gpu-server-us-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/2404025-NE-GPUSERVER81 .
gpu-server-us-report Processor Motherboard Chipset Memory Disk Graphics Audio Network OS Kernel Desktop Display Server Display Driver Vulkan Compiler File-System 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 1.3.242 GCC 11.4.0 ext4 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-us-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 210.71 73.17 207.19 206.01 209.34 74.15 209.22 73.08 206.48 72.99 73.00 73.09 38.19 37.41 37.09 37.51 37.35 37.67 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 +/- 0.68, N = 3 210.71 MIN: 182.95 / MAX: 213.78
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 16 32 48 64 80 SE +/- 0.11, N = 3 73.17 MIN: 66.86 / MAX: 74.21
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 +/- 0.58, N = 3 207.19 MIN: 181.53 / MAX: 210.07
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 +/- 1.42, N = 3 206.01 MIN: 191.13 / MAX: 210.79
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 +/- 2.16, N = 3 209.34 MIN: 192.07 / MAX: 215.05
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 16 32 48 64 80 SE +/- 0.46, N = 3 74.15 MIN: 67.45 / MAX: 75.6
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 +/- 2.00, N = 3 209.22 MIN: 167.77 / MAX: 214.94
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 16 32 48 64 80 SE +/- 0.19, N = 3 73.08 MIN: 66.91 / MAX: 73.84
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 +/- 1.90, N = 6 206.48 MIN: 158.71 / MAX: 212.17
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 16 32 48 64 80 SE +/- 0.75, N = 3 72.99 MIN: 66.15 / MAX: 74.82
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 16 32 48 64 80 SE +/- 0.34, N = 3 73.00 MIN: 66.76 / MAX: 74.15
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 16 32 48 64 80 SE +/- 0.78, N = 5 73.09 MIN: 65.51 / MAX: 74.89
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 +/- 0.13, N = 3 38.19 MIN: 34.59 / MAX: 38.63
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 +/- 0.23, N = 3 37.41 MIN: 34.09 / MAX: 37.98
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 +/- 0.31, N = 3 37.09 MIN: 33.26 / MAX: 37.6
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 +/- 0.23, N = 3 37.51 MIN: 33.96 / MAX: 38.21
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 +/- 0.11, N = 3 37.35 MIN: 33.88 / MAX: 37.7
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 +/- 0.27, N = 3 37.67 MIN: 34.39 / MAX: 38.17
Phoronix Test Suite v10.8.4