The Importance Of The TUXEDO Driver Package On Their Newer Ryzen Laptops

Written by Michael Larabel in Computers on 14 February 2024 at 08:50 AM EST. Page 3 of 6. 22 Comments.
Timed Godot Game Engine Compilation benchmark with settings of Time To Compile. performance was the fastest.
Timed Godot Game Engine Compilation benchmark with settings of Time To Compile. performance was the fastest.
Timed PHP Compilation benchmark with settings of Time To Compile. performance was the fastest.
Timed PHP Compilation benchmark with settings of Time To Compile. performance was the fastest.

With the TUXEDO drivers package and setting to the performance mode, the Ryzen 7 7840HS was now able to sustain much higher power behavior than out-of-the-box on Ubuntu 23.10.

OpenVINO benchmark with settings of Model: Face Detection FP16-INT8, Device: CPU. performance was the fastest.
OpenVINO benchmark with settings of Model: Face Detection FP16-INT8, Device: CPU. performance was the fastest.
OpenVINO benchmark with settings of Model: Weld Porosity Detection FP16-INT8, Device: CPU. performance was the fastest.
OpenVINO benchmark with settings of Model: Weld Porosity Detection FP16-INT8, Device: CPU. performance was the fastest.
OpenVINO benchmark with settings of Model: Person Vehicle Bike Detection FP16, Device: CPU. performance was the fastest.
OpenVINO benchmark with settings of Model: Person Vehicle Bike Detection FP16, Device: CPU. performance was the fastest.

So if investing in a TUXEDO Computers laptop as a desktop replacement or have some heavy-lifting to get done from your laptop, making use of the TUXEDO performance profile can be very beneficial to the performance.

PyTorch benchmark with settings of Device: CPU, Batch Size: 1, Model: ResNet-152. performance was the fastest.
PyTorch benchmark with settings of Device: CPU, Batch Size: 1, Model: ResNet-152. performance was the fastest.
PyTorch benchmark with settings of Device: CPU, Batch Size: 1, Model: ResNet-152. performance was the fastest.
PyTorch benchmark with settings of Device: CPU, Batch Size: 64, Model: ResNet-50. performance was the fastest.
PyTorch benchmark with settings of Device: CPU, Batch Size: 64, Model: ResNet-50. performance was the fastest.
PyTorch benchmark with settings of Device: CPU, Batch Size: 64, Model: ResNet-50. performance was the fastest.
TensorFlow benchmark with settings of Device: CPU, Batch Size: 64, Model: ResNet-50. performance was the fastest.
TensorFlow benchmark with settings of Device: CPU, Batch Size: 64, Model: ResNet-50. performance was the fastest.
Blender benchmark with settings of Blend File: BMW27, Compute: CPU-Only. performance was the fastest.
Blender benchmark with settings of Blend File: BMW27, Compute: CPU-Only. performance was the fastest.
Appleseed benchmark with settings of Scene: Emily. performance was the fastest.
Appleseed benchmark with settings of Scene: Emily. performance was the fastest.

It's during the heavy multi-threaded workloads where the performance profile provided the most benefit.


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