Intel Xeon Platinum 8490H testing with a Quanta Cloud S6Q-MB-MPS (3A10.uh 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 2307296-NE-8490H1S1663
8490h 1s,
"Apache Cassandra 4.1.3 - Test: Writes",
Higher Results Are Better
"a",
"b",
"c",
"d",
"e",
"Blender 3.6 - Blend File: BMW27 - Compute: CPU-Only",
Lower Results Are Better
"a",
"b",
"c",
"d",
"e",
"Blender 3.6 - Blend File: Classroom - Compute: CPU-Only",
Lower Results Are Better
"a",
"b",
"c",
"d",
"e",
"Blender 3.6 - Blend File: Fishy Cat - Compute: CPU-Only",
Lower Results Are Better
"a",
"b",
"c",
"d",
"e",
"Blender 3.6 - Blend File: Barbershop - Compute: CPU-Only",
Lower Results Are Better
"a",
"b",
"c",
"d",
"e",
"Blender 3.6 - Blend File: Pabellon Barcelona - Compute: CPU-Only",
Lower Results Are Better
"a",
"b",
"c",
"d",
"e",
"BRL-CAD 7.36 - VGR Performance Metric",
Higher Results Are Better
"a",
"b",
"c",
"d",
"e",
"Crypto++ 8.8 - Test: All Algorithms",
Higher Results Are Better
"a",
"Crypto++ 8.8 - Test: Keyed Algorithms",
Higher Results Are Better
"a",
"Crypto++ 8.8 - Test: Unkeyed Algorithms",
Higher Results Are Better
"a",
"Dragonflydb 1.6.2 - Clients Per Thread: 10 - Set To Get Ratio: 1:5",
Higher Results Are Better
"a",
"b",14334901.06,14553637.61,14541963.64
"c",
"d",
"e",
"Dragonflydb 1.6.2 - Clients Per Thread: 20 - Set To Get Ratio: 1:5",
"a",
"b",
"c",
"d",
"e",
"Dragonflydb 1.6.2 - Clients Per Thread: 10 - Set To Get Ratio: 1:10",
Higher Results Are Better
"a",
"b",14236827.92,14347678.31,14292281.32
"c",
"d",
"e",
"Dragonflydb 1.6.2 - Clients Per Thread: 20 - Set To Get Ratio: 1:10",
"a",
"b",
"c",
"d",
"e",
"Dragonflydb 1.6.2 - Clients Per Thread: 10 - Set To Get Ratio: 1:100",
Higher Results Are Better
"a",
"b",14179386.08,14682820.88,14433895.67
"c",
"d",
"e",
"Dragonflydb 1.6.2 - Clients Per Thread: 20 - Set To Get Ratio: 1:100",
"a",
"b",
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",
"b",57.5868,57.6286,58.1521
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",
"b",519.2032,517.3496,514.9375
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"a",
"b",35.3577,35.2457,35.4804
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"a",
"b",28.2761,28.366,28.1783
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",
"b",1893.4355,1889.2889,1890.445
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",
"b",15.8121,15.8445,15.8383
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"a",
"b",209.1462,207.8506,210.3094
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"a",
"b",4.7791,4.8087,4.7527
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",
"b",632.3595,649.1548,650.6343
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",
"b",47.3603,46.1885,46.0833
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"a",
"b",179.4229,179.5182,180.1038
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"a",
"b",5.5667,5.5645,5.5465
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",
"b",191.9325,191.8605,192.1875
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",
"b",156.2628,156.3135,156.0564
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"a",
"b",87.0636,87.3302,87.0808
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"a",
"b",11.4768,11.4433,11.4759
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",
"b",781.6289,783.5762,780.9149
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",
"b",38.3577,38.2615,38.392
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"a",
"b",269.1984,271.4047,270.0854
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"a",
"b",3.7104,3.6798,3.6982
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",
"b",5685.0857,5675.7993,5674.6607
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",
"b",5.2564,5.265,5.2668
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"a",
"b",747.9747,746.3364,745.8107
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"a",
"b",1.3349,1.3378,1.3387
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",
"b",347.0506,348.1693,347.7458
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",
"b",86.3993,86.1227,86.2303
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"a",
"b",185.3252,184.8247,187.0859
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"a",
"b",5.3909,5.4052,5.3398
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",
"b",60.7709,61.761,62.2548
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",
"b",491.0668,485.666,481.8226
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"a",
"b",21.2809,21.1664,21.0361
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"a",
"b",46.9835,47.2374,47.5302
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",
"b",780.5745,781.2978,782.0348
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",
"b",38.4084,38.3726,38.337
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"a",
"b",271.3413,269.9591,271.2603
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"a",
"b",3.6807,3.6991,3.6815
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",
"b",348.1034,348.1907,349.8678
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",
"b",86.0126,86.1214,85.6657
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"a",
"b",184.5483,183.5328,183.6992
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"a",
"b",5.4159,5.4457,5.4405
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",
"b",486.6855,486.5461,489.1622
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",
"b",61.6163,61.6205,61.2939
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"a",
"b",122.9849,124.4637,123.5857
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"a",
"b",8.1256,8.0293,8.0862
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",
"b",75.5738,75.7505,76.3212
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",
"b",396.8915,395.8967,392.8414
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"a",
"b",39.9479,39.9658,39.9613
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"a",
"b",24.9993,24.9846,24.9913
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",
"b",870.4772,869.2706,868.6628
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",
"b",34.4295,34.4588,34.5018
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"a",
"b",
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"a",
"b",
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",
"b",
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",
"b",
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"a",
"b",
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"a",
"b",
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"a",
"b",
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"a",
"b",
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"a",
"b",
"c",
"d",
"e",
"Neural Magic DeepSparse 1.5 - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"a",
"b",
"c",
"d",
"e",
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:1",
Higher Results Are Better
"a",
"b",
"c",
"d",
"e",
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:5",
Higher Results Are Better
"a",
"b",
"c",
"d",
"e",
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:1",
Higher Results Are Better
"a",
"b",
"c",
"d",
"e",
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:5",
Higher Results Are Better
"a",
"b",
"c",
"d",
"e",
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:10",
Higher Results Are Better
"a",
"b",
"c",
"d",
"e",
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:1",
"a",
"b",
"c",
"d",
"e",
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:5",
"a",
"b",
"c",
"d",
"e",
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10",
Higher Results Are Better
"a",
"b",
"c",
"d",
"e",
"Redis 7.0.12 + memtier_benchmark 2.0 - Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:10",
"a",
"b",
"c",
"d",
"e",