AMD EPYC 7763 64-Core testing with a AMD DAYTONA_X (RYM1009B BIOS) and ASPEED on Ubuntu 22.04 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 2308059-NE-77632204529
7763 2204
AMD EPYC 7763 64-Core testing with a AMD DAYTONA_X (RYM1009B BIOS) and ASPEED on Ubuntu 22.04 via the Phoronix Test Suite.
a:
Processor: AMD EPYC 7763 64-Core @ 2.45GHz (64 Cores / 128 Threads), Motherboard: AMD DAYTONA_X (RYM1009B BIOS), Chipset: AMD Starship/Matisse, Memory: 256GB, Disk: 800GB INTEL SSDPF21Q800GB, Graphics: ASPEED, Monitor: VE228, Network: 2 x Mellanox MT27710
OS: Ubuntu 22.04, Kernel: 6.2.0-phx (x86_64), Desktop: GNOME Shell 42.5, Display Server: X Server 1.21.1.3, Vulkan: 1.3.224, Compiler: GCC 11.3.0 + LLVM 14.0.0, File-System: ext4, Screen Resolution: 1920x1080
b:
Processor: AMD EPYC 7763 64-Core @ 2.45GHz (64 Cores / 128 Threads), Motherboard: AMD DAYTONA_X (RYM1009B BIOS), Chipset: AMD Starship/Matisse, Memory: 256GB, Disk: 800GB INTEL SSDPF21Q800GB, Graphics: ASPEED, Monitor: VE228, Network: 2 x Mellanox MT27710
OS: Ubuntu 22.04, Kernel: 6.2.0-phx (x86_64), Desktop: GNOME Shell 42.5, Display Server: X Server 1.21.1.3, Vulkan: 1.3.224, Compiler: GCC 11.3.0 + LLVM 14.0.0, File-System: ext4, Screen Resolution: 1920x1080
c:
Processor: AMD EPYC 7763 64-Core @ 2.45GHz (64 Cores / 128 Threads), Motherboard: AMD DAYTONA_X (RYM1009B BIOS), Chipset: AMD Starship/Matisse, Memory: 256GB, Disk: 800GB INTEL SSDPF21Q800GB, Graphics: ASPEED, Monitor: VE228, Network: 2 x Mellanox MT27710
OS: Ubuntu 22.04, Kernel: 6.2.0-phx (x86_64), Desktop: GNOME Shell 42.5, Display Server: X Server 1.21.1.3, Vulkan: 1.3.224, Compiler: GCC 11.3.0 + LLVM 14.0.0, File-System: ext4, Screen Resolution: 1920x1080
Apache Cassandra 4.1.3
Test: Writes
Op/s > Higher Is Better
a . 236650 |===================================================================
b . 238161 |===================================================================
c . 234887 |==================================================================
Apache CouchDB 3.3.2
Bulk Size: 100 - Inserts: 1000 - Rounds: 30
Seconds < Lower Is Better
a . 101.58 |===================================================================
Apache CouchDB 3.3.2
Bulk Size: 100 - Inserts: 3000 - Rounds: 30
Seconds < Lower Is Better
a . 346.09 |===================================================================
Apache CouchDB 3.3.2
Bulk Size: 300 - Inserts: 1000 - Rounds: 30
Seconds < Lower Is Better
a . 169.51 |===================================================================
Apache CouchDB 3.3.2
Bulk Size: 300 - Inserts: 3000 - Rounds: 30
Seconds < Lower Is Better
a . 572.13 |===================================================================
Apache CouchDB 3.3.2
Bulk Size: 500 - Inserts: 1000 - Rounds: 30
Seconds < Lower Is Better
a . 339.97 |===================================================================
Apache CouchDB 3.3.2
Bulk Size: 500 - Inserts: 3000 - Rounds: 30
Seconds < Lower Is Better
a . 2390.93 |==================================================================
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200
point/sec > Higher Is Better
a . 644019.72 |==============================================================
b . 648308.27 |==============================================================
c . 667880.96 |================================================================
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200
Average Latency > Higher Is Better
a . 17.45 |====================================================================
b . 17.28 |===================================================================
c . 16.35 |================================================================
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500
point/sec > Higher Is Better
a . 1038515.62 |=============================================================
b . 1069145.79 |===============================================================
c . 1044153.44 |==============================================================
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500
Average Latency > Higher Is Better
a . 34.36 |====================================================================
b . 32.92 |=================================================================
c . 34.08 |===================================================================
Apache IoTDB 1.1.2
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200
point/sec > Higher Is Better
a . 898967.08 |===========================================================
b . 978176.76 |================================================================
c . 870795.92 |=========================================================
Apache IoTDB 1.1.2
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200
Average Latency > Higher Is Better
a . 15.24 |================================================================
b . 13.60 |==========================================================
c . 16.07 |====================================================================
Apache IoTDB 1.1.2
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500
point/sec > Higher Is Better
a . 1232509.19 |==============================================================
b . 1226219.88 |=============================================================
c . 1261385.89 |===============================================================
Apache IoTDB 1.1.2
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500
Average Latency > Higher Is Better
a . 33.50 |===================================================================
b . 33.86 |====================================================================
c . 32.71 |==================================================================
Apache IoTDB 1.1.2
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200
point/sec > Higher Is Better
a . 1182440.62 |======================================================
b . 1365831.50 |===============================================================
c . 1367763.49 |===============================================================
Apache IoTDB 1.1.2
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200
Average Latency > Higher Is Better
a . 13.54 |====================================================================
b . 11.63 |==========================================================
c . 11.83 |===========================================================
Apache IoTDB 1.1.2
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500
point/sec > Higher Is Better
a . 1636128.73 |=============================================================
b . 1686943.16 |===============================================================
c . 1446487.70 |======================================================
Apache IoTDB 1.1.2
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500
Average Latency > Higher Is Better
a . 27.1 |============================================================
b . 26.0 |=========================================================
c . 31.4 |=====================================================================
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200
point/sec > Higher Is Better
a . 39287432.92 |=============================================================
b . 38401769.17 |============================================================
c . 39945212.99 |==============================================================
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200
Average Latency > Higher Is Better
a . 36.04 |===================================================================
b . 36.85 |====================================================================
c . 35.01 |=================================================================
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500
point/sec > Higher Is Better
a . 51316464.44 |=============================================================
b . 50507747.12 |============================================================
c . 52464142.83 |==============================================================
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500
Average Latency > Higher Is Better
a . 81.20 |===================================================================
b . 82.26 |====================================================================
c . 79.16 |=================================================================
Apache IoTDB 1.1.2
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 200
point/sec > Higher Is Better
a . 46437377.67 |=============================================================
b . 47245476.78 |==============================================================
c . 46674344.69 |=============================================================
Apache IoTDB 1.1.2
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 200
Average Latency > Higher Is Better
a . 35.09 |====================================================================
b . 33.84 |==================================================================
c . 34.79 |===================================================================
Apache IoTDB 1.1.2
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 500
point/sec > Higher Is Better
a . 42048733.22 |============================================================
b . 41987111.39 |============================================================
c . 43363203.76 |==============================================================
Apache IoTDB 1.1.2
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 500
Average Latency > Higher Is Better
a . 109.38 |==================================================================
b . 110.88 |===================================================================
c . 106.73 |================================================================
Apache IoTDB 1.1.2
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200
point/sec > Higher Is Better
a . 51341708.85 |==============================================================
b . 50045888.98 |============================================================
c . 49201448.81 |===========================================================
Apache IoTDB 1.1.2
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200
Average Latency > Higher Is Better
a . 35.05 |================================================================
b . 36.10 |==================================================================
c . 37.12 |====================================================================
Apache IoTDB 1.1.2
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500
point/sec > Higher Is Better
a . 56935634.55 |===========================================================
b . 59505306.55 |==============================================================
c . 56463717.54 |===========================================================
Apache IoTDB 1.1.2
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500
Average Latency > Higher Is Better
a . 81.81 |===================================================================
b . 79.83 |=================================================================
c . 83.14 |====================================================================
Blender 3.6
Blend File: BMW27 - Compute: CPU-Only
Seconds < Lower Is Better
a . 27.27 |===================================================================
b . 27.50 |====================================================================
c . 27.24 |===================================================================
Blender 3.6
Blend File: Classroom - Compute: CPU-Only
Seconds < Lower Is Better
a . 68.80 |====================================================================
b . 68.50 |====================================================================
c . 68.70 |====================================================================
Blender 3.6
Blend File: Fishy Cat - Compute: CPU-Only
Seconds < Lower Is Better
a . 33.70 |====================================================================
b . 33.76 |====================================================================
c . 33.72 |====================================================================
Blender 3.6
Blend File: Barbershop - Compute: CPU-Only
Seconds < Lower Is Better
a . 253.49 |===================================================================
b . 253.77 |===================================================================
c . 253.43 |===================================================================
Blender 3.6
Blend File: Pabellon Barcelona - Compute: CPU-Only
Seconds < Lower Is Better
a . 84.55 |====================================================================
b . 84.35 |====================================================================
c . 84.17 |====================================================================
BRL-CAD 7.36
VGR Performance Metric
VGR Performance Metric > Higher Is Better
a . 734386 |===================================================================
b . 729876 |===================================================================
c . 730434 |===================================================================
NCNN 20230517
Target: CPU - Model: mobilenet
ms < Lower Is Better
a . 14.11 |====================================================================
b . 13.97 |===================================================================
c . 14.03 |====================================================================
NCNN 20230517
Target: CPU-v2-v2 - Model: mobilenet-v2
ms < Lower Is Better
a . 6.35 |=====================================================================
b . 6.26 |====================================================================
c . 6.17 |===================================================================
NCNN 20230517
Target: CPU-v3-v3 - Model: mobilenet-v3
ms < Lower Is Better
a . 7.00 |=====================================================================
b . 6.55 |=================================================================
c . 6.34 |==============================================================
NCNN 20230517
Target: CPU - Model: shufflenet-v2
ms < Lower Is Better
a . 9.09 |=====================================================================
b . 7.93 |============================================================
c . 7.60 |==========================================================
NCNN 20230517
Target: CPU - Model: mnasnet
ms < Lower Is Better
a . 6.09 |=====================================================================
b . 5.90 |===================================================================
c . 5.86 |==================================================================
NCNN 20230517
Target: CPU - Model: efficientnet-b0
ms < Lower Is Better
a . 9.98 |=====================================================================
b . 9.78 |====================================================================
c . 9.75 |===================================================================
NCNN 20230517
Target: CPU - Model: blazeface
ms < Lower Is Better
a . 3.97 |=====================================================================
b . 3.43 |============================================================
c . 3.48 |============================================================
NCNN 20230517
Target: CPU - Model: googlenet
ms < Lower Is Better
a . 14.62 |====================================================================
b . 14.53 |====================================================================
c . 14.47 |===================================================================
NCNN 20230517
Target: CPU - Model: vgg16
ms < Lower Is Better
a . 23.84 |====================================================================
b . 23.64 |===================================================================
c . 23.91 |====================================================================
NCNN 20230517
Target: CPU - Model: resnet18
ms < Lower Is Better
a . 8.50 |=====================================================================
b . 8.42 |====================================================================
c . 8.51 |=====================================================================
NCNN 20230517
Target: CPU - Model: alexnet
ms < Lower Is Better
a . 5.23 |=====================================================================
b . 5.22 |=====================================================================
c . 5.22 |=====================================================================
NCNN 20230517
Target: CPU - Model: resnet50
ms < Lower Is Better
a . 15.49 |====================================================================
b . 15.34 |===================================================================
c . 15.54 |====================================================================
NCNN 20230517
Target: CPU - Model: yolov4-tiny
ms < Lower Is Better
a . 20.66 |====================================================================
b . 20.51 |===================================================================
c . 20.80 |====================================================================
NCNN 20230517
Target: CPU - Model: squeezenet_ssd
ms < Lower Is Better
a . 14.17 |==================================================================
b . 14.11 |==================================================================
c . 14.59 |====================================================================
NCNN 20230517
Target: CPU - Model: regnety_400m
ms < Lower Is Better
a . 35.24 |====================================================================
b . 27.54 |=====================================================
c . 27.59 |=====================================================
NCNN 20230517
Target: CPU - Model: vision_transformer
ms < Lower Is Better
a . 48.79 |====================================================================
b . 48.49 |====================================================================
c . 48.43 |===================================================================
NCNN 20230517
Target: CPU - Model: FastestDet
ms < Lower Is Better
a . 10.25 |====================================================================
b . 9.04 |============================================================
c . 8.88 |===========================================================
Neural Magic DeepSparse 1.5
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 37.61 |====================================================================
b . 37.53 |====================================================================
c . 37.58 |====================================================================
Neural Magic DeepSparse 1.5
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 840.54 |===================================================================
b . 840.95 |===================================================================
c . 840.12 |===================================================================
Neural Magic DeepSparse 1.5
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
a . 19.95 |====================================================================
b . 20.03 |====================================================================
c . 20.02 |====================================================================
Neural Magic DeepSparse 1.5
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
a . 50.13 |====================================================================
b . 49.92 |====================================================================
c . 49.93 |====================================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 1105.38 |==================================================================
b . 1104.04 |==================================================================
c . 1103.36 |==================================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 28.92 |====================================================================
b . 28.95 |====================================================================
c . 28.96 |====================================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
a . 173.95 |===================================================================
b . 172.75 |===================================================================
c . 172.07 |==================================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
a . 5.7470 |==================================================================
b . 5.7863 |===================================================================
c . 5.8092 |===================================================================
Neural Magic DeepSparse 1.5
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 489.77 |===================================================================
b . 486.17 |===================================================================
c . 482.13 |==================================================================
Neural Magic DeepSparse 1.5
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 65.28 |===================================================================
b . 65.74 |===================================================================
c . 66.28 |====================================================================
Neural Magic DeepSparse 1.5
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
a . 86.61 |====================================================================
b . 86.39 |====================================================================
c . 86.32 |====================================================================
Neural Magic DeepSparse 1.5
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
a . 11.54 |====================================================================
b . 11.57 |====================================================================
c . 11.58 |====================================================================
Neural Magic DeepSparse 1.5
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 143.07 |===================================================================
b . 143.21 |===================================================================
c . 143.57 |===================================================================
Neural Magic DeepSparse 1.5
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 223.47 |===================================================================
b . 223.37 |===================================================================
c . 222.82 |===================================================================
Neural Magic DeepSparse 1.5
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
a . 39.42 |====================================================================
b . 39.56 |====================================================================
c . 39.51 |====================================================================
Neural Magic DeepSparse 1.5
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
a . 25.36 |====================================================================
b . 25.27 |====================================================================
c . 25.30 |====================================================================
Neural Magic DeepSparse 1.5
Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 468.14 |===================================================================
b . 467.97 |===================================================================
c . 468.33 |===================================================================
Neural Magic DeepSparse 1.5
Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 68.29 |====================================================================
b . 68.28 |====================================================================
c . 68.25 |====================================================================
Neural Magic DeepSparse 1.5
Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
a . 159.85 |===================================================================
b . 159.75 |===================================================================
c . 160.59 |===================================================================
Neural Magic DeepSparse 1.5
Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
a . 6.2525 |===================================================================
b . 6.2566 |===================================================================
c . 6.2238 |===================================================================
Neural Magic DeepSparse 1.5
Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 3814.52 |==================================================================
b . 3824.30 |==================================================================
c . 3823.08 |==================================================================
Neural Magic DeepSparse 1.5
Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 8.3665 |===================================================================
b . 8.3441 |===================================================================
c . 8.3468 |===================================================================
Neural Magic DeepSparse 1.5
Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
a . 723.71 |==================================================================
b . 732.11 |===================================================================
c . 731.51 |===================================================================
Neural Magic DeepSparse 1.5
Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
a . 1.3784 |===================================================================
b . 1.3623 |==================================================================
c . 1.3634 |==================================================================
Neural Magic DeepSparse 1.5
Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 225.31 |===================================================================
b . 225.46 |===================================================================
c . 225.80 |===================================================================
Neural Magic DeepSparse 1.5
Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 141.70 |===================================================================
b . 141.62 |===================================================================
c . 141.48 |===================================================================
Neural Magic DeepSparse 1.5
Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
a . 119.80 |===================================================================
b . 119.88 |===================================================================
c . 119.98 |===================================================================
Neural Magic DeepSparse 1.5
Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
a . 8.3423 |===================================================================
b . 8.3368 |===================================================================
c . 8.3300 |===================================================================
Neural Magic DeepSparse 1.5
Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 46.72 |====================================================================
b . 46.57 |====================================================================
c . 46.90 |====================================================================
Neural Magic DeepSparse 1.5
Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 681.28 |===================================================================
b . 679.82 |===================================================================
c . 679.81 |===================================================================
Neural Magic DeepSparse 1.5
Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
a . 24.44 |====================================================================
b . 24.57 |====================================================================
c . 24.53 |====================================================================
Neural Magic DeepSparse 1.5
Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
a . 40.90 |====================================================================
b . 40.70 |====================================================================
c . 40.76 |====================================================================
Neural Magic DeepSparse 1.5
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 467.98 |===================================================================
b . 468.11 |===================================================================
c . 467.64 |===================================================================
Neural Magic DeepSparse 1.5
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 68.32 |====================================================================
b . 68.27 |====================================================================
c . 68.34 |====================================================================
Neural Magic DeepSparse 1.5
Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
a . 159.97 |===================================================================
b . 159.92 |===================================================================
c . 160.70 |===================================================================
Neural Magic DeepSparse 1.5
Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
a . 6.2481 |===================================================================
b . 6.2495 |===================================================================
c . 6.2195 |===================================================================
Neural Magic DeepSparse 1.5
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 227.42 |===================================================================
b . 227.64 |===================================================================
c . 227.57 |===================================================================
Neural Magic DeepSparse 1.5
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 140.40 |===================================================================
b . 140.26 |===================================================================
c . 140.30 |===================================================================
Neural Magic DeepSparse 1.5
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
a . 120.36 |===================================================================
b . 120.18 |===================================================================
c . 120.58 |===================================================================
Neural Magic DeepSparse 1.5
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
a . 8.3056 |===================================================================
b . 8.3182 |===================================================================
c . 8.2903 |===================================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 326.31 |===================================================================
b . 326.54 |===================================================================
c . 326.41 |===================================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 97.87 |====================================================================
b . 97.84 |====================================================================
c . 97.87 |====================================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
a . 97.36 |====================================================================
b . 97.61 |====================================================================
c . 96.88 |===================================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
a . 10.26 |====================================================================
b . 10.24 |====================================================================
c . 10.31 |====================================================================
Neural Magic DeepSparse 1.5
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 53.48 |====================================================================
b . 53.55 |====================================================================
c . 53.62 |====================================================================
Neural Magic DeepSparse 1.5
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 597.97 |===================================================================
b . 596.81 |===================================================================
c . 596.53 |===================================================================
Neural Magic DeepSparse 1.5
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
a . 28.63 |====================================================================
b . 28.63 |====================================================================
c . 28.64 |====================================================================
Neural Magic DeepSparse 1.5
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
a . 34.91 |====================================================================
b . 34.91 |====================================================================
c . 34.90 |====================================================================
Neural Magic DeepSparse 1.5
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 574.96 |===================================================================
b . 575.29 |===================================================================
c . 575.12 |===================================================================
Neural Magic DeepSparse 1.5
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 55.58 |====================================================================
b . 55.56 |====================================================================
c . 55.58 |====================================================================
Neural Magic DeepSparse 1.5
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
a . 94.51 |====================================================================
b . 94.04 |====================================================================
c . 94.55 |====================================================================
Neural Magic DeepSparse 1.5
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
a . 10.58 |====================================================================
b . 10.63 |====================================================================
c . 10.57 |====================================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 166.00 |===================================================================
b . 166.22 |===================================================================
c . 166.06 |===================================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 192.35 |===================================================================
b . 192.16 |===================================================================
c . 192.21 |===================================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
a . 53.78 |====================================================================
b . 53.70 |====================================================================
c . 53.95 |====================================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
a . 18.59 |====================================================================
b . 18.61 |====================================================================
c . 18.53 |====================================================================
Neural Magic DeepSparse 1.5
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 37.61 |====================================================================
b . 37.54 |====================================================================
c . 37.58 |====================================================================
Neural Magic DeepSparse 1.5
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 841.48 |===================================================================
b . 840.26 |===================================================================
c . 840.44 |===================================================================
Neural Magic DeepSparse 1.5
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
a . 20.07 |====================================================================
b . 20.03 |====================================================================
c . 20.05 |====================================================================
Neural Magic DeepSparse 1.5
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
a . 49.81 |====================================================================
b . 49.91 |====================================================================
c . 49.86 |====================================================================
srsRAN Project 23.5
Test: Downlink Processor Benchmark
Mbps > Higher Is Better
a . 657.7 |====================================================================
b . 658.1 |====================================================================
c . 619.3 |================================================================
srsRAN Project 23.5
Test: PUSCH Processor Benchmark, Throughput Total
Mbps > Higher Is Better
a . 9682.1 |===================================================================
b . 9718.6 |===================================================================
c . 9727.1 |===================================================================
srsRAN Project 23.5
Test: PUSCH Processor Benchmark, Throughput Thread
Mbps > Higher Is Better
a . 211.1 |====================================================================
b . 208.2 |===================================================================
c . 210.8 |====================================================================
Timed GCC Compilation 13.2
Time To Compile
Seconds < Lower Is Better
a . 1020.13 |==================================================================
b . 1020.85 |==================================================================
c . 1020.22 |==================================================================
VVenC 1.9
Video Input: Bosphorus 4K - Video Preset: Fast
Frames Per Second > Higher Is Better
a . 5.991 |====================================================================
b . 5.993 |====================================================================
c . 5.976 |====================================================================
VVenC 1.9
Video Input: Bosphorus 4K - Video Preset: Faster
Frames Per Second > Higher Is Better
a . 10.65 |===================================================================
b . 10.82 |====================================================================
c . 10.82 |====================================================================
VVenC 1.9
Video Input: Bosphorus 1080p - Video Preset: Fast
Frames Per Second > Higher Is Better
a . 16.08 |====================================================================
b . 16.09 |====================================================================
c . 16.06 |====================================================================
VVenC 1.9
Video Input: Bosphorus 1080p - Video Preset: Faster
Frames Per Second > Higher Is Better
a . 29.35 |====================================================================
b . 29.39 |====================================================================
c . 29.47 |====================================================================