Tests for a future article. Intel Core i9-14900K testing with a ASUS PRIME Z790-P WIFI (1402 BIOS) and ASUS Intel RPL-S 31GB on Ubuntu 23.10 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 2403268-PTS-14900KTU66
14900k tues
Tests for a future article. Intel Core i9-14900K testing with a ASUS PRIME Z790-P WIFI (1402 BIOS) and ASUS Intel RPL-S 31GB on Ubuntu 23.10 via the Phoronix Test Suite.
a:
Processor: Intel Core i9-14900K @ 5.70GHz (24 Cores / 32 Threads), Motherboard: ASUS PRIME Z790-P WIFI (1402 BIOS), Chipset: Intel Device 7a27, Memory: 2 x 16GB DRAM-6000MT/s Corsair CMK32GX5M2B6000C36, Disk: Western Digital WD_BLACK SN850X 1000GB, Graphics: ASUS Intel RPL-S 31GB (1650MHz), Audio: Realtek ALC897, Monitor: ASUS VP28U
OS: Ubuntu 23.10, Kernel: 6.8.0-phx (x86_64), Desktop: GNOME Shell 45.1, Display Server: X Server 1.21.1.7, OpenGL: 4.6 Mesa 24.0~git2312240600.c05261~oibaf~m (git-c05261a 2023-12-24 mantic-oibaf-ppa), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 3840x2160
b:
Processor: Intel Core i9-14900K @ 5.70GHz (24 Cores / 32 Threads), Motherboard: ASUS PRIME Z790-P WIFI (1402 BIOS), Chipset: Intel Device 7a27, Memory: 2 x 16GB DRAM-6000MT/s Corsair CMK32GX5M2B6000C36, Disk: Western Digital WD_BLACK SN850X 1000GB, Graphics: ASUS Intel RPL-S 31GB (1650MHz), Audio: Realtek ALC897, Monitor: ASUS VP28U
OS: Ubuntu 23.10, Kernel: 6.8.0-phx (x86_64), Desktop: GNOME Shell 45.1, Display Server: X Server 1.21.1.7, OpenGL: 4.6 Mesa 24.0~git2312240600.c05261~oibaf~m (git-c05261a 2023-12-24 mantic-oibaf-ppa), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 3840x2160
TensorFlow 2.16.1
Device: CPU - Batch Size: 64 - Model: AlexNet
images/sec > Higher Is Better
a . 270.94 |===================================================================
b . 261.58 |=================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 32 - Model: AlexNet
images/sec > Higher Is Better
a . 229.12 |==================================================================
b . 231.71 |===================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 16 - Model: VGG-16
images/sec > Higher Is Better
a . 12.98 |====================================================================
b . 12.85 |===================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 512 - Model: AlexNet
images/sec > Higher Is Better
a . 293.53 |===================================================================
b . 290.96 |==================================================================
Blender 4.1
Blend File: Barbershop - Compute: CPU-Only
Seconds < Lower Is Better
a . 549.97 |==================================================================
b . 554.76 |===================================================================
Blender 4.1
Blend File: Fishy Cat - Compute: CPU-Only
Seconds < Lower Is Better
a . 74.38 |====================================================================
b . 74.93 |====================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 512 - Model: GoogLeNet
images/sec > Higher Is Better
a . 123.03 |===================================================================
b . 123.90 |===================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 16 - Model: GoogLeNet
images/sec > Higher Is Better
a . 143.68 |===================================================================
b . 142.95 |===================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 256 - Model: GoogLeNet
images/sec > Higher Is Better
a . 124.00 |===================================================================
b . 123.38 |===================================================================
Blender 4.1
Blend File: Classroom - Compute: CPU-Only
Seconds < Lower Is Better
a . 150.60 |===================================================================
b . 151.16 |===================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 256 - Model: AlexNet
images/sec > Higher Is Better
a . 286.40 |===================================================================
b . 285.35 |===================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 1 - Model: GoogLeNet
images/sec > Higher Is Better
a . 56.41 |====================================================================
b . 56.55 |====================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 32 - Model: VGG-16
images/sec > Higher Is Better
a . 13.30 |====================================================================
b . 13.27 |====================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 16 - Model: AlexNet
images/sec > Higher Is Better
a . 181.62 |===================================================================
b . 182.00 |===================================================================
Blender 4.1
Blend File: Pabellon Barcelona - Compute: CPU-Only
Seconds < Lower Is Better
a . 178.90 |===================================================================
b . 179.26 |===================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 16 - Model: ResNet-50
images/sec > Higher Is Better
a . 42.04 |====================================================================
b . 42.12 |====================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 1 - Model: AlexNet
images/sec > Higher Is Better
a . 21.69 |====================================================================
b . 21.73 |====================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 32 - Model: ResNet-50
images/sec > Higher Is Better
a . 39.45 |====================================================================
b . 39.38 |====================================================================
Blender 4.1
Blend File: BMW27 - Compute: CPU-Only
Seconds < Lower Is Better
a . 53.63 |====================================================================
b . 53.71 |====================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 64 - Model: VGG-16
images/sec > Higher Is Better
a . 13.49 |====================================================================
b . 13.47 |====================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 32 - Model: GoogLeNet
images/sec > Higher Is Better
a . 131.07 |===================================================================
b . 131.26 |===================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 64 - Model: ResNet-50
images/sec > Higher Is Better
a . 37.61 |====================================================================
b . 37.66 |====================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 1 - Model: ResNet-50
images/sec > Higher Is Better
a . 17.66 |====================================================================
b . 17.68 |====================================================================
Blender 4.1
Blend File: Junkshop - Compute: CPU-Only
Seconds < Lower Is Better
a . 74.57 |====================================================================
b . 74.53 |====================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 64 - Model: GoogLeNet
images/sec > Higher Is Better
a . 128.45 |===================================================================
b . 128.49 |===================================================================
TensorFlow 2.16.1
Device: CPU - Batch Size: 1 - Model: VGG-16
images/sec > Higher Is Better
a . 6.08 |=====================================================================
b . 6.08 |=====================================================================