gpu-server-eu-report AMD Ryzen Threadripper PRO 5955WX 16-Cores testing with a ASUS Pro WS WRX80E-SAGE SE WIFI II (1302 BIOS) and Gigabyte NVIDIA GeForce RTX 4090 24GB on Ubuntu 22.04 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2404028-NE-GPUSERVER82 .
gpu-server-eu-report Processor Motherboard Chipset Memory Disk Graphics Audio Network OS Kernel Desktop Display Server Display Driver Compiler File-System Screen Resolution AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte AMD Ryzen Threadripper PRO 5955WX 16-Cores @ 4.00GHz (16 Cores / 32 Threads) ASUS Pro WS WRX80E-SAGE SE WIFI II (1302 BIOS) AMD Starship/Matisse 256GB 4001GB Western Digital WD_BLACK SN850X 4000GB Gigabyte NVIDIA GeForce RTX 4090 24GB NVIDIA Device 22ba 2 x Intel 10G X550T + Intel Wi-Fi 6 AX210/AX211/AX411 Ubuntu 22.04 6.5.0-26-generic (x86_64) GNOME Shell 42.9 X Server 1.21.1.4 NVIDIA GCC 11.4.0 ext4 800x600 OpenBenchmarking.org - Transparent Huge Pages: madvise - Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0xa008205 - Python 3.10.12 - gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + spec_rstack_overflow: Vulnerable: Safe RET no microcode + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected
gpu-server-eu-report pytorch: NVIDIA CUDA GPU - 1 - ResNet-50 pytorch: NVIDIA CUDA GPU - 1 - ResNet-152 pytorch: NVIDIA CUDA GPU - 16 - ResNet-50 pytorch: NVIDIA CUDA GPU - 32 - ResNet-50 pytorch: NVIDIA CUDA GPU - 64 - ResNet-50 pytorch: NVIDIA CUDA GPU - 16 - ResNet-152 pytorch: NVIDIA CUDA GPU - 256 - ResNet-50 pytorch: NVIDIA CUDA GPU - 32 - ResNet-152 pytorch: NVIDIA CUDA GPU - 512 - ResNet-50 pytorch: NVIDIA CUDA GPU - 64 - ResNet-152 pytorch: NVIDIA CUDA GPU - 256 - ResNet-152 pytorch: NVIDIA CUDA GPU - 512 - ResNet-152 pytorch: NVIDIA CUDA GPU - 1 - Efficientnet_v2_l pytorch: NVIDIA CUDA GPU - 16 - Efficientnet_v2_l pytorch: NVIDIA CUDA GPU - 32 - Efficientnet_v2_l pytorch: NVIDIA CUDA GPU - 64 - Efficientnet_v2_l pytorch: NVIDIA CUDA GPU - 256 - Efficientnet_v2_l pytorch: NVIDIA CUDA GPU - 512 - Efficientnet_v2_l AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 214.60 74.76 213.53 212.31 213.68 75.20 213.57 74.44 211.68 75.22 75.13 74.93 38.73 38.12 38.01 37.76 38.08 38.27 OpenBenchmarking.org
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 50 100 150 200 250 SE +/- 0.46, N = 3 214.60 MIN: 182.08 / MAX: 216.79
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 20 40 60 80 100 SE +/- 0.18, N = 3 74.76 MIN: 68.57 / MAX: 75.86
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 50 100 150 200 250 SE +/- 0.25, N = 3 213.53 MIN: 197.82 / MAX: 215.82
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 50 100 150 200 250 SE +/- 0.36, N = 3 212.31 MIN: 199.16 / MAX: 214.97
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 50 100 150 200 250 SE +/- 1.41, N = 3 213.68 MIN: 126.84 / MAX: 218.74
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 20 40 60 80 100 SE +/- 0.44, N = 3 75.20 MIN: 68.59 / MAX: 76.29
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 50 100 150 200 250 SE +/- 0.90, N = 3 213.57 MIN: 202.47 / MAX: 217.01
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 20 40 60 80 100 SE +/- 0.59, N = 3 74.44 MIN: 67.59 / MAX: 76.29
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 50 100 150 200 250 SE +/- 0.09, N = 3 211.68 MIN: 201.79 / MAX: 214.18
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 20 40 60 80 100 SE +/- 0.43, N = 3 75.22 MIN: 68.96 / MAX: 76.24
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 20 40 60 80 100 SE +/- 0.36, N = 3 75.13 MIN: 68.59 / MAX: 76.23
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 20 40 60 80 100 SE +/- 0.54, N = 3 74.93 MIN: 68.82 / MAX: 76.5
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_l AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 9 18 27 36 45 SE +/- 0.36, N = 6 38.73 MIN: 34.49 / MAX: 39.57
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_l AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 9 18 27 36 45 SE +/- 0.17, N = 3 38.12 MIN: 34.52 / MAX: 38.69
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_l AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 9 18 27 36 45 SE +/- 0.46, N = 4 38.01 MIN: 34.07 / MAX: 38.96
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_l AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 9 18 27 36 45 SE +/- 0.28, N = 13 37.76 MIN: 33.85 / MAX: 39.14
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_l AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 9 18 27 36 45 SE +/- 0.10, N = 3 38.08 MIN: 34.82 / MAX: 38.49
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_l OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_l AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 9 18 27 36 45 SE +/- 0.18, N = 3 38.27 MIN: 34.61 / MAX: 38.86
Phoronix Test Suite v10.8.4