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.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2402040-NE-2024YEAR116
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February 03
  1 Hour, 38 Minutes
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February 04
  4 Hours, 50 Minutes
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February 04
  1 Hour, 37 Minutes
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February 04
  1 Hour, 34 Minutes
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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. ,,"a","b","c","d" Processor,,AMD Ryzen Threadripper PRO 5965WX 24-Cores @ 3.80GHz (24 Cores / 48 Threads),AMD Ryzen Threadripper PRO 5965WX 24-Cores @ 3.80GHz (24 Cores / 48 Threads),AMD Ryzen Threadripper PRO 5965WX 24-Cores @ 3.80GHz (24 Cores / 48 Threads),AMD Ryzen Threadripper PRO 5965WX 24-Cores @ 3.80GHz (24 Cores / 48 Threads) Motherboard,,ASUS Pro WS WRX80E-SAGE SE WIFI (1201 BIOS),ASUS Pro WS WRX80E-SAGE SE WIFI (1201 BIOS),ASUS Pro WS WRX80E-SAGE SE WIFI (1201 BIOS),ASUS Pro WS WRX80E-SAGE SE WIFI (1201 BIOS) Chipset,,AMD Starship/Matisse,AMD Starship/Matisse,AMD Starship/Matisse,AMD Starship/Matisse Memory,,8 x 16GB DDR4-2133MT/s Corsair CMK32GX4M2E3200C16,8 x 16GB DDR4-2133MT/s Corsair CMK32GX4M2E3200C16,8 x 16GB DDR4-2133MT/s Corsair CMK32GX4M2E3200C16,8 x 16GB DDR4-2133MT/s Corsair CMK32GX4M2E3200C16 Disk,,2048GB SOLIDIGM SSDPFKKW020X7,2048GB SOLIDIGM SSDPFKKW020X7,2048GB SOLIDIGM SSDPFKKW020X7,2048GB SOLIDIGM SSDPFKKW020X7 Graphics,,ASUS NVIDIA NV106 2GB,ASUS NVIDIA NV106 2GB,ASUS NVIDIA NV106 2GB,ASUS NVIDIA NV106 2GB Audio,,AMD Starship/Matisse,AMD Starship/Matisse,AMD Starship/Matisse,AMD Starship/Matisse Monitor,,VA2431,VA2431,VA2431,VA2431 Network,,2 x Intel X550 + Intel Wi-Fi 6 AX200,2 x Intel X550 + Intel Wi-Fi 6 AX200,2 x Intel X550 + Intel Wi-Fi 6 AX200,2 x Intel X550 + Intel Wi-Fi 6 AX200 OS,,Ubuntu 23.10,Ubuntu 23.10,Ubuntu 23.10,Ubuntu 23.10 Kernel,,6.5.0-13-generic (x86_64),6.5.0-13-generic (x86_64),6.5.0-13-generic (x86_64),6.5.0-13-generic (x86_64) Desktop,,GNOME Shell 45.0,GNOME Shell 45.0,GNOME Shell 45.0,GNOME Shell 45.0 Display Server,,X Server + Wayland,X Server + Wayland,X Server + Wayland,X Server + Wayland Display Driver,,nouveau,nouveau,nouveau,nouveau OpenGL,,4.3 Mesa 23.2.1-1ubuntu3,4.3 Mesa 23.2.1-1ubuntu3,4.3 Mesa 23.2.1-1ubuntu3,4.3 Mesa 23.2.1-1ubuntu3 Compiler,,GCC 13.2.0,GCC 13.2.0,GCC 13.2.0,GCC 13.2.0 File-System,,ext4,ext4,ext4,ext4 Screen Resolution,,1920x1080,1920x1080,1920x1080,1920x1080 ,,"a","b","c","d" "LeelaChessZero - Backend: BLAS (Nodes/s)",HIB,173,219,225,213 "LeelaChessZero - Backend: Eigen (Nodes/s)",HIB,121,146,154,151 "Quicksilver - Input: CTS2 (Figure Of Merit)",HIB,20680000,20646667,20620000,20600000 "Quicksilver - Input: CORAL2 P1 (Figure Of Merit)",HIB,24210000,24230000,24240000,24290000 "Quicksilver - Input: CORAL2 P2 (Figure Of Merit)",HIB,24030000,24026667,23890000,23840000 "CacheBench - Test: Read (MB/s)",HIB,11543.372321,11543.164687,11543.096362,11543.486939 "CacheBench - Test: Write (MB/s)",HIB,69134.680498,69140.530992,69142.435503,69142.188854 "CacheBench - Test: Read / Modify / Write (MB/s)",HIB,130857.577562,130069.848338,130806.245683,130851.30507 "LZ4 Compression - Compression Level: 1 - Compression Speed (MB/s)",HIB,828.78,829.36,829.15,830.37 "LZ4 Compression - Compression Level: 1 - Decompression Speed (MB/s)",HIB,5019.5,5020.0,5023.2,5019.5 "LZ4 Compression - Compression Level: 3 - Compression Speed (MB/s)",HIB,131.24,131.10,131.4,131.61 "LZ4 Compression - Compression Level: 3 - Decompression Speed (MB/s)",HIB,4595.9,4597.9,4598,4596.7 "LZ4 Compression - Compression Level: 9 - Compression Speed (MB/s)",HIB,44.28,44.48,45.49,44.52 "LZ4 Compression - Compression Level: 9 - Decompression Speed (MB/s)",HIB,4840.5,4841.4,4842.4,4844.5 "rav1e - Speed: 1 (FPS)",HIB,1.044,1.048,1.044,1.054 "rav1e - Speed: 5 (FPS)",HIB,3.747,3.791,3.769,3.891 "rav1e - Speed: 6 (FPS)",HIB,5.261,5.292,5.282,5.191 "rav1e - Speed: 10 (FPS)",HIB,10.634,10.885,10.957,11.022 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 4K (FPS)",HIB,6.677,6.669,6.678,6.633 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 4K (FPS)",HIB,61.53,61.830,61.47,60.95 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 4K (FPS)",HIB,190.916,190.402,192.157,192.683 "SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 4K (FPS)",HIB,190.794,192.726,189.252,192.603 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 1080p (FPS)",HIB,18.803,18.682,18.409,18.922 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 1080p (FPS)",HIB,122.948,123.293,122.95,122.616 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 1080p (FPS)",HIB,501.42,506.596,501.156,506.174 "SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 1080p (FPS)",HIB,543.554,573.042,580.467,565.906 "Y-Cruncher - Pi Digits To Calculate: 1B (sec)",LIB,15.545,15.497,15.532,15.501 "Y-Cruncher - Pi Digits To Calculate: 500M (sec)",LIB,7.325,7.349,7.301,7.297 "PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-50 (batches/sec)",HIB,40.68,40.42,40.82,40.35 "PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-50 (batches/sec)",HIB,32.50,32.10,31.65,32.21 "PyTorch - Device: CPU - Batch Size: 256 - Model: ResNet-50 (batches/sec)",HIB,31.92,32.15,31.59,32.07 "TensorFlow - Device: CPU - Batch Size: 1 - Model: VGG-16 (images/sec)",HIB,2.72,2.70,2.72,2.69 "TensorFlow - Device: CPU - Batch Size: 1 - Model: AlexNet (images/sec)",HIB,6.26,6.23,6.23,6.21 "TensorFlow - Device: CPU - Batch Size: 16 - Model: VGG-16 (images/sec)",HIB,8.51,8.46,8.48,8.51 "TensorFlow - Device: CPU - Batch Size: 16 - Model: AlexNet (images/sec)",HIB,100.44,100.01,100.08,99.9 "TensorFlow - Device: CPU - Batch Size: 1 - Model: GoogLeNet (images/sec)",HIB,9.94,9.74,16.49,9.73 "TensorFlow - Device: CPU - Batch Size: 1 - Model: ResNet-50 (images/sec)",HIB,8.85,8.79,8.85,8.89 "TensorFlow - Device: CPU - Batch Size: 16 - Model: GoogLeNet (images/sec)",HIB,60.85,60.18,60.04,59.82 "TensorFlow - Device: CPU - Batch Size: 16 - Model: ResNet-50 (images/sec)",HIB,19.87,19.45,19.61,19.81 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,26.8394,26.6478,26.6741,26.6902 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,446.6313,448.9079,449.2956,449.1605 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (items/sec)",HIB,18.3872,18.3317,18.3589,18.3959 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (ms/batch)",LIB,54.3716,54.5365,54.4554,54.3471 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,685.1217,681.0038,683.2461,681.5303 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,17.495,17.5999,17.5377,17.5865 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,189.9093,193.1106,192.9801,189.2673 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,5.2624,5.1753,5.1787,5.2804 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,307.0146,304.7712,306.5028,305.8614 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,39.0436,39.3339,39.1233,39.1902 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream (items/sec)",HIB,157.2286,155.4837,155.403,155.2016 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream (ms/batch)",LIB,6.3518,6.4230,6.4266,6.4345 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,2012.2062,1962.7047,1970.6106,1962.1551 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,5.9507,6.1010,6.0747,6.1024 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,757.1631,752.7784,765.9923,757.2715 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,1.3175,1.3252,1.3025,1.3175 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,150.0421,148.2388,148.7561,149.2683 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,79.8855,80.8465,80.5729,80.2684 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (items/sec)",HIB,98.516,98.5635,98.6526,98.7671 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (ms/batch)",LIB,10.1424,10.1362,10.1277,10.1151 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,32.5188,32.3621,32.4212,32.3762 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,368.3051,369.7720,369.6228,369.53 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream (items/sec)",HIB,17.1616,17.1337,17.2376,17.1162 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream (ms/batch)",LIB,58.254,58.3490,57.9978,58.4094 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,306.9919,305.3795,306.5175,299.8295 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,39.0493,39.2555,39.1181,39.977 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (items/sec)",HIB,156.9701,155.5402,155.6182,155.1926 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (ms/batch)",LIB,6.3619,6.4208,6.4178,6.4346 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,151.4666,149.7818,149.9507,149.7647 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,79.1618,80.0470,79.8946,80.052 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,98.9102,98.7456,98.4979,98.8912 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,10.1046,10.1213,10.146,10.1068 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,224.1802,221.7053,223.0515,222.7232 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,53.4699,54.0612,53.7417,53.8237 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (items/sec)",HIB,112.0914,111.2892,111.5864,111.4965 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (ms/batch)",LIB,8.9109,8.9755,8.952,8.9586 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,30.489,30.2809,30.451,30.3159 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,392.9588,394.7759,393.8346,394.3756 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (items/sec)",HIB,21.7296,21.4743,21.6853,21.6655 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (ms/batch)",LIB,45.9976,46.5453,46.0912,46.1334 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,335.3479,333.3674,333.6358,334.6424 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,35.762,35.9726,35.9278,35.837 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,76.1394,75.5067,75.9898,75.7073 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,13.129,13.2388,13.1545,13.204 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,26.9119,26.6672,26.705,26.5435 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,445.2565,448.0408,448.9031,448.0349 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (items/sec)",HIB,18.4727,18.4000,18.4635,18.4443 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,54.1194,54.3340,54.1474,54.204 "Speedb - Test: Random Fill (Op/s)",HIB,558330,554997,557348,556675 "Speedb - Test: Random Read (Op/s)",HIB,148134848,147432214,146285738,146473036 "Speedb - Test: Update Random (Op/s)",HIB,431692,418848,423788,417457 "Speedb - Test: Sequential Fill (Op/s)",HIB,620607,618776,604758,612428 "Speedb - Test: Random Fill Sync (Op/s)",HIB,47488,47708,47373,47267 "Speedb - Test: Read While Writing (Op/s)",HIB,7004007,7070407,7047502,6896007 "Speedb - Test: Read Random Write Random (Op/s)",HIB,2327911,2307686,2320670,2316804 "Llama.cpp - Model: llama-2-7b.Q4_0.gguf (Tokens/sec)",HIB,20.76,20.95,20.74,20.68 "Llama.cpp - Model: llama-2-13b.Q4_0.gguf (Tokens/sec)",HIB,11.64,11.32,11.27,11.25 "Llama.cpp - Model: llama-2-70b-chat.Q5_0.gguf (Tokens/sec)",HIB,1.94,1.94,1.95,1.95 "Llamafile - Test: llava-v1.5-7b-q4 - Acceleration: CPU (Tokens/sec)",HIB,17.22,17.26,17.3,17.25 "Llamafile - Test: mistral-7b-instruct-v0.2.Q8_0 - Acceleration: CPU (Tokens/sec)",HIB,10.13,10.15,10.14,10.13 "Llamafile - Test: wizardcoder-python-34b-v1.0.Q6_K - Acceleration: CPU (Tokens/sec)",HIB,3.25,3.25,3.25,3.25