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AMD Ryzen 7 7840HS testing with a NB05 TUXEDO Pulse 14 Gen3 R14FA1 (8.06 BIOS) and AMD Phoenix1 4GB on Tuxedo 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 2403266-NE-NEW59105411
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CPU Massive 5 Tests
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Multi-Core 5 Tests
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new Suite 1.0.0 System Test suite extracted from new. pts/jpegxl-decode-1.6.0 --num_threads=1 --num_reps=90 CPU Threads: 1 pts/brl-cad-1.6.0 VGR Performance Metric pts/svt-av1-2.12.0 --preset 13 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 13 - Input: Bosphorus 4K pts/jpegxl-decode-1.6.0 --num_reps=250 CPU Threads: All pts/svt-av1-2.12.0 --preset 12 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 12 - Input: Bosphorus 4K pts/rocksdb-1.6.0 --benchmarks="fillseq" Test: Sequential Fill pts/jpegxl-1.6.0 sample-4.png out.jxl -q 90 --num_reps 50 Input: PNG - Quality: 90 pts/jpegxl-1.6.0 sample-4.png out.jxl -q 80 --num_reps 80 Input: PNG - Quality: 80 pts/svt-av1-2.12.0 --preset 4 -n 160 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 4 - Input: Bosphorus 1080p pts/svt-av1-2.12.0 --preset 13 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 13 - Input: Bosphorus 1080p pts/svt-av1-2.12.0 --preset 12 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 12 - Input: Bosphorus 1080p pts/openvino-1.5.0 -m models/intel/person-vehicle-bike-detection-2004/FP16/person-vehicle-bike-detection-2004.xml -d CPU Model: Person Vehicle Bike Detection FP16 - Device: CPU pts/deepsparse-1.7.0 zoo:llama2-7b-llama2_chat_llama2_pretrain-base_quantized --scenario async Model: Llama2 Chat 7b Quantized - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.7.0 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_uniform_quant-none --scenario async Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/openvino-1.5.0 -m models/intel/machine-translation-nar-en-de-0002/FP16/machine-translation-nar-en-de-0002.xml -d CPU Model: Machine Translation EN To DE FP16 - Device: CPU pts/svt-av1-2.12.0 --preset 4 -n 160 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 4 - Input: Bosphorus 4K pts/jpegxl-1.6.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 80 --num_reps 80 Input: JPEG - Quality: 80 pts/openvino-1.5.0 -m models/intel/noise-suppression-poconetlike-0001/FP16/noise-suppression-poconetlike-0001.xml -d CPU Model: Noise Suppression Poconet-Like FP16 - Device: CPU pts/openvino-1.5.0 -m models/intel/face-detection-retail-0005/FP16-INT8/face-detection-retail-0005.xml -d CPU Model: Face Detection Retail FP16-INT8 - Device: CPU pts/svt-av1-2.12.0 --preset 8 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 8 - Input: Bosphorus 1080p pts/rocksdb-1.6.0 --benchmarks="readwhilewriting" Test: Read While Writing pts/rocksdb-1.6.0 --benchmarks="fillsync" Test: Random Fill Sync pts/rocksdb-1.6.0 --benchmarks="overwrite" Test: Overwrite pts/deepsparse-1.7.0 zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned90-none --scenario async Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream pts/jpegxl-1.6.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 90 --num_reps 50 Input: JPEG - Quality: 90 pts/draco-1.6.1 -i church.ply Model: Church Facade pts/deepsparse-1.7.0 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned85-none --scenario async Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/openvino-1.5.0 -m models/intel/road-segmentation-adas-0001/FP16/road-segmentation-adas-0001.xml -d CPU Model: Road Segmentation ADAS FP16 - Device: CPU pts/build-linux-kernel-1.16.0 defconfig Build: defconfig pts/openvino-1.5.0 -m models/intel/handwritten-english-recognition-0001/FP16/handwritten-english-recognition-0001.xml -d CPU Model: Handwritten English Recognition FP16 - Device: CPU pts/openvino-1.5.0 -m models/intel/age-gender-recognition-retail-0013/FP16-INT8/age-gender-recognition-retail-0013.xml -d CPU Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU pts/draco-1.6.1 -i lion.ply Model: Lion pts/openvino-1.5.0 -m models/intel/road-segmentation-adas-0001/FP16-INT8/road-segmentation-adas-0001.xml -d CPU Model: Road Segmentation ADAS FP16-INT8 - Device: CPU pts/v-ray-1.5.0 -m vray Mode: CPU pts/deepsparse-1.7.0 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned85-none --scenario sync Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream pts/jpegxl-1.6.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 100 --num_reps 20 Input: JPEG - Quality: 100 pts/jpegxl-1.6.0 sample-4.png out.jxl -q 100 --num_reps 20 Input: PNG - Quality: 100 pts/openvino-1.5.0 -m models/intel/handwritten-english-recognition-0001/FP16-INT8/handwritten-english-recognition-0001.xml -d CPU Model: Handwritten English Recognition FP16-INT8 - Device: CPU pts/openvino-1.5.0 -m models/intel/face-detection-retail-0005/FP16/face-detection-retail-0005.xml -d CPU Model: Face Detection Retail FP16 - Device: CPU pts/rocksdb-1.6.0 --benchmarks="updaterandom" Test: Update Random pts/deepsparse-1.7.0 zoo:nlp/sentiment_analysis/oberta-base/pytorch/huggingface/sst2/pruned90_quant-none --input_shapes='[1,128]' --scenario async Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/openvino-1.5.0 -m models/intel/face-detection-0206/FP16/face-detection-0206.xml -d CPU Model: Face Detection FP16 - Device: CPU pts/rocksdb-1.6.0 --benchmarks="fillrandom" Test: Random Fill pts/svt-av1-2.12.0 --preset 8 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 8 - Input: Bosphorus 4K pts/openvino-1.5.0 -m models/intel/vehicle-detection-0202/FP16/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16 - Device: CPU pts/deepsparse-1.7.0 zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned90-none --scenario sync Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream pts/deepsparse-1.7.0 zoo:nlp/sentiment_analysis/oberta-base/pytorch/huggingface/sst2/pruned90_quant-none --input_shapes='[1,128]' --scenario sync Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream pts/deepsparse-1.7.0 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario sync Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream pts/primesieve-1.10.0 1e13 Length: 1e13 pts/openvino-1.5.0 -m models/intel/face-detection-0206/FP16-INT8/face-detection-0206.xml -d CPU Model: Face Detection FP16-INT8 - Device: CPU pts/deepsparse-1.7.0 zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario sync Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream pts/deepsparse-1.7.0 zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario async Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream pts/primesieve-1.10.0 1e12 Length: 1e12 pts/rocksdb-1.6.0 --benchmarks="readrandomwriterandom" Test: Read Random Write Random pts/openvino-1.5.0 -m models/intel/person-reidentification-retail-0277/FP16/person-reidentification-retail-0277.xml -d CPU Model: Person Re-Identification Retail FP16 - Device: CPU pts/build-linux-kernel-1.16.0 allmodconfig Build: allmodconfig pts/deepsparse-1.7.0 zoo:nlp/document_classification/obert-base/pytorch/huggingface/imdb/base-none --scenario async Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.7.0 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_uniform_quant-none --scenario sync Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream pts/deepsparse-1.7.0 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario async Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream pts/openvino-1.5.0 -m models/intel/person-detection-0303/FP32/person-detection-0303.xml -d CPU Model: Person Detection FP32 - Device: CPU pts/openvino-1.5.0 -m models/intel/age-gender-recognition-retail-0013/FP16/age-gender-recognition-retail-0013.xml -d CPU Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU pts/deepsparse-1.7.0 zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned97_quant-none --input_shapes='[1,128]' --scenario async Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.7.0 zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario sync Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream pts/openvino-1.5.0 -m models/intel/weld-porosity-detection-0001/FP16/weld-porosity-detection-0001.xml -d CPU Model: Weld Porosity Detection FP16 - Device: CPU pts/deepsparse-1.7.0 zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned97_quant-none --input_shapes='[1,128]' --scenario sync Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream pts/deepsparse-1.7.0 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario sync Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream pts/openvino-1.5.0 -m models/intel/person-detection-0303/FP16/person-detection-0303.xml -d CPU Model: Person Detection FP16 - Device: CPU pts/deepsparse-1.7.0 zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario async Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream pts/openvino-1.5.0 -m models/intel/weld-porosity-detection-0001/FP16-INT8/weld-porosity-detection-0001.xml -d CPU Model: Weld Porosity Detection FP16-INT8 - Device: CPU pts/openvino-1.5.0 -m models/intel/vehicle-detection-0202/FP16-INT8/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16-INT8 - Device: CPU pts/deepsparse-1.7.0 zoo:nlp/document_classification/obert-base/pytorch/huggingface/imdb/base-none --scenario sync Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream pts/deepsparse-1.7.0 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario async Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream pts/rocksdb-1.6.0 --benchmarks="readrandom" Test: Random Read pts/deepsparse-1.7.0 zoo:llama2-7b-llama2_chat_llama2_pretrain-base_quantized --scenario sync Model: Llama2 Chat 7b Quantized - Scenario: Synchronous Single-Stream