 - 4 - Disabledtensorrt-inference: ResNet50 - FP16 - 32 - Disabledtensorrt-inference: VGG16 - INT8 - 4 - Disabledtensorrt-inference: ResNet152 - INT8 - 4 - Disabledcuda-mini-nbody: Originalglmark2: 1920 x 1080Jetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QRaspberry Pi 3 Model B+ASUS TinkerBoardJetson TX1 Max-PODROID-XU4Jetson Nano54.4771.94114647.622038953172.50224.19208.76203.96247.957961200547.502515.0132.371921280.063551331283007902.78493.223143259.8211431693394.661006475.081215.08265.81636303.78372.7347.13287665.0711346226.5635.1132.6429.8336.8719726492.28104.965593144.9758549.26296540849.9722.0730141.9118413015.9223319.9159.6914.3211117.5618.298.24104.2888.8837421.0427.3425.9923.9429.8315621672.01170.253294253.8086928.85493873539.1517.3623732.6714810412.5917915.7947.1511.4586.0814.2414.506.77339.531097.692013342.23203017.662.7420913279.051821.052836496.62171821.221150279.20128.454508145.8075345.09633997.03180.66574.11412082741.96520.705009104.77132.6747.8215.3720114011.5915.7614.3583.3711841.04150.194049129.8792140.94271.04708420.9612817.3884.1055.6698.9325.0846.517.764.07646OpenBenchmarking.org

FLAC Audio Encoding

This test times how long it takes to encode a sample WAV file to FLAC format five times. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterFLAC Audio Encoding 1.3.2WAV To FLACJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QRaspberry Pi 3 Model B+ASUS TinkerBoardJetson TX1 Max-PODROID-XU4Jetson Nano70140210280350SE +/- 0.61, N = 5SE +/- 0.15, N = 5SE +/- 0.18, N = 5SE +/- 0.98, N = 5SE +/- 2.51, N = 5SE +/- 0.74, N = 5SE +/- 0.31, N = 5SE +/- 0.83, N = 554.4765.07104.28339.53279.0579.2097.03104.771. (CXX) g++ options: -O2 -fvisibility=hidden -logg -lm

Tesseract OCR

Tesseract-OCR is the open-source optical character recognition (OCR) engine for the conversion of text within images to raw text output. This test profile relies upon a system-supplied Tesseract installation. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTesseract OCR 4.0.0-beta.1Time To OCR 7 ImagesJetson AGX XavierODROID-XU4Jetson Nano4080120160200SE +/- 0.89, N = 3SE +/- 1.38, N = 3SE +/- 1.50, N = 371.94180.66132.67

NVIDIA TensorRT Inference

This test profile uses any existing system installation of NVIDIA TensorRT for carrying out inference benchmarks with various neural networks. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: INT8 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano2004006008001000SE +/- 4.31, N = 3SE +/- 1.65, N = 3SE +/- 1.32, N = 3SE +/- 0.60, N = 31146.00113.0088.8847.82

LeelaChessZero

LeelaChessZero (lc0 / lczero) is a chess engine automated vian neural networks. This test profile can be used for OpenCL, CUDA + cuDNN, and BLAS (CPU-based) benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgNodes Per Second, More Is BetterLeelaChessZero 0.20.1Backend: BLASJetson AGX XavierJetson Nano1122334455SE +/- 0.62, N = 7SE +/- 0.03, N = 347.6215.371. (CXX) g++ options: -lpthread -lz

NVIDIA TensorRT Inference

This test profile uses any existing system installation of NVIDIA TensorRT for carrying out inference benchmarks with various neural networks. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: FP16 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano400800120016002000SE +/- 2.07, N = 3SE +/- 7.68, N = 12SE +/- 2.82, N = 3SE +/- 1.59, N = 32038462374201

LeelaChessZero

LeelaChessZero (lc0 / lczero) is a chess engine automated vian neural networks. This test profile can be used for OpenCL, CUDA + cuDNN, and BLAS (CPU-based) benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgNodes Per Second, More Is BetterLeelaChessZero 0.20.1Backend: CUDA + cuDNNJetson AGX XavierJetson Nano2004006008001000SE +/- 6.14, N = 3SE +/- 0.26, N = 39531401. (CXX) g++ options: -lpthread -lz

NVIDIA TensorRT Inference

This test profile uses any existing system installation of NVIDIA TensorRT for carrying out inference benchmarks with various neural networks. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: FP16 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano4080120160200SE +/- 0.50, N = 3SE +/- 0.38, N = 3SE +/- 0.34, N = 3SE +/- 0.05, N = 2172.5026.5621.0411.59

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: FP16 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano50100150200250SE +/- 0.22, N = 3SE +/- 0.36, N = 3SE +/- 0.34, N = 3SE +/- 0.04, N = 3224.1935.1127.3415.76

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: FP16 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano50100150200250SE +/- 0.10, N = 3SE +/- 0.50, N = 4SE +/- 0.13, N = 3SE +/- 0.02, N = 2208.7632.6425.9914.35

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: FP16 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-Q4080120160200SE +/- 0.04, N = 3SE +/- 0.05, N = 3SE +/- 0.07, N = 3203.9629.8323.94

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: FP16 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-Q50100150200250SE +/- 0.12, N = 3SE +/- 0.31, N = 3SE +/- 0.18, N = 3247.9536.8729.83

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: FP16 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano2004006008001000SE +/- 2.48, N = 3SE +/- 2.27, N = 3SE +/- 1.90, N = 12SE +/- 0.70, N = 3796.00197.00156.0083.37

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: FP16 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano30060090012001500SE +/- 1.82, N = 3SE +/- 7.77, N = 12SE +/- 3.03, N = 6SE +/- 2.12, N = 121200264216118

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: FP16 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano120240360480600SE +/- 0.03, N = 3SE +/- 1.32, N = 12SE +/- 1.10, N = 12SE +/- 0.25, N = 3547.5092.2872.0141.04

LeelaChessZero

LeelaChessZero (lc0 / lczero) is a chess engine automated vian neural networks. This test profile can be used for OpenCL, CUDA + cuDNN, and BLAS (CPU-based) benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgNodes Per Second, More Is BetterLeelaChessZero 0.20.1Backend: CUDA + cuDNN FP16Jetson AGX Xavier5001000150020002500SE +/- 7.60, N = 32515.011. (CXX) g++ options: -lpthread -lz

Rust Prime Benchmark

Based on petehunt/rust-benchmark, this is a prime number benchmark that is multi-threaded and written in Rustlang. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterRust Prime BenchmarkPrime Number Test To 200,000,000Jetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QRaspberry Pi 3 Model B+ASUS TinkerBoardJetson TX1 Max-PODROID-XU4Jetson Nano400800120016002000SE +/- 0.00, N = 3SE +/- 0.04, N = 3SE +/- 0.09, N = 3SE +/- 1.55, N = 3SE +/- 187.90, N = 6SE +/- 0.77, N = 3SE +/- 0.37, N = 3SE +/- 0.22, N = 332.37104.96170.251097.691821.05128.45574.11150.19-ldl -lrt -lpthread -lgcc_s -lc -lm -lutil1. (CC) gcc options: -pie -nodefaultlibs

7-Zip Compression

This is a test of 7-Zip using p7zip with its integrated benchmark feature or upstream 7-Zip for the Windows x64 build. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMIPS, More Is Better7-Zip Compression 16.02Compress Speed TestJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QRaspberry Pi 3 Model B+ASUS TinkerBoardJetson TX1 Max-PODROID-XU4Jetson Nano4K8K12K16K20KSE +/- 274.18, N = 12SE +/- 20.85, N = 3SE +/- 13.05, N = 3SE +/- 23.74, N = 11SE +/- 34.93, N = 3SE +/- 13.43, N = 3SE +/- 89.16, N = 12SE +/- 18.00, N = 31921255933294201328364508412040491. (CXX) g++ options: -pipe -lpthread

Zstd Compression

This test measures the time needed to compress a sample file (an Ubuntu file-system image) using Zstd compression. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterZstd Compression 1.3.4Compressing ubuntu-16.04.3-server-i386.img, Compression Level 19Jetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QRaspberry Pi 3 Model B+ASUS TinkerBoardJetson TX1 Max-PJetson Nano110220330440550SE +/- 0.91, N = 3SE +/- 0.29, N = 3SE +/- 1.02, N = 3SE +/- 1.03, N = 3SE +/- 2.16, N = 3SE +/- 0.42, N = 3SE +/- 0.23, N = 380.06144.97253.80342.23496.62145.80129.871. (CC) gcc options: -O3 -pthread -lz -llzma

C-Ray

This is a test of C-Ray, a simple raytracer designed to test the floating-point CPU performance. This test is multi-threaded (16 threads per core), will shoot 8 rays per pixel for anti-aliasing, and will generate a 1600 x 1200 image. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterC-Ray 1.1Total Time - 4K, 16 Rays Per PixelJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QRaspberry Pi 3 Model B+ASUS TinkerBoardJetson TX1 Max-PODROID-XU4Jetson Nano400800120016002000SE +/- 7.17, N = 9SE +/- 49.09, N = 9SE +/- 1.44, N = 3SE +/- 2.46, N = 3SE +/- 22.09, N = 3SE +/- 10.23, N = 3SE +/- 29.65, N = 9SE +/- 0.35, N = 3355585869203017187538279211. (CC) gcc options: -lm -lpthread -O3

TTSIOD 3D Renderer

A portable GPL 3D software renderer that supports OpenMP and Intel Threading Building Blocks with many different rendering modes. This version does not use OpenGL but is entirely CPU/software based. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterTTSIOD 3D Renderer 2.3bPhong Rendering With Soft-Shadow MappingJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QRaspberry Pi 3 Model B+ASUS TinkerBoardJetson TX1 Max-PODROID-XU4Jetson Nano306090120150SE +/- 1.63, N = 12SE +/- 0.15, N = 3SE +/- 0.46, N = 4SE +/- 0.16, N = 3SE +/- 0.27, N = 9SE +/- 0.04, N = 3SE +/- 0.97, N = 9SE +/- 0.11, N = 3133.0049.2628.8517.6621.2245.0941.9640.941. (CXX) g++ options: -O3 -fomit-frame-pointer -ffast-math -mtune=native -flto -lSDL -fopenmp -fwhole-program -lstdc++

OpenCV Benchmark

Stress benchmark tests to measure time consumed by the OpenCV libraries installed Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenCV Benchmark 3.3.0Jetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QRaspberry Pi 3 Model B+ODROID-XU4Jetson Nano110220330440550SE +/- 1.57, N = 3SE +/- 0.27, N = 3SE +/- 5.74, N = 3SE +/- 5.31, N = 3SE +/- 4.66, N = 9128.00296.00493.002.74520.70271.041. (CXX) g++ options: -std=c++11 -rdynamic

PyBench

This test profile reports the total time of the different average timed test results from PyBench. PyBench reports average test times for different functions such as BuiltinFunctionCalls and NestedForLoops, with this total result providing a rough estimate as to Python's average performance on a given system. This test profile runs PyBench each time for 20 rounds. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyBench 2018-02-16Total For Average Test TimesJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QRaspberry Pi 3 Model B+ASUS TinkerBoardJetson TX1 Max-PODROID-XU4Jetson Nano4K8K12K16K20KSE +/- 4.67, N = 3SE +/- 33.86, N = 3SE +/- 42.52, N = 3SE +/- 43.80, N = 3SE +/- 854.75, N = 9SE +/- 18.55, N = 3SE +/- 30.99, N = 3SE +/- 37.23, N = 33007540887352091311502633950097084

NVIDIA TensorRT Inference

This test profile uses any existing system installation of NVIDIA TensorRT for carrying out inference benchmarks with various neural networks. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: INT8 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano2004006008001000SE +/- 1.86, N = 3SE +/- 0.79, N = 4SE +/- 0.64, N = 3SE +/- 0.36, N = 3902.7849.9739.1520.96

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: INT8 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-Q110220330440550SE +/- 0.81, N = 3SE +/- 0.03, N = 3SE +/- 0.00, N = 3493.2222.0717.36

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: INT8 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano7001400210028003500SE +/- 1.06, N = 3SE +/- 0.52, N = 3SE +/- 1.39, N = 3SE +/- 0.06, N = 33143301237128

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: FP16 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano60120180240300SE +/- 0.26, N = 3SE +/- 0.07, N = 3SE +/- 0.10, N = 3SE +/- 0.01, N = 3259.8241.9132.6717.38

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: AlexNet - Precision: INT8 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano2004006008001000SE +/- 2.59, N = 3SE +/- 2.79, N = 5SE +/- 0.91, N = 3SE +/- 0.72, N = 31143.00184.00148.0084.10

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: INT8 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano400800120016002000SE +/- 8.72, N = 3SE +/- 0.74, N = 3SE +/- 0.07, N = 3SE +/- 0.18, N = 31693.00130.00104.0055.66

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: INT8 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-Q90180270360450SE +/- 0.23, N = 3SE +/- 0.06, N = 3SE +/- 0.03, N = 3394.6615.9212.59

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: GoogleNet - Precision: FP16 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano2004006008001000SE +/- 0.21, N = 3SE +/- 4.50, N = 3SE +/- 2.17, N = 8SE +/- 0.19, N = 31006.00233.00179.0098.93

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: INT8 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-Q100200300400500SE +/- 0.10, N = 3SE +/- 0.05, N = 3SE +/- 0.01, N = 3475.0819.9115.79

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: INT8 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano30060090012001500SE +/- 0.25, N = 3SE +/- 0.04, N = 3SE +/- 0.08, N = 3SE +/- 0.06, N = 31215.0859.6947.1525.08

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG19 - Precision: INT8 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-Q60120180240300SE +/- 0.20, N = 3SE +/- 0.25, N = 4SE +/- 0.23, N = 3265.8114.3211.45

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet50 - Precision: FP16 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano140280420560700SE +/- 1.23, N = 3SE +/- 1.22, N = 3SE +/- 0.86, N = 3SE +/- 0.02, N = 3636.00111.0086.0846.51

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: VGG16 - Precision: INT8 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-Q70140210280350SE +/- 0.46, N = 3SE +/- 0.25, N = 6SE +/- 0.20, N = 5303.7817.5614.24

OpenBenchmarking.orgImages Per Second, More Is BetterNVIDIA TensorRT InferenceNeural Network: ResNet152 - Precision: INT8 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano80160240320400SE +/- 1.59, N = 3SE +/- 0.14, N = 3SE +/- 0.15, N = 3SE +/- 0.03, N = 3372.7318.2914.507.76

CUDA Mini-Nbody

OpenBenchmarking.org(NBody^2)/s, More Is BetterCUDA Mini-Nbody 2015-11-10Test: OriginalJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano1122334455SE +/- 0.00, N = 3SE +/- 0.01, N = 3SE +/- 0.03, N = 3SE +/- 0.01, N = 347.138.246.774.07

GLmark2

This is a test of any system-installed GLMark2 OpenGL benchmark. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgScore, More Is BetterGLmark2Resolution: 1920 x 1080Jetson AGX XavierJetson Nano60012001800240030002876646

TTSIOD 3D Renderer

OpenBenchmarking.orgFPS Per Dollar, More Is BetterTTSIOD 3D Renderer 2.3bPerformance / Cost - Phong Rendering With Soft-Shadow MappingJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QRaspberry Pi 3 Model B+ASUS TinkerBoardJetson TX1 Max-PODROID-XU4Jetson Nano0.1530.3060.4590.6120.7650.100.080.050.500.320.090.680.411. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Raspberry Pi 3 Model B+: $35 reported cost.5. ASUS TinkerBoard: $66 reported cost.6. Jetson TX1 Max-P: $499 reported cost.7. ODROID-XU4: $62 reported cost.8. Jetson Nano: $99 reported cost.

7-Zip Compression

OpenBenchmarking.orgMIPS Per Dollar, More Is Better7-Zip Compression 16.02Performance / Cost - Compress Speed TestJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QRaspberry Pi 3 Model B+ASUS TinkerBoardJetson TX1 Max-PODROID-XU4Jetson Nano153045607514.799.345.5057.5142.979.0366.4540.901. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Raspberry Pi 3 Model B+: $35 reported cost.5. ASUS TinkerBoard: $66 reported cost.6. Jetson TX1 Max-P: $499 reported cost.7. ODROID-XU4: $62 reported cost.8. Jetson Nano: $99 reported cost.

C-Ray

OpenBenchmarking.orgSeconds x Dollar, Fewer Is BetterC-Ray 1.1Performance / Cost - Total Time - 4K, 16 Rays Per PixelJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QRaspberry Pi 3 Model B+ASUS TinkerBoardJetson TX1 Max-PODROID-XU4Jetson Nano110K220K330K440K550K461145.00350415.00520531.0071050.00113388.00375747.0051274.0091179.001. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Raspberry Pi 3 Model B+: $35 reported cost.5. ASUS TinkerBoard: $66 reported cost.6. Jetson TX1 Max-P: $499 reported cost.7. ODROID-XU4: $62 reported cost.8. Jetson Nano: $99 reported cost.

Rust Prime Benchmark

OpenBenchmarking.orgSeconds x Dollar, Fewer Is BetterRust Prime BenchmarkPerformance / Cost - Prime Number Test To 200,000,000Jetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QRaspberry Pi 3 Model B+ASUS TinkerBoardJetson TX1 Max-PODROID-XU4Jetson Nano30K60K90K120K150K42048.6362871.04101979.7538419.15120189.3064096.5535594.8214868.811. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Raspberry Pi 3 Model B+: $35 reported cost.5. ASUS TinkerBoard: $66 reported cost.6. Jetson TX1 Max-P: $499 reported cost.7. ODROID-XU4: $62 reported cost.8. Jetson Nano: $99 reported cost.

Zstd Compression

OpenBenchmarking.orgSeconds x Dollar, Fewer Is BetterZstd Compression 1.3.4Performance / Cost - Compressing ubuntu-16.04.3-server-i386.img, Compression Level 19Jetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QRaspberry Pi 3 Model B+ASUS TinkerBoardJetson TX1 Max-PJetson Nano30K60K90K120K150K103997.9486837.03152026.2011978.0532776.9272754.2012857.131. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Raspberry Pi 3 Model B+: $35 reported cost.5. ASUS TinkerBoard: $66 reported cost.6. Jetson TX1 Max-P: $499 reported cost.7. Jetson Nano: $99 reported cost.

FLAC Audio Encoding

OpenBenchmarking.orgSeconds x Dollar, Fewer Is BetterFLAC Audio Encoding 1.3.2Performance / Cost - WAV To FLACJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QRaspberry Pi 3 Model B+ASUS TinkerBoardJetson TX1 Max-PODROID-XU4Jetson Nano15K30K45K60K75K70756.5338976.9362463.7211883.5518417.3039520.806015.8610372.231. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Raspberry Pi 3 Model B+: $35 reported cost.5. ASUS TinkerBoard: $66 reported cost.6. Jetson TX1 Max-P: $499 reported cost.7. ODROID-XU4: $62 reported cost.8. Jetson Nano: $99 reported cost.

PyBench

OpenBenchmarking.orgMilliseconds x Dollar, Fewer Is BetterPyBench 2018-02-16Performance / Cost - Total For Average Test TimesJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QRaspberry Pi 3 Model B+ASUS TinkerBoardJetson TX1 Max-PODROID-XU4Jetson Nano1.1M2.2M3.3M4.4M5.5M3906093.003239392.005232265.00731955.00759132.003163161.00310558.00701316.001. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Raspberry Pi 3 Model B+: $35 reported cost.5. ASUS TinkerBoard: $66 reported cost.6. Jetson TX1 Max-P: $499 reported cost.7. ODROID-XU4: $62 reported cost.8. Jetson Nano: $99 reported cost.

CUDA Mini-Nbody

OpenBenchmarking.org(NBody^2)/s Per Dollar, More Is BetterCUDA Mini-Nbody 2015-11-10Performance / Cost - Test: OriginalJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano0.0090.0180.0270.0360.0450.040.010.010.041. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Jetson Nano: $99 reported cost.

NVIDIA TensorRT Inference

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: VGG16 - Precision: FP16 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano0.0360.0720.1080.1440.180.160.050.040.141. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Jetson Nano: $99 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: VGG16 - Precision: INT8 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-Q0.05180.10360.15540.20720.2590.230.030.021. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: VGG19 - Precision: FP16 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano0.02930.05860.08790.11720.14650.130.040.040.121. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Jetson Nano: $99 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: VGG19 - Precision: INT8 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-Q0.0450.090.1350.180.2250.200.020.021. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: VGG16 - Precision: FP16 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-Q0.04280.08560.12840.17120.2140.190.060.051. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: VGG16 - Precision: INT8 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-Q0.08330.16660.24990.33320.41650.370.030.031. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: VGG19 - Precision: FP16 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-Q0.0360.0720.1080.1440.180.160.050.041. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: VGG19 - Precision: INT8 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-Q0.06750.1350.20250.270.33750.300.030.021. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: AlexNet - Precision: FP16 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano0.26780.53560.80341.07121.3390.920.440.361.191. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Jetson Nano: $99 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: AlexNet - Precision: INT8 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano0.1980.3960.5940.7920.990.880.310.250.851. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Jetson Nano: $99 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: AlexNet - Precision: FP16 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano0.45680.91361.37041.82722.2841.570.770.622.031. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Jetson Nano: $99 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: AlexNet - Precision: INT8 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano0.54451.0891.63352.1782.72252.420.500.401.291. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Jetson Nano: $99 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: ResNet50 - Precision: FP16 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano0.09450.1890.28350.3780.47250.420.150.120.411. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Jetson Nano: $99 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: ResNet50 - Precision: INT8 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano0.15530.31060.46590.62120.77650.690.080.070.211. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Jetson Nano: $99 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: GoogleNet - Precision: FP16 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano0.1890.3780.5670.7560.9450.610.330.260.841. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Jetson Nano: $99 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: GoogleNet - Precision: INT8 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano0.1980.3960.5940.7920.990.880.190.150.481. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Jetson Nano: $99 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: ResNet152 - Precision: FP16 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano0.03830.07660.11490.15320.19150.170.060.050.161. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Jetson Nano: $99 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: ResNet152 - Precision: INT8 - Batch Size: 4 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano0.06530.13060.19590.26120.32650.290.030.020.081. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Jetson Nano: $99 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: ResNet50 - Precision: FP16 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano0.11030.22060.33090.44120.55150.490.190.140.471. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Jetson Nano: $99 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: ResNet50 - Precision: INT8 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano0.21150.4230.63450.8461.05750.940.100.080.251. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Jetson Nano: $99 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: GoogleNet - Precision: FP16 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano0.2250.450.6750.91.1250.770.390.301.001. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Jetson Nano: $99 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: GoogleNet - Precision: INT8 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano0.29250.5850.87751.171.46251.300.220.170.561. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Jetson Nano: $99 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: ResNet152 - Precision: FP16 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QJetson Nano0.0450.090.1350.180.2250.200.070.050.181. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Jetson Nano: $99 reported cost.

OpenBenchmarking.orgImages Per Second Per Dollar, More Is BetterNVIDIA TensorRT InferencePerformance / Cost - Neural Network: ResNet152 - Precision: INT8 - Batch Size: 32 - DLA Cores: DisabledJetson AGX XavierJetson TX2 Max-PJetson TX2 Max-Q0.08550.1710.25650.3420.42750.380.040.031. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.

OpenCV Benchmark

OpenBenchmarking.orgSeconds x Dollar, Fewer Is BetterOpenCV Benchmark 3.3.0Performance / Cost -Jetson AGX XavierJetson TX2 Max-PJetson TX2 Max-QRaspberry Pi 3 Model B+ODROID-XU4Jetson Nano60K120K180K240K300K166272.00177304.00295307.0095.9032283.4026832.961. Jetson AGX Xavier: $1299 reported cost.2. Jetson TX2 Max-P: $599 reported cost.3. Jetson TX2 Max-Q: $599 reported cost.4. Raspberry Pi 3 Model B+: $35 reported cost.5. ODROID-XU4: $62 reported cost.6. Jetson Nano: $99 reported cost.

GLmark2

OpenBenchmarking.orgScore Per Dollar, More Is BetterGLmark2Performance / Cost - Resolution: 1920 x 1080Jetson AGX XavierJetson Nano2468102.216.531. Jetson AGX Xavier: $1299 reported cost.2. Jetson Nano: $99 reported cost.

LeelaChessZero

OpenBenchmarking.orgNodes Per Second Per Dollar, More Is BetterLeelaChessZero 0.20.1Performance / Cost - Backend: BLASJetson AGX XavierJetson Nano0.0360.0720.1080.1440.180.040.161. Jetson AGX Xavier: $1299 reported cost.2. Jetson Nano: $99 reported cost.

OpenBenchmarking.orgNodes Per Second Per Dollar, More Is BetterLeelaChessZero 0.20.1Performance / Cost - Backend: CUDA + cuDNNJetson AGX XavierJetson Nano0.31730.63460.95191.26921.58650.731.411. Jetson AGX Xavier: $1299 reported cost.2. Jetson Nano: $99 reported cost.

OpenBenchmarking.orgNodes Per Second Per Dollar, More Is BetterLeelaChessZero 0.20.1Performance / Cost - Backend: CUDA + cuDNN FP16Jetson AGX Xavier0.43650.8731.30951.7462.18251.941. $1299 reported cost.

Tesseract OCR

OpenBenchmarking.orgSeconds x Dollar, Fewer Is BetterTesseract OCR 4.0.0-beta.1Performance / Cost - Time To OCR 7 ImagesJetson AGX XavierODROID-XU4Jetson Nano20K40K60K80K100K93450.0611200.9213134.331. Jetson AGX Xavier: $1299 reported cost.2. ODROID-XU4: $62 reported cost.3. Jetson Nano: $99 reported cost.

76 Results Shown

FLAC Audio Encoding
Tesseract OCR
NVIDIA TensorRT Inference
LeelaChessZero
NVIDIA TensorRT Inference
LeelaChessZero
NVIDIA TensorRT Inference:
  VGG19 - FP16 - 4 - Disabled
  ResNet152 - FP16 - 4 - Disabled
  VGG16 - FP16 - 4 - Disabled
  VGG19 - FP16 - 32 - Disabled
  VGG16 - FP16 - 32 - Disabled
  GoogleNet - FP16 - 4 - Disabled
  AlexNet - FP16 - 4 - Disabled
  ResNet50 - FP16 - 4 - Disabled
LeelaChessZero
Rust Prime Benchmark
7-Zip Compression
Zstd Compression
C-Ray
TTSIOD 3D Renderer
OpenCV Benchmark
PyBench
NVIDIA TensorRT Inference:
  ResNet50 - INT8 - 4 - Disabled
  ResNet152 - INT8 - 32 - Disabled
  AlexNet - INT8 - 32 - Disabled
  ResNet152 - FP16 - 32 - Disabled
  AlexNet - INT8 - 4 - Disabled
  GoogleNet - INT8 - 32 - Disabled
  VGG19 - INT8 - 32 - Disabled
  GoogleNet - FP16 - 32 - Disabled
  VGG16 - INT8 - 32 - Disabled
  ResNet50 - INT8 - 32 - Disabled
  VGG19 - INT8 - 4 - Disabled
  ResNet50 - FP16 - 32 - Disabled
  VGG16 - INT8 - 4 - Disabled
  ResNet152 - INT8 - 4 - Disabled
CUDA Mini-Nbody
GLmark2
TTSIOD 3D Renderer:
  Performance / Cost - Phong Rendering With Soft-Shadow Mapping
  Performance / Cost - Compress Speed Test
  Performance / Cost - Total Time - 4K, 16 Rays Per Pixel
  Performance / Cost - Prime Number Test To 200,000,000
  Performance / Cost - Compressing ubuntu-16.04.3-server-i386.img, Compression Level 19
  Performance / Cost - WAV To FLAC
  Performance / Cost - Total For Average Test Times
  Performance / Cost - Original
  Performance / Cost - VGG16 - FP16 - 4 - Disabled
  Performance / Cost - VGG16 - INT8 - 4 - Disabled
  Performance / Cost - VGG19 - FP16 - 4 - Disabled
  Performance / Cost - VGG19 - INT8 - 4 - Disabled
  Performance / Cost - VGG16 - FP16 - 32 - Disabled
  Performance / Cost - VGG16 - INT8 - 32 - Disabled
  Performance / Cost - VGG19 - FP16 - 32 - Disabled
  Performance / Cost - VGG19 - INT8 - 32 - Disabled
  Performance / Cost - AlexNet - FP16 - 4 - Disabled
  Performance / Cost - AlexNet - INT8 - 4 - Disabled
  Performance / Cost - AlexNet - FP16 - 32 - Disabled
  Performance / Cost - AlexNet - INT8 - 32 - Disabled
  Performance / Cost - ResNet50 - FP16 - 4 - Disabled
  Performance / Cost - ResNet50 - INT8 - 4 - Disabled
  Performance / Cost - GoogleNet - FP16 - 4 - Disabled
  Performance / Cost - GoogleNet - INT8 - 4 - Disabled
  Performance / Cost - ResNet152 - FP16 - 4 - Disabled
  Performance / Cost - ResNet152 - INT8 - 4 - Disabled
  Performance / Cost - ResNet50 - FP16 - 32 - Disabled
  Performance / Cost - ResNet50 - INT8 - 32 - Disabled
  Performance / Cost - GoogleNet - FP16 - 32 - Disabled
  Performance / Cost - GoogleNet - INT8 - 32 - Disabled
  Performance / Cost - ResNet152 - FP16 - 32 - Disabled
  Performance / Cost - ResNet152 - INT8 - 32 - Disabled
  Performance / Cost -
  Performance / Cost - 1920 x 1080
  Performance / Cost - BLAS
  Performance / Cost - CUDA + cuDNN
  Performance / Cost - CUDA + cuDNN FP16
  Performance / Cost - Time To OCR 7 Images