2024 year AMD Ryzen Threadripper PRO 5965WX 24-Cores testing with a ASUS Pro WS WRX80E-SAGE SE WIFI (1201 BIOS) and ASUS NVIDIA NV106 2GB on Ubuntu 23.10 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2402040-NE-2024YEAR116&grr&sro .
2024 year Processor Motherboard Chipset Memory Disk Graphics Audio Monitor Network OS Kernel Desktop Display Server Display Driver OpenGL Compiler File-System Screen Resolution a b c d AMD Ryzen Threadripper PRO 5965WX 24-Cores @ 3.80GHz (24 Cores / 48 Threads) ASUS Pro WS WRX80E-SAGE SE WIFI (1201 BIOS) AMD Starship/Matisse 8 x 16GB DDR4-2133MT/s Corsair CMK32GX4M2E3200C16 2048GB SOLIDIGM SSDPFKKW020X7 ASUS NVIDIA NV106 2GB AMD Starship/Matisse VA2431 2 x Intel X550 + Intel Wi-Fi 6 AX200 Ubuntu 23.10 6.5.0-13-generic (x86_64) GNOME Shell 45.0 X Server + Wayland nouveau 4.3 Mesa 23.2.1-1ubuntu3 GCC 13.2.0 ext4 1920x1080 OpenBenchmarking.org Kernel Details - Transparent Huge Pages: madvise Compiler Details - --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --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-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-XYspKM/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-XYspKM/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-build-config=bootstrap-lto-lean --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 Processor Details - Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0xa008205 Python Details - Python 3.11.6 Security Details - gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + spec_rstack_overflow: Mitigation of safe RET no microcode + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected
2024 year lczero: BLAS lczero: Eigen quicksilver: CTS2 llama-cpp: llama-2-70b-chat.Q5_0.gguf tensorflow: CPU - 16 - VGG-16 quicksilver: CORAL2 P2 cachebench: Read cachebench: Read / Modify / Write cachebench: Write tensorflow: CPU - 16 - ResNet-50 llamafile: mistral-7b-instruct-v0.2.Q8_0 - CPU speedb: Seq Fill rav1e: 1 rav1e: 10 deepsparse: BERT-Large, NLP Question Answering - Asynchronous Multi-Stream deepsparse: BERT-Large, NLP Question Answering - Asynchronous Multi-Stream deepsparse: BERT-Large, NLP Question Answering - Synchronous Single-Stream deepsparse: BERT-Large, NLP Question Answering - Synchronous Single-Stream deepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-Stream deepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-Stream llamafile: wizardcoder-python-34b-v1.0.Q6_K - CPU pytorch: CPU - 256 - ResNet-50 pytorch: CPU - 16 - ResNet-50 speedb: Rand Fill Sync speedb: Rand Fill speedb: Update Rand speedb: Read While Writing speedb: Read Rand Write Rand speedb: Rand Read rav1e: 5 quicksilver: CORAL2 P1 deepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Synchronous Single-Stream deepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Synchronous Single-Stream deepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Stream deepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Stream deepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Stream deepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Stream deepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Stream deepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Stream deepsparse: NLP Document Classification, oBERT base uncased on IMDB - Synchronous Single-Stream deepsparse: NLP Document Classification, oBERT base uncased on IMDB - Synchronous Single-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Stream tensorflow: CPU - 1 - VGG-16 deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Stream deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Stream deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Stream deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Stream deepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Stream deepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Stream llama-cpp: llama-2-13b.Q4_0.gguf deepsparse: NLP Text Classification, DistilBERT mnli - Synchronous Single-Stream deepsparse: NLP Text Classification, DistilBERT mnli - Synchronous Single-Stream deepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Stream deepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Stream rav1e: 6 deepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream deepsparse: ResNet-50, Baseline - Asynchronous Multi-Stream deepsparse: ResNet-50, Baseline - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Synchronous Single-Stream deepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Synchronous Single-Stream deepsparse: ResNet-50, Baseline - Synchronous Single-Stream deepsparse: ResNet-50, Baseline - Synchronous Single-Stream deepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Stream deepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO - Synchronous Single-Stream deepsparse: CV Detection, YOLOv5s COCO - Synchronous Single-Stream deepsparse: CV Classification, ResNet-50 ImageNet - Synchronous Single-Stream deepsparse: CV Classification, ResNet-50 ImageNet - Synchronous Single-Stream deepsparse: ResNet-50, Sparse INT8 - Synchronous Single-Stream deepsparse: ResNet-50, Sparse INT8 - Synchronous Single-Stream tensorflow: CPU - 16 - GoogLeNet pytorch: CPU - 1 - ResNet-50 svt-av1: Preset 4 - Bosphorus 4K compress-lz4: 9 - Decompression Speed compress-lz4: 9 - Compression Speed llama-cpp: llama-2-7b.Q4_0.gguf compress-lz4: 3 - Decompression Speed compress-lz4: 3 - Compression Speed compress-lz4: 1 - Decompression Speed compress-lz4: 1 - Compression Speed tensorflow: CPU - 16 - AlexNet tensorflow: CPU - 1 - AlexNet y-cruncher: 1B llamafile: llava-v1.5-7b-q4 - CPU tensorflow: CPU - 1 - ResNet-50 tensorflow: CPU - 1 - GoogLeNet svt-av1: Preset 8 - Bosphorus 4K svt-av1: Preset 4 - Bosphorus 1080p y-cruncher: 500M svt-av1: Preset 12 - Bosphorus 4K svt-av1: Preset 13 - Bosphorus 4K svt-av1: Preset 8 - Bosphorus 1080p svt-av1: Preset 12 - Bosphorus 1080p svt-av1: Preset 13 - Bosphorus 1080p a b c d 173 121 20680000 1.94 8.51 24030000 11543.372321 130857.577562 69134.680498 19.87 10.13 620607 1.044 10.634 368.3051 32.5188 58.254 17.1616 13.129 76.1394 3.25 31.92 32.50 47488 558330 431692 7004007 2327911 148134848 3.747 24210000 5.2624 189.9093 17.495 685.1217 446.6313 26.8394 35.762 335.3479 445.2565 26.9119 54.3716 18.3872 54.1194 18.4727 2.72 392.9588 30.489 45.9976 21.7296 53.4699 224.1802 11.64 8.9109 112.0914 5.9507 2012.2062 5.261 79.8855 150.0421 39.0436 307.0146 79.1618 151.4666 10.1046 98.9102 6.3518 157.2286 39.0493 306.9919 10.1424 98.516 6.3619 156.9701 1.3175 757.1631 60.85 40.68 6.677 4840.5 44.28 20.76 4595.9 131.24 5019.5 828.78 100.44 6.26 15.545 17.22 8.85 9.94 61.53 18.803 7.325 190.916 190.794 122.948 501.42 543.554 219 146 20646667 1.94 8.46 24026667 11543.164687 130069.848338 69140.530992 19.45 10.15 618776 1.048 10.885 369.7720 32.3621 58.3490 17.1337 13.2388 75.5067 3.25 32.15 32.10 47708 554997 418848 7070407 2307686 147432214 3.791 24230000 5.1753 193.1106 17.5999 681.0038 448.9079 26.6478 35.9726 333.3674 448.0408 26.6672 54.5365 18.3317 54.3340 18.4000 2.70 394.7759 30.2809 46.5453 21.4743 54.0612 221.7053 11.32 8.9755 111.2892 6.1010 1962.7047 5.292 80.8465 148.2388 39.3339 304.7712 80.0470 149.7818 10.1213 98.7456 6.4230 155.4837 39.2555 305.3795 10.1362 98.5635 6.4208 155.5402 1.3252 752.7784 60.18 40.42 6.669 4841.4 44.48 20.95 4597.9 131.10 5020.0 829.36 100.01 6.23 15.497 17.26 8.79 9.74 61.830 18.682 7.349 190.402 192.726 123.293 506.596 573.042 225 154 20620000 1.95 8.48 23890000 11543.096362 130806.245683 69142.435503 19.61 10.14 604758 1.044 10.957 369.6228 32.4212 57.9978 17.2376 13.1545 75.9898 3.25 31.59 31.65 47373 557348 423788 7047502 2320670 146285738 3.769 24240000 5.1787 192.9801 17.5377 683.2461 449.2956 26.6741 35.9278 333.6358 448.9031 26.705 54.4554 18.3589 54.1474 18.4635 2.72 393.8346 30.451 46.0912 21.6853 53.7417 223.0515 11.27 8.952 111.5864 6.0747 1970.6106 5.282 80.5729 148.7561 39.1233 306.5028 79.8946 149.9507 10.146 98.4979 6.4266 155.403 39.1181 306.5175 10.1277 98.6526 6.4178 155.6182 1.3025 765.9923 60.04 40.82 6.678 4842.4 45.49 20.74 4598 131.4 5023.2 829.15 100.08 6.23 15.532 17.3 8.85 16.49 61.47 18.409 7.301 192.157 189.252 122.95 501.156 580.467 213 151 20600000 1.95 8.51 23840000 11543.486939 130851.30507 69142.188854 19.81 10.13 612428 1.054 11.022 369.53 32.3762 58.4094 17.1162 13.204 75.7073 3.25 32.07 32.21 47267 556675 417457 6896007 2316804 146473036 3.891 24290000 5.2804 189.2673 17.5865 681.5303 449.1605 26.6902 35.837 334.6424 448.0349 26.5435 54.3471 18.3959 54.204 18.4443 2.69 394.3756 30.3159 46.1334 21.6655 53.8237 222.7232 11.25 8.9586 111.4965 6.1024 1962.1551 5.191 80.2684 149.2683 39.1902 305.8614 80.052 149.7647 10.1068 98.8912 6.4345 155.2016 39.977 299.8295 10.1151 98.7671 6.4346 155.1926 1.3175 757.2715 59.82 40.35 6.633 4844.5 44.52 20.68 4596.7 131.61 5019.5 830.37 99.9 6.21 15.501 17.25 8.89 9.73 60.95 18.922 7.297 192.683 192.603 122.616 506.174 565.906 OpenBenchmarking.org
LeelaChessZero Backend: BLAS OpenBenchmarking.org Nodes Per Second, More Is Better LeelaChessZero 0.30 Backend: BLAS a b c d 50 100 150 200 250 SE +/- 0.33, N = 3 173 219 225 213 1. (CXX) g++ options: -flto -pthread
LeelaChessZero Backend: Eigen OpenBenchmarking.org Nodes Per Second, More Is Better LeelaChessZero 0.30 Backend: Eigen a b c d 30 60 90 120 150 SE +/- 2.08, N = 3 121 146 154 151 1. (CXX) g++ options: -flto -pthread
Quicksilver Input: CTS2 OpenBenchmarking.org Figure Of Merit, More Is Better Quicksilver 20230818 Input: CTS2 a b c d 4M 8M 12M 16M 20M SE +/- 6666.67, N = 3 20680000 20646667 20620000 20600000 1. (CXX) g++ options: -fopenmp -O3 -march=native
Llama.cpp Model: llama-2-70b-chat.Q5_0.gguf OpenBenchmarking.org Tokens Per Second, More Is Better Llama.cpp b1808 Model: llama-2-70b-chat.Q5_0.gguf a b c d 0.4388 0.8776 1.3164 1.7552 2.194 SE +/- 0.00, N = 3 1.94 1.94 1.95 1.95 1. (CXX) g++ options: -std=c++11 -fPIC -O3 -pthread -march=native -mtune=native -lopenblas
TensorFlow Device: CPU - Batch Size: 16 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 16 - Model: VGG-16 a b c d 2 4 6 8 10 SE +/- 0.03, N = 3 8.51 8.46 8.48 8.51
Quicksilver Input: CORAL2 P2 OpenBenchmarking.org Figure Of Merit, More Is Better Quicksilver 20230818 Input: CORAL2 P2 a b c d 5M 10M 15M 20M 25M SE +/- 3333.33, N = 3 24030000 24026667 23890000 23840000 1. (CXX) g++ options: -fopenmp -O3 -march=native
CacheBench Test: Read OpenBenchmarking.org MB/s, More Is Better CacheBench Test: Read a b c d 2K 4K 6K 8K 10K SE +/- 0.09, N = 3 11543.37 11543.16 11543.10 11543.49 MIN: 11542.37 / MAX: 11544.55 MIN: 11542.65 / MAX: 11544.48 MIN: 11542.7 / MAX: 11543.41 MIN: 11542.8 / MAX: 11544.64 1. (CC) gcc options: -O3 -lrt
CacheBench Test: Read / Modify / Write OpenBenchmarking.org MB/s, More Is Better CacheBench Test: Read / Modify / Write a b c d 30K 60K 90K 120K 150K SE +/- 386.79, N = 3 130857.58 130069.85 130806.25 130851.31 MIN: 112608.55 / MAX: 137126.28 MIN: 101861.72 / MAX: 137133.31 MIN: 112724.52 / MAX: 137125.96 MIN: 112492.8 / MAX: 137124.99 1. (CC) gcc options: -O3 -lrt
CacheBench Test: Write OpenBenchmarking.org MB/s, More Is Better CacheBench Test: Write a b c d 15K 30K 45K 60K 75K SE +/- 3.29, N = 3 69134.68 69140.53 69142.44 69142.19 MIN: 68881.15 / MAX: 69208.76 MIN: 68883.98 / MAX: 69225.86 MIN: 68884.8 / MAX: 69218.23 MIN: 68886.61 / MAX: 69217.36 1. (CC) gcc options: -O3 -lrt
TensorFlow Device: CPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 16 - Model: ResNet-50 a b c d 5 10 15 20 25 SE +/- 0.08, N = 3 19.87 19.45 19.61 19.81
Llamafile Test: mistral-7b-instruct-v0.2.Q8_0 - Acceleration: CPU OpenBenchmarking.org Tokens Per Second, More Is Better Llamafile 0.6 Test: mistral-7b-instruct-v0.2.Q8_0 - Acceleration: CPU a b c d 3 6 9 12 15 SE +/- 0.01, N = 3 10.13 10.15 10.14 10.13
Speedb Test: Sequential Fill OpenBenchmarking.org Op/s, More Is Better Speedb 2.7 Test: Sequential Fill a b c d 130K 260K 390K 520K 650K SE +/- 3239.87, N = 3 620607 618776 604758 612428 1. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
rav1e Speed: 1 OpenBenchmarking.org Frames Per Second, More Is Better rav1e 0.7 Speed: 1 a b c d 0.2372 0.4744 0.7116 0.9488 1.186 SE +/- 0.004, N = 3 1.044 1.048 1.044 1.054
rav1e Speed: 10 OpenBenchmarking.org Frames Per Second, More Is Better rav1e 0.7 Speed: 10 a b c d 3 6 9 12 15 SE +/- 0.11, N = 5 10.63 10.89 10.96 11.02
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream a b c d 80 160 240 320 400 SE +/- 0.27, N = 3 368.31 369.77 369.62 369.53
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream a b c d 8 16 24 32 40 SE +/- 0.05, N = 3 32.52 32.36 32.42 32.38
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream a b c d 13 26 39 52 65 SE +/- 0.07, N = 3 58.25 58.35 58.00 58.41
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream a b c d 4 8 12 16 20 SE +/- 0.02, N = 3 17.16 17.13 17.24 17.12
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream a b c d 3 6 9 12 15 SE +/- 0.02, N = 3 13.13 13.24 13.15 13.20
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream a b c d 20 40 60 80 100 SE +/- 0.14, N = 3 76.14 75.51 75.99 75.71
Llamafile Test: wizardcoder-python-34b-v1.0.Q6_K - Acceleration: CPU OpenBenchmarking.org Tokens Per Second, More Is Better Llamafile 0.6 Test: wizardcoder-python-34b-v1.0.Q6_K - Acceleration: CPU a b c d 0.7313 1.4626 2.1939 2.9252 3.6565 SE +/- 0.00, N = 3 3.25 3.25 3.25 3.25
PyTorch Device: CPU - Batch Size: 256 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 256 - Model: ResNet-50 a b c d 7 14 21 28 35 SE +/- 0.11, N = 3 31.92 32.15 31.59 32.07 MIN: 30 / MAX: 32.18 MIN: 30.21 / MAX: 32.69 MIN: 29.73 / MAX: 32.12 MIN: 30.1 / MAX: 32.3
PyTorch Device: CPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 16 - Model: ResNet-50 a b c d 8 16 24 32 40 SE +/- 0.12, N = 3 32.50 32.10 31.65 32.21 MIN: 30.56 / MAX: 32.75 MIN: 29.1 / MAX: 32.53 MIN: 29.55 / MAX: 31.86 MIN: 30.29 / MAX: 32.43
Speedb Test: Random Fill Sync OpenBenchmarking.org Op/s, More Is Better Speedb 2.7 Test: Random Fill Sync a b c d 10K 20K 30K 40K 50K SE +/- 66.17, N = 3 47488 47708 47373 47267 1. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
Speedb Test: Random Fill OpenBenchmarking.org Op/s, More Is Better Speedb 2.7 Test: Random Fill a b c d 120K 240K 360K 480K 600K SE +/- 4227.88, N = 3 558330 554997 557348 556675 1. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
Speedb Test: Update Random OpenBenchmarking.org Op/s, More Is Better Speedb 2.7 Test: Update Random a b c d 90K 180K 270K 360K 450K SE +/- 4060.59, N = 3 431692 418848 423788 417457 1. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
Speedb Test: Read While Writing OpenBenchmarking.org Op/s, More Is Better Speedb 2.7 Test: Read While Writing a b c d 1.5M 3M 4.5M 6M 7.5M SE +/- 60887.57, N = 3 7004007 7070407 7047502 6896007 1. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
Speedb Test: Read Random Write Random OpenBenchmarking.org Op/s, More Is Better Speedb 2.7 Test: Read Random Write Random a b c d 500K 1000K 1500K 2000K 2500K SE +/- 1258.96, N = 3 2327911 2307686 2320670 2316804 1. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
Speedb Test: Random Read OpenBenchmarking.org Op/s, More Is Better Speedb 2.7 Test: Random Read a b c d 30M 60M 90M 120M 150M SE +/- 81483.06, N = 3 148134848 147432214 146285738 146473036 1. (CXX) g++ options: -O3 -march=native -pthread -fno-builtin-memcmp -fno-rtti -lpthread
rav1e Speed: 5 OpenBenchmarking.org Frames Per Second, More Is Better rav1e 0.7 Speed: 5 a b c d 0.8755 1.751 2.6265 3.502 4.3775 SE +/- 0.014, N = 3 3.747 3.791 3.769 3.891
Quicksilver Input: CORAL2 P1 OpenBenchmarking.org Figure Of Merit, More Is Better Quicksilver 20230818 Input: CORAL2 P1 a b c d 5M 10M 15M 20M 25M SE +/- 11547.01, N = 3 24210000 24230000 24240000 24290000 1. (CXX) g++ options: -fopenmp -O3 -march=native
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream a b c d 1.1881 2.3762 3.5643 4.7524 5.9405 SE +/- 0.0173, N = 3 5.2624 5.1753 5.1787 5.2804
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream a b c d 40 80 120 160 200 SE +/- 0.65, N = 3 189.91 193.11 192.98 189.27
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c d 4 8 12 16 20 SE +/- 0.02, N = 3 17.50 17.60 17.54 17.59
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c d 150 300 450 600 750 SE +/- 0.83, N = 3 685.12 681.00 683.25 681.53
Neural Magic DeepSparse Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream a b c d 100 200 300 400 500 SE +/- 0.27, N = 3 446.63 448.91 449.30 449.16
Neural Magic DeepSparse Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream a b c d 6 12 18 24 30 SE +/- 0.05, N = 3 26.84 26.65 26.67 26.69
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c d 8 16 24 32 40 SE +/- 0.05, N = 3 35.76 35.97 35.93 35.84
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c d 70 140 210 280 350 SE +/- 0.44, N = 3 335.35 333.37 333.64 334.64
Neural Magic DeepSparse Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream a b c d 100 200 300 400 500 SE +/- 0.32, N = 3 445.26 448.04 448.90 448.03
Neural Magic DeepSparse Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream a b c d 6 12 18 24 30 SE +/- 0.03, N = 3 26.91 26.67 26.71 26.54
Neural Magic DeepSparse Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream a b c d 12 24 36 48 60 SE +/- 0.02, N = 3 54.37 54.54 54.46 54.35
Neural Magic DeepSparse Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream a b c d 5 10 15 20 25 SE +/- 0.01, N = 3 18.39 18.33 18.36 18.40
Neural Magic DeepSparse Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream a b c d 12 24 36 48 60 SE +/- 0.03, N = 3 54.12 54.33 54.15 54.20
Neural Magic DeepSparse Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream a b c d 5 10 15 20 25 SE +/- 0.01, N = 3 18.47 18.40 18.46 18.44
TensorFlow Device: CPU - Batch Size: 1 - Model: VGG-16 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 1 - Model: VGG-16 a b c d 0.612 1.224 1.836 2.448 3.06 SE +/- 0.01, N = 3 2.72 2.70 2.72 2.69
Neural Magic DeepSparse Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream a b c d 90 180 270 360 450 SE +/- 0.41, N = 3 392.96 394.78 393.83 394.38
Neural Magic DeepSparse Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream a b c d 7 14 21 28 35 SE +/- 0.02, N = 3 30.49 30.28 30.45 30.32
Neural Magic DeepSparse Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream a b c d 11 22 33 44 55 SE +/- 0.12, N = 3 46.00 46.55 46.09 46.13
Neural Magic DeepSparse Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream a b c d 5 10 15 20 25 SE +/- 0.05, N = 3 21.73 21.47 21.69 21.67
Neural Magic DeepSparse Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream a b c d 12 24 36 48 60 SE +/- 0.03, N = 3 53.47 54.06 53.74 53.82
Neural Magic DeepSparse Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream a b c d 50 100 150 200 250 SE +/- 0.12, N = 3 224.18 221.71 223.05 222.72
Llama.cpp Model: llama-2-13b.Q4_0.gguf OpenBenchmarking.org Tokens Per Second, More Is Better Llama.cpp b1808 Model: llama-2-13b.Q4_0.gguf a b c d 3 6 9 12 15 SE +/- 0.06, N = 3 11.64 11.32 11.27 11.25 1. (CXX) g++ options: -std=c++11 -fPIC -O3 -pthread -march=native -mtune=native -lopenblas
Neural Magic DeepSparse Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream a b c d 3 6 9 12 15 SE +/- 0.0211, N = 3 8.9109 8.9755 8.9520 8.9586
Neural Magic DeepSparse Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream a b c d 30 60 90 120 150 SE +/- 0.26, N = 3 112.09 111.29 111.59 111.50
Neural Magic DeepSparse Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c d 2 4 6 8 10 SE +/- 0.0329, N = 3 5.9507 6.1010 6.0747 6.1024
Neural Magic DeepSparse Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c d 400 800 1200 1600 2000 SE +/- 10.49, N = 3 2012.21 1962.70 1970.61 1962.16
rav1e Speed: 6 OpenBenchmarking.org Frames Per Second, More Is Better rav1e 0.7 Speed: 6 a b c d 1.1907 2.3814 3.5721 4.7628 5.9535 SE +/- 0.008, N = 3 5.261 5.292 5.282 5.191
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream a b c d 20 40 60 80 100 SE +/- 0.20, N = 3 79.89 80.85 80.57 80.27
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream a b c d 30 60 90 120 150 SE +/- 0.36, N = 3 150.04 148.24 148.76 149.27
Neural Magic DeepSparse Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream a b c d 9 18 27 36 45 SE +/- 0.01, N = 3 39.04 39.33 39.12 39.19
Neural Magic DeepSparse Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream a b c d 70 140 210 280 350 SE +/- 0.13, N = 3 307.01 304.77 306.50 305.86
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c d 20 40 60 80 100 SE +/- 0.02, N = 3 79.16 80.05 79.89 80.05
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream a b c d 30 60 90 120 150 SE +/- 0.01, N = 3 151.47 149.78 149.95 149.76
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream a b c d 3 6 9 12 15 SE +/- 0.03, N = 3 10.10 10.12 10.15 10.11
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream a b c d 20 40 60 80 100 SE +/- 0.29, N = 3 98.91 98.75 98.50 98.89
Neural Magic DeepSparse Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream a b c d 2 4 6 8 10 SE +/- 0.0106, N = 3 6.3518 6.4230 6.4266 6.4345
Neural Magic DeepSparse Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream a b c d 30 60 90 120 150 SE +/- 0.26, N = 3 157.23 155.48 155.40 155.20
Neural Magic DeepSparse Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream a b c d 9 18 27 36 45 SE +/- 0.09, N = 3 39.05 39.26 39.12 39.98
Neural Magic DeepSparse Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream a b c d 70 140 210 280 350 SE +/- 0.76, N = 3 306.99 305.38 306.52 299.83
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream a b c d 3 6 9 12 15 SE +/- 0.01, N = 3 10.14 10.14 10.13 10.12
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream a b c d 20 40 60 80 100 SE +/- 0.09, N = 3 98.52 98.56 98.65 98.77
Neural Magic DeepSparse Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream a b c d 2 4 6 8 10 SE +/- 0.0037, N = 3 6.3619 6.4208 6.4178 6.4346
Neural Magic DeepSparse Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream a b c d 30 60 90 120 150 SE +/- 0.08, N = 3 156.97 155.54 155.62 155.19
Neural Magic DeepSparse Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream a b c d 0.2982 0.5964 0.8946 1.1928 1.491 SE +/- 0.0024, N = 3 1.3175 1.3252 1.3025 1.3175
Neural Magic DeepSparse Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.6 Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream a b c d 170 340 510 680 850 SE +/- 1.33, N = 3 757.16 752.78 765.99 757.27
TensorFlow Device: CPU - Batch Size: 16 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 16 - Model: GoogLeNet a b c d 14 28 42 56 70 SE +/- 0.36, N = 3 60.85 60.18 60.04 59.82
PyTorch Device: CPU - Batch Size: 1 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 1 - Model: ResNet-50 a b c d 9 18 27 36 45 SE +/- 0.19, N = 3 40.68 40.42 40.82 40.35 MIN: 37.73 / MAX: 40.91 MIN: 37.5 / MAX: 41 MIN: 37.73 / MAX: 41.05 MIN: 37.34 / MAX: 40.65
SVT-AV1 Encoder Mode: Preset 4 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.8 Encoder Mode: Preset 4 - Input: Bosphorus 4K a b c d 2 4 6 8 10 SE +/- 0.015, N = 3 6.677 6.669 6.678 6.633 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
LZ4 Compression Compression Level: 9 - Decompression Speed OpenBenchmarking.org MB/s, More Is Better LZ4 Compression 1.9.4 Compression Level: 9 - Decompression Speed a b c d 1000 2000 3000 4000 5000 SE +/- 1.12, N = 3 4840.5 4841.4 4842.4 4844.5 1. (CC) gcc options: -O3
LZ4 Compression Compression Level: 9 - Compression Speed OpenBenchmarking.org MB/s, More Is Better LZ4 Compression 1.9.4 Compression Level: 9 - Compression Speed a b c d 10 20 30 40 50 SE +/- 0.02, N = 3 44.28 44.48 45.49 44.52 1. (CC) gcc options: -O3
Llama.cpp Model: llama-2-7b.Q4_0.gguf OpenBenchmarking.org Tokens Per Second, More Is Better Llama.cpp b1808 Model: llama-2-7b.Q4_0.gguf a b c d 5 10 15 20 25 SE +/- 0.24, N = 4 20.76 20.95 20.74 20.68 1. (CXX) g++ options: -std=c++11 -fPIC -O3 -pthread -march=native -mtune=native -lopenblas
LZ4 Compression Compression Level: 3 - Decompression Speed OpenBenchmarking.org MB/s, More Is Better LZ4 Compression 1.9.4 Compression Level: 3 - Decompression Speed a b c d 1000 2000 3000 4000 5000 SE +/- 0.63, N = 3 4595.9 4597.9 4598.0 4596.7 1. (CC) gcc options: -O3
LZ4 Compression Compression Level: 3 - Compression Speed OpenBenchmarking.org MB/s, More Is Better LZ4 Compression 1.9.4 Compression Level: 3 - Compression Speed a b c d 30 60 90 120 150 SE +/- 0.30, N = 3 131.24 131.10 131.40 131.61 1. (CC) gcc options: -O3
LZ4 Compression Compression Level: 1 - Decompression Speed OpenBenchmarking.org MB/s, More Is Better LZ4 Compression 1.9.4 Compression Level: 1 - Decompression Speed a b c d 1100 2200 3300 4400 5500 SE +/- 1.42, N = 3 5019.5 5020.0 5023.2 5019.5 1. (CC) gcc options: -O3
LZ4 Compression Compression Level: 1 - Compression Speed OpenBenchmarking.org MB/s, More Is Better LZ4 Compression 1.9.4 Compression Level: 1 - Compression Speed a b c d 200 400 600 800 1000 SE +/- 0.63, N = 3 828.78 829.36 829.15 830.37 1. (CC) gcc options: -O3
TensorFlow Device: CPU - Batch Size: 16 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 16 - Model: AlexNet a b c d 20 40 60 80 100 SE +/- 0.20, N = 3 100.44 100.01 100.08 99.90
TensorFlow Device: CPU - Batch Size: 1 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 1 - Model: AlexNet a b c d 2 4 6 8 10 SE +/- 0.01, N = 3 6.26 6.23 6.23 6.21
Y-Cruncher Pi Digits To Calculate: 1B OpenBenchmarking.org Seconds, Fewer Is Better Y-Cruncher 0.8.3 Pi Digits To Calculate: 1B a b c d 4 8 12 16 20 SE +/- 0.01, N = 3 15.55 15.50 15.53 15.50
Llamafile Test: llava-v1.5-7b-q4 - Acceleration: CPU OpenBenchmarking.org Tokens Per Second, More Is Better Llamafile 0.6 Test: llava-v1.5-7b-q4 - Acceleration: CPU a b c d 4 8 12 16 20 SE +/- 0.01, N = 3 17.22 17.26 17.30 17.25
TensorFlow Device: CPU - Batch Size: 1 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 1 - Model: ResNet-50 a b c d 2 4 6 8 10 SE +/- 0.05, N = 3 8.85 8.79 8.85 8.89
TensorFlow Device: CPU - Batch Size: 1 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 1 - Model: GoogLeNet a b c d 4 8 12 16 20 SE +/- 0.09, N = 3 9.94 9.74 16.49 9.73
SVT-AV1 Encoder Mode: Preset 8 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.8 Encoder Mode: Preset 8 - Input: Bosphorus 4K a b c d 14 28 42 56 70 SE +/- 0.07, N = 3 61.53 61.83 61.47 60.95 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 4 - Input: Bosphorus 1080p OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.8 Encoder Mode: Preset 4 - Input: Bosphorus 1080p a b c d 5 10 15 20 25 SE +/- 0.08, N = 3 18.80 18.68 18.41 18.92 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
Y-Cruncher Pi Digits To Calculate: 500M OpenBenchmarking.org Seconds, Fewer Is Better Y-Cruncher 0.8.3 Pi Digits To Calculate: 500M a b c d 2 4 6 8 10 SE +/- 0.007, N = 3 7.325 7.349 7.301 7.297
SVT-AV1 Encoder Mode: Preset 12 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.8 Encoder Mode: Preset 12 - Input: Bosphorus 4K a b c d 40 80 120 160 200 SE +/- 1.08, N = 3 190.92 190.40 192.16 192.68 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 13 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.8 Encoder Mode: Preset 13 - Input: Bosphorus 4K a b c d 40 80 120 160 200 SE +/- 0.80, N = 3 190.79 192.73 189.25 192.60 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 8 - Input: Bosphorus 1080p OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.8 Encoder Mode: Preset 8 - Input: Bosphorus 1080p a b c d 30 60 90 120 150 SE +/- 0.59, N = 3 122.95 123.29 122.95 122.62 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 12 - Input: Bosphorus 1080p OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.8 Encoder Mode: Preset 12 - Input: Bosphorus 1080p a b c d 110 220 330 440 550 SE +/- 5.37, N = 5 501.42 506.60 501.16 506.17 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 13 - Input: Bosphorus 1080p OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.8 Encoder Mode: Preset 13 - Input: Bosphorus 1080p a b c d 130 260 390 520 650 SE +/- 7.15, N = 3 543.55 573.04 580.47 565.91 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
Phoronix Test Suite v10.8.4