5800x3d smoke okt AMD Ryzen 7 5800X3D 8-Core testing with a ASUS ROG CROSSHAIR VIII HERO (4201 BIOS) and Intel DG2 8GB on Ubuntu 22.04 via the Phoronix Test Suite. ,,"A","B","C","D" Processor,,AMD Ryzen 7 5800X3D 8-Core @ 3.40GHz (8 Cores / 16 Threads),AMD Ryzen 7 5800X3D 8-Core @ 3.40GHz (8 Cores / 16 Threads),AMD Ryzen 7 5800X3D 8-Core @ 3.40GHz (8 Cores / 16 Threads),AMD Ryzen 7 5800X3D 8-Core @ 3.40GHz (8 Cores / 16 Threads) Motherboard,,ASUS ROG CROSSHAIR VIII HERO (4201 BIOS),ASUS ROG CROSSHAIR VIII HERO (4201 BIOS),ASUS ROG CROSSHAIR VIII HERO (4201 BIOS),ASUS ROG CROSSHAIR VIII HERO (4201 BIOS) Chipset,,AMD Starship/Matisse,AMD Starship/Matisse,AMD Starship/Matisse,AMD Starship/Matisse Memory,,32GB,32GB,32GB,32GB Disk,,1000GB Western Digital WDS100T1X0E-00AFY0 + 2000GB,1000GB Western Digital WDS100T1X0E-00AFY0 + 2000GB,1000GB Western Digital WDS100T1X0E-00AFY0 + 2000GB,1000GB Western Digital WDS100T1X0E-00AFY0 + 2000GB Graphics,,Intel DG2 8GB (2400MHz),Intel DG2 8GB (2400MHz),Intel DG2 8GB (2400MHz),Intel DG2 8GB (2400MHz) Audio,,Intel Device 4f90,Intel Device 4f90,Intel Device 4f90,Intel Device 4f90 Monitor,,ASUS VP28U,ASUS VP28U,ASUS VP28U,ASUS VP28U Network,,Realtek RTL8125 2.5GbE + Intel I211,Realtek RTL8125 2.5GbE + Intel I211,Realtek RTL8125 2.5GbE + Intel I211,Realtek RTL8125 2.5GbE + Intel I211 OS,,Ubuntu 22.04,Ubuntu 22.04,Ubuntu 22.04,Ubuntu 22.04 Kernel,,5.15.47+prerelease3723 (x86_64),5.15.47+prerelease3723 (x86_64),5.15.47+prerelease3723 (x86_64),5.15.47+prerelease3723 (x86_64) Desktop,,GNOME Shell 42.2,GNOME Shell 42.2,GNOME Shell 42.2,GNOME Shell 42.2 Display Server,,X Server 1.21.1.3 + Wayland,X Server 1.21.1.3 + Wayland,X Server 1.21.1.3 + Wayland,X Server 1.21.1.3 + Wayland OpenGL,,4.6 Mesa 22.2.0-devel (git-44289c46d9),4.6 Mesa 22.2.0-devel (git-44289c46d9),4.6 Mesa 22.2.0-devel (git-44289c46d9),4.6 Mesa 22.2.0-devel (git-44289c46d9) Vulkan,,1.3.219,1.3.219,1.3.219,1.3.219 Compiler,,GCC 11.2.0,GCC 11.2.0,GCC 11.2.0,GCC 11.2.0 File-System,,ext4,ext4,ext4,ext4 Screen Resolution,,3840x2160,3840x2160,3840x2160,3840x2160 ,,"A","B","C","D" "SMHasher - Hash: wyhash (MiB/sec)",HIB,26419.11,26334.38,26302.3,25891.34 "SMHasher - Hash: wyhash (cycles/hash)",LIB,16.251,16.297,16.212,16.75 "SMHasher - Hash: SHA3-256 (MiB/sec)",HIB,191.23,196.15,183.14,194.55 "SMHasher - Hash: SHA3-256 (cycles/hash)",LIB,2033.496,1979.325,2115.646,1988.985 "SMHasher - Hash: Spooky32 (MiB/sec)",HIB,19181.6,19233.26,19034.7,19265.23 "SMHasher - Hash: Spooky32 (cycles/hash)",LIB,31.715,31.651,31.897,31.549 "SMHasher - Hash: fasthash32 (MiB/sec)",HIB,7536.68,7602.61,7573.13,7589.92 "SMHasher - Hash: fasthash32 (cycles/hash)",LIB,25.97,25.731,25.809,25.754 "SMHasher - Hash: FarmHash128 (MiB/sec)",HIB,17295.31,17506.62,17175.32,17426.17 "SMHasher - Hash: FarmHash128 (cycles/hash)",LIB,55.273,55.258,55.247,55.258 "SMHasher - Hash: t1ha2_atonce (MiB/sec)",HIB,19795.33,19752.83,20051.08,19749.24 "SMHasher - Hash: t1ha2_atonce (cycles/hash)",LIB,24.319,24.115,24.093,24.063 "SMHasher - Hash: FarmHash32 x86_64 AVX (MiB/sec)",HIB,33756.46,34042.82,33899.05,34051.8 "SMHasher - Hash: FarmHash32 x86_64 AVX (cycles/hash)",LIB,30.29,30.29,30.322,32.559 "SMHasher - Hash: t1ha0_aes_avx2 x86_64 (MiB/sec)",HIB,77605.07,73947.9,74795.04,75226.23 "SMHasher - Hash: t1ha0_aes_avx2 x86_64 (cycles/hash)",LIB,23.246,23.064,23.087,23.064 "SMHasher - Hash: MeowHash x86_64 AES-NI (MiB/sec)",HIB,45994.36,45752.01,45844.24,45447.31 "SMHasher - Hash: MeowHash x86_64 AES-NI (cycles/hash)",LIB,50.105,50.096,50.105,50.25 "QuadRay - Scene: 1 - Resolution: 4K (FPS)",HIB,8.42,8.43,8.38,8.4 "QuadRay - Scene: 2 - Resolution: 4K (FPS)",HIB,2.3,2.31,2.31,2.3 "QuadRay - Scene: 3 - Resolution: 4K (FPS)",HIB,1.96,1.98,1.97,1.98 "QuadRay - Scene: 5 - Resolution: 4K (FPS)",HIB,0.53,0.53,0.53,0.53 "QuadRay - Scene: 1 - Resolution: 1080p (FPS)",HIB,32.62,32.77,32.66,32.6 "QuadRay - Scene: 2 - Resolution: 1080p (FPS)",HIB,8.8,9,8.9,8.97 "QuadRay - Scene: 3 - Resolution: 1080p (FPS)",HIB,7.7,7.75,7.62,7.67 "QuadRay - Scene: 5 - Resolution: 1080p (FPS)",HIB,2.14,2.14,2.14,2.14 "AOM AV1 - Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 4K (FPS)",HIB,0.21,0.21,0.21,0.21 "AOM AV1 - Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 4K (FPS)",HIB,7.61,7.69,7.66,7.67 "AOM AV1 - Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4K (FPS)",HIB,36.34,36.61,36.36,36.52 "AOM AV1 - Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K (FPS)",HIB,14.43,14.67,14.52,14.53 "AOM AV1 - Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4K (FPS)",HIB,58.95,58.84,58.18,58.14 "AOM AV1 - Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4K (FPS)",HIB,80.78,81.58,81.46,81.04 "AOM AV1 - Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K (FPS)",HIB,84.14,85.07,83.56,84.35 "AOM AV1 - Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 1080p (FPS)",HIB,0.64,0.64,0.64,0.64 "AOM AV1 - Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 1080p (FPS)",HIB,17.71,17.71,17.73,17.59 "AOM AV1 - Encoder Mode: Speed 6 Realtime - Input: Bosphorus 1080p (FPS)",HIB,74.81,73.6,74.15,73.14 "AOM AV1 - Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 1080p (FPS)",HIB,44.51,44.99,44.92,45.05 "AOM AV1 - Encoder Mode: Speed 8 Realtime - Input: Bosphorus 1080p (FPS)",HIB,156.1,155.18,154.69,155.73 "AOM AV1 - Encoder Mode: Speed 9 Realtime - Input: Bosphorus 1080p (FPS)",HIB,189.92,189.67,188.95,189.14 "AOM AV1 - Encoder Mode: Speed 10 Realtime - Input: Bosphorus 1080p (FPS)",HIB,199.11,200.46,194.65,198.99 "Y-Cruncher - Pi Digits To Calculate: 1B (sec)",LIB,39.187,39.031,39.051,39.236 "Y-Cruncher - Pi Digits To Calculate: 500M (sec)",LIB,17.737,17.612,17.696,17.827 "oneDNN - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU (ms)",LIB,2.93636,2.91481,2.91791,2.93008 "oneDNN - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU (ms)",LIB,6.73091,6.80659,7.43205,7.50774 "oneDNN - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1.24936,1.24604,1.24858,1.24944 "oneDNN - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.608646,0.610055,0.610944,0.613447 "oneDNN - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,,, "oneDNN - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,,, "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU (ms)",LIB,12.7863,12.865,13.2371,13.3507 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU (ms)",LIB,7.23318,6.83968,7.37916,7.10311 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU (ms)",LIB,5.61357,5.59992,5.61586,5.61171 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,11.8886,10.6745,11.1844,11.3702 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1.79935,1.7939,1.79785,1.79717 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,2.52002,2.48677,2.54366,2.53624 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU (ms)",LIB,2717.67,2701.46,2711.47,2726.78 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU (ms)",LIB,1395.86,1389.29,1391.77,1393.37 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,2724.5,2715.72,2719.71,2722.9 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,,, "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,,, "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,,, "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1392.31,1385.11,1389.43,1390.76 "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU (ms)",LIB,1.08635,1.0792,1.08253,1.08663 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,2724.19,2707.34,2717.02,2721.31 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,1400.14,1388.94,1393.85,1393.64 "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.847434,0.841664,0.840243,0.844385 "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,,, "TensorFlow - Device: CPU - Batch Size: 16 - Model: VGG-16 (images/sec)",HIB,5.55,5.55,,5.55 "TensorFlow - Device: CPU - Batch Size: 32 - Model: VGG-16 (images/sec)",HIB,5.81,5.82,,5.81 "TensorFlow - Device: CPU - Batch Size: 64 - Model: VGG-16 (images/sec)",HIB,5.94,5.95,,5.93 "TensorFlow - Device: CPU - Batch Size: 16 - Model: AlexNet (images/sec)",HIB,68.32,68.45,,68.16 "TensorFlow - Device: CPU - Batch Size: 256 - Model: VGG-16 (images/sec)",HIB,5.86,,,5.88 "TensorFlow - Device: CPU - Batch Size: 32 - Model: AlexNet (images/sec)",HIB,91.5,,,91.54 "TensorFlow - Device: CPU - Batch Size: 512 - Model: VGG-16 (images/sec)",HIB,,,, "TensorFlow - Device: CPU - Batch Size: 64 - Model: AlexNet (images/sec)",HIB,109.77,,,109.92 "TensorFlow - Device: CPU - Batch Size: 256 - Model: AlexNet (images/sec)",HIB,122.89,,,123.47 "TensorFlow - Device: CPU - Batch Size: 512 - Model: AlexNet (images/sec)",HIB,123.66,,,124.44 "TensorFlow - Device: CPU - Batch Size: 16 - Model: GoogLeNet (images/sec)",HIB,41.31,,,41.39 "TensorFlow - Device: CPU - Batch Size: 16 - Model: ResNet-50 (images/sec)",HIB,14.55,,,14.49 "TensorFlow - Device: CPU - Batch Size: 32 - Model: GoogLeNet (images/sec)",HIB,39.37,,,39.35 "TensorFlow - Device: CPU - Batch Size: 32 - Model: ResNet-50 (images/sec)",HIB,13.53,,,13.58 "TensorFlow - Device: CPU - Batch Size: 64 - Model: GoogLeNet (images/sec)",HIB,37.81,,,37.85 "TensorFlow - Device: CPU - Batch Size: 64 - Model: ResNet-50 (images/sec)",HIB,12.65,,,12.67 "TensorFlow - Device: CPU - Batch Size: 256 - Model: GoogLeNet (images/sec)",HIB,36.26,,,36.38 "TensorFlow - Device: CPU - Batch Size: 256 - Model: ResNet-50 (images/sec)",HIB,11.87,,,11.86 "TensorFlow - Device: CPU - Batch Size: 512 - Model: GoogLeNet (images/sec)",HIB,36.02,,,35.96 "TensorFlow - Device: CPU - Batch Size: 512 - Model: ResNet-50 (images/sec)",HIB,,,, "spaCy - Model: en_core_web_lg (tokens/sec)",HIB,15416,15357,15401,15424 "spaCy - Model: en_core_web_trf (tokens/sec)",HIB,740,748,756,747 "OpenRadioss - Model: Bumper Beam (sec)",LIB,144.69,145.55,149.29,145.41 "OpenRadioss - Model: Cell Phone Drop Test (sec)",LIB,100.35,100.26,100.11,103.13 "OpenRadioss - Model: Bird Strike on Windshield (sec)",LIB,279.3,291.67,279.49,280.05 "OpenRadioss - Model: Rubber O-Ring Seal Installation (sec)",LIB,146.06,146.45,146.6,145.56 "OpenRadioss - Model: INIVOL and Fluid Structure Interaction Drop Container (sec)",LIB,569.41,571.31,568.9,571.12 "PostgreSQL - Scaling Factor: 1 - Clients: 1 - Mode: Read Only (TPS)",HIB,37199,35381,35743,35777 "PostgreSQL - Scaling Factor: 1 - Clients: 1 - Mode: Read Only - Average Latency (ms)",LIB,0.027,0.028,0.028,0.028 "PostgreSQL - Scaling Factor: 1 - Clients: 1 - Mode: Read Write (TPS)",HIB,2507,2488,2486,2484 "PostgreSQL - Scaling Factor: 1 - Clients: 1 - Mode: Read Write - Average Latency (ms)",LIB,0.399,0.402,0.402,0.403 "PostgreSQL - Scaling Factor: 1 - Clients: 50 - Mode: Read Only (TPS)",HIB,310942,311138,308393,314549 "PostgreSQL - Scaling Factor: 1 - Clients: 50 - Mode: Read Only - Average Latency (ms)",LIB,0.161,0.161,0.162,0.159 "PostgreSQL - Scaling Factor: 1 - Clients: 100 - Mode: Read Only (TPS)",HIB,307562,309250,307586,310891 "PostgreSQL - Scaling Factor: 1 - Clients: 100 - Mode: Read Only - Average Latency (ms)",LIB,0.325,0.323,0.325,0.322 "PostgreSQL - Scaling Factor: 1 - Clients: 50 - Mode: Read Write (TPS)",HIB,3127,3131,3138,3133 "PostgreSQL - Scaling Factor: 1 - Clients: 50 - Mode: Read Write - Average Latency (ms)",LIB,15.991,15.971,15.931,15.961 "PostgreSQL - Scaling Factor: 100 - Clients: 1 - Mode: Read Only (TPS)",HIB,34362,33875,34948,35562 "PostgreSQL - Scaling Factor: 100 - Clients: 1 - Mode: Read Only - Average Latency (ms)",LIB,0.029,0.03,0.029,0.028 "PostgreSQL - Scaling Factor: 1 - Clients: 100 - Mode: Read Write (TPS)",HIB,2861,2856,,2873 "PostgreSQL - Scaling Factor: 1 - Clients: 100 - Mode: Read Write - Average Latency (ms)",LIB,34.948,35.019,,34.81 "PostgreSQL - Scaling Factor: 100 - Clients: 1 - Mode: Read Write (TPS)",HIB,2378,2400,,2394 "PostgreSQL - Scaling Factor: 100 - Clients: 1 - Mode: Read Write - Average Latency (ms)",LIB,0.421,0.417,,0.418 "PostgreSQL - Scaling Factor: 100 - Clients: 50 - Mode: Read Only (TPS)",HIB,299777,301135,,305622 "PostgreSQL - Scaling Factor: 100 - Clients: 50 - Mode: Read Only - Average Latency (ms)",LIB,0.167,0.166,,0.164 "PostgreSQL - Scaling Factor: 100 - Clients: 100 - Mode: Read Only (TPS)",HIB,297424,299533,,300952 "PostgreSQL - Scaling Factor: 100 - Clients: 100 - Mode: Read Only - Average Latency (ms)",LIB,0.336,0.334,,0.332 "PostgreSQL - Scaling Factor: 100 - Clients: 50 - Mode: Read Write (TPS)",HIB,38140,37994,,38108 "PostgreSQL - Scaling Factor: 100 - Clients: 50 - Mode: Read Write - Average Latency (ms)",LIB,1.311,1.316,,1.312 "PostgreSQL - Scaling Factor: 100 - Clients: 100 - Mode: Read Write (TPS)",HIB,38997,39365,,39700 "PostgreSQL - Scaling Factor: 100 - Clients: 100 - Mode: Read Write - Average Latency (ms)",LIB,2.564,2.54,,2.519 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,8.29,8.2282,,8.3401 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,482.491,484.9651,,478.2818 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (items/sec)",HIB,8.0673,8.0362,,8.0834 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (ms/batch)",LIB,123.9518,124.431,,123.7043 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,34.1768,34.0455,,34.3275 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,117.0127,117.4646,,116.501 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream (items/sec)",HIB,30.053,30.0241,,29.9986 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,33.2672,33.2992,,33.3273 "Neural Magic DeepSparse - Model: CV Detection,YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,45.9093,46.0804,,46.5056 "Neural Magic DeepSparse - Model: CV Detection,YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,87.1056,86.7801,,85.93 "Neural Magic DeepSparse - Model: CV Detection,YOLOv5s COCO - Scenario: Synchronous Single-Stream (items/sec)",HIB,44.9825,45.0534,,44.9935 "Neural Magic DeepSparse - Model: CV Detection,YOLOv5s COCO - Scenario: Synchronous Single-Stream (ms/batch)",LIB,22.225,22.1902,,22.2191 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,95.0749,95.0701,,95.0642 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,42.059,42.0602,,42.0568 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (items/sec)",HIB,84.572,84.5008,,84.5271 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (ms/batch)",LIB,11.8188,11.829,,11.826 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,71.7306,71.6352,,71.7935 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,55.7492,55.821,,55.6997 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (items/sec)",HIB,60.2563,60.8494,,60.5163 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (ms/batch)",LIB,16.5906,16.4288,,16.5194 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,35.2514,34.9918,,35.2305 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,113.4572,114.2925,,113.5164 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream (items/sec)",HIB,30.1119,30.0779,,30.1595 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,33.2041,33.2415,,33.1516 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,8.3235,8.3116,,8.3717 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,478.9736,480.4444,,476.7667 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (items/sec)",HIB,8.1277,8.0704,,8.0822 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,123.0312,123.9041,,123.7235