pytorch tensorflow

AMD Ryzen 9 7950X 16-Core testing with a ASUS ROG STRIX X670E-E GAMING WIFI (1905 BIOS) and NVIDIA GeForce RTX 3080 10GB on Ubuntu 23.10 via the Phoronix Test Suite.

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Result
Identifier
Performance Per
Dollar
Date
Run
  Test
  Duration
AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 3080
March 27
  2 Hours, 4 Minutes
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pytorch tensorflow AMD Ryzen 9 7950X 16-Core testing with a ASUS ROG STRIX X670E-E GAMING WIFI (1905 BIOS) and NVIDIA GeForce RTX 3080 10GB on Ubuntu 23.10 via the Phoronix Test Suite. ,,"AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 3080" Processor,,AMD Ryzen 9 7950X 16-Core @ 5.88GHz (16 Cores / 32 Threads) Motherboard,,ASUS ROG STRIX X670E-E GAMING WIFI (1905 BIOS) Chipset,,AMD Device 14d8 Memory,,2 x 16GB DRAM-6000MT/s G Skill F5-6000J3038F16G Disk,,2000GB Samsung SSD 980 PRO 2TB + 123GB SanDisk 3.2Gen1 Graphics,,NVIDIA GeForce RTX 3080 10GB Audio,,NVIDIA GA102 HD Audio Monitor,,DELL U2723QE Network,,Intel I225-V + Intel Wi-Fi 6 AX210/AX211/AX411 OS,,Ubuntu 23.10 Kernel,,6.7.0-060700-generic (x86_64) Desktop,,GNOME Shell 45.2 Display Server,,X Server 1.21.1.7 Display Driver,,NVIDIA 550.54.14 OpenGL,,4.6.0 OpenCL,,OpenCL 3.0 CUDA 12.4.89 Compiler,,GCC 13.2.0 File-System,,ext4 Screen Resolution,,3840x2160 ,,"AMD Ryzen 9 7950X 16-Core - NVIDIA GeForce RTX 3080" "PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-50 (batches/sec)",HIB,71.77 "PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-152 (batches/sec)",HIB,29.39 "PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-50 (batches/sec)",HIB,48.85 "PyTorch - Device: CPU - Batch Size: 32 - Model: ResNet-50 (batches/sec)",HIB,48.54 "PyTorch - Device: CPU - Batch Size: 64 - Model: ResNet-50 (batches/sec)",HIB,48.57 "PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-152 (batches/sec)",HIB,20.08 "PyTorch - Device: CPU - Batch Size: 256 - Model: ResNet-50 (batches/sec)",HIB,48.51 "PyTorch - Device: CPU - Batch Size: 32 - Model: ResNet-152 (batches/sec)",HIB,19.52 "PyTorch - Device: CPU - Batch Size: 64 - Model: ResNet-152 (batches/sec)",HIB,19.69 "PyTorch - Device: CPU - Batch Size: 256 - Model: ResNet-152 (batches/sec)",HIB,19.90 "PyTorch - Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l (batches/sec)",HIB,16.53 "PyTorch - Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l (batches/sec)",HIB,11.56 "PyTorch - Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l (batches/sec)",HIB,11.86 "PyTorch - Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l (batches/sec)",HIB,11.77 "PyTorch - Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l (batches/sec)",HIB,11.84 "TensorFlow - Device: CPU - Batch Size: 1 - Model: VGG-16 (images/sec)",HIB,5.65 "TensorFlow - Device: CPU - Batch Size: 1 - Model: AlexNet (images/sec)",HIB,16.11 "TensorFlow - Device: CPU - Batch Size: 16 - Model: VGG-16 (images/sec)",HIB,18.05 "TensorFlow - Device: CPU - Batch Size: 32 - Model: VGG-16 (images/sec)",HIB,18.99 "TensorFlow - Device: CPU - Batch Size: 64 - Model: VGG-16 (images/sec)",HIB,19.68 "TensorFlow - Device: CPU - Batch Size: 16 - Model: AlexNet (images/sec)",HIB,170.76 "TensorFlow - Device: CPU - Batch Size: 32 - Model: AlexNet (images/sec)",HIB,254.51 "TensorFlow - Device: CPU - Batch Size: 64 - Model: AlexNet (images/sec)",HIB,334.74 "TensorFlow - Device: CPU - Batch Size: 1 - Model: GoogLeNet (images/sec)",HIB,61.10 "TensorFlow - Device: CPU - Batch Size: 1 - Model: ResNet-50 (images/sec)",HIB,16.15 "TensorFlow - Device: CPU - Batch Size: 16 - Model: GoogLeNet (images/sec)",HIB,142.24 "TensorFlow - Device: CPU - Batch Size: 16 - Model: ResNet-50 (images/sec)",HIB,41.34 "TensorFlow - Device: CPU - Batch Size: 32 - Model: GoogLeNet (images/sec)",HIB,141.41 "TensorFlow - Device: CPU - Batch Size: 32 - Model: ResNet-50 (images/sec)",HIB,42.48 "TensorFlow - Device: CPU - Batch Size: 64 - Model: GoogLeNet (images/sec)",HIB,138.76 "TensorFlow - Device: CPU - Batch Size: 64 - Model: ResNet-50 (images/sec)",HIB,42.68