7950X_4090_ubuntu22_pytorch 7950X_4090_ubuntu22_pytorch
Compare your own system(s) to this result file with the
Phoronix Test Suite by running the command:
phoronix-test-suite benchmark 2404018-NE-7950X409062 7950X_4090_ubuntu22_pytorch Processor: AMD Ryzen 9 7950X 16-Core @ 5.88GHz (16 Cores / 32 Threads), Motherboard: 0KDR38 (1.10.1 BIOS), Chipset: AMD Device 14d8, Memory: 4 x 32 GB DDR5-3600MT/s M323R4GA3BB0-CQKOD, Disk: CA6-8D2048-Q11 NVMe SSSTC 2048GB + 2000GB Seagate ST2000DM008-2UB1, Graphics: NVIDIA GeForce RTX 4090 24GB, Audio: NVIDIA Device 22ba, Monitor: LG HDR DQHD, Network: Realtek RTL8125 2.5GbE + Qualcomm Atheros QCNFA765
OS: Ubuntu 22.04, Kernel: 6.5.0-26-generic (x86_64), Desktop: GNOME Shell 42.9, Display Server: X Server 1.21.1.4, Display Driver: NVIDIA 550.54.14, OpenGL: 4.6.0, OpenCL: OpenCL 3.0 CUDA 12.4.89, Vulkan: 1.3.277, Compiler: GCC 11.4.0 + CUDA 12.4, File-System: ext4, Screen Resolution: 3840x1080
Kernel Notes: Transparent Huge Pages: madviseProcessor Notes: Scaling Governor: amd-pstate-epp powersave (EPP: performance) - CPU Microcode: 0xa601203Python Notes: Python 3.10.12Security Notes: 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 Enhanced / Automatic IBRS IBPB: conditional STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected
PyTorch OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50 7950X_4090_ubuntu22_pytorch 80 160 240 320 400 SE +/- 1.58, N = 6 389.01 MIN: 274.46 / MAX: 406.66
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152 7950X_4090_ubuntu22_pytorch 30 60 90 120 150 SE +/- 0.45, N = 20 138.05 MIN: 82.9 / MAX: 144.86
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50 7950X_4090_ubuntu22_pytorch 80 160 240 320 400 SE +/- 0.48, N = 100 380.15 MIN: 259.6 / MAX: 403.8
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50 7950X_4090_ubuntu22_pytorch 80 160 240 320 400 SE +/- 1.00, N = 20 380.93 MIN: 301.68 / MAX: 395.65
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50 7950X_4090_ubuntu22_pytorch 80 160 240 320 400 SE +/- 1.01, N = 20 380.31 MIN: 295 / MAX: 393.65
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152 7950X_4090_ubuntu22_pytorch 30 60 90 120 150 SE +/- 0.42, N = 20 138.68 MIN: 110.38 / MAX: 145.41
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50 7950X_4090_ubuntu22_pytorch 80 160 240 320 400 SE +/- 0.98, N = 20 379.16 MIN: 313.69 / MAX: 393.28
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152 7950X_4090_ubuntu22_pytorch 30 60 90 120 150 SE +/- 0.38, N = 20 138.82 MIN: 115.08 / MAX: 144.91
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50 7950X_4090_ubuntu22_pytorch 80 160 240 320 400 SE +/- 1.20, N = 20 379.59 MIN: 317.27 / MAX: 395.2
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152 7950X_4090_ubuntu22_pytorch 30 60 90 120 150 SE +/- 0.33, N = 20 139.49 MIN: 116.37 / MAX: 144.17
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152 7950X_4090_ubuntu22_pytorch 30 60 90 120 150 SE +/- 0.40, N = 20 139.57 MIN: 113.63 / MAX: 144.53
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152 7950X_4090_ubuntu22_pytorch 30 60 90 120 150 SE +/- 0.55, N = 20 139.59 MIN: 114.91 / MAX: 146.81
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_l 7950X_4090_ubuntu22_pytorch 16 32 48 64 80 SE +/- 0.23, N = 20 71.99 MIN: 59.11 / MAX: 75.02
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_l 7950X_4090_ubuntu22_pytorch 15 30 45 60 75 SE +/- 0.22, N = 20 69.60 MIN: 56.88 / MAX: 72.75
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_l 7950X_4090_ubuntu22_pytorch 16 32 48 64 80 SE +/- 0.21, N = 20 69.73 MIN: 57.6 / MAX: 73.33
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_l 7950X_4090_ubuntu22_pytorch 15 30 45 60 75 SE +/- 0.25, N = 20 69.47 MIN: 57.52 / MAX: 72.93
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_l 7950X_4090_ubuntu22_pytorch 15 30 45 60 75 SE +/- 0.16, N = 20 69.64 MIN: 54.54 / MAX: 73
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_l 7950X_4090_ubuntu22_pytorch 15 30 45 60 75 SE +/- 0.16, N = 20 69.49 MIN: 41.96 / MAX: 73.49
7950X_4090_ubuntu22_pytorch Processor: AMD Ryzen 9 7950X 16-Core @ 5.88GHz (16 Cores / 32 Threads), Motherboard: 0KDR38 (1.10.1 BIOS), Chipset: AMD Device 14d8, Memory: 4 x 32 GB DDR5-3600MT/s M323R4GA3BB0-CQKOD, Disk: CA6-8D2048-Q11 NVMe SSSTC 2048GB + 2000GB Seagate ST2000DM008-2UB1, Graphics: NVIDIA GeForce RTX 4090 24GB, Audio: NVIDIA Device 22ba, Monitor: LG HDR DQHD, Network: Realtek RTL8125 2.5GbE + Qualcomm Atheros QCNFA765
OS: Ubuntu 22.04, Kernel: 6.5.0-26-generic (x86_64), Desktop: GNOME Shell 42.9, Display Server: X Server 1.21.1.4, Display Driver: NVIDIA 550.54.14, OpenGL: 4.6.0, OpenCL: OpenCL 3.0 CUDA 12.4.89, Vulkan: 1.3.277, Compiler: GCC 11.4.0 + CUDA 12.4, File-System: ext4, Screen Resolution: 3840x1080
Kernel Notes: Transparent Huge Pages: madviseProcessor Notes: Scaling Governor: amd-pstate-epp powersave (EPP: performance) - CPU Microcode: 0xa601203Python Notes: Python 3.10.12Security Notes: 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 Enhanced / Automatic IBRS IBPB: conditional STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected
Testing initiated at 1 April 2024 16:53 by user root.