I use the 7zip benchmark as a baseline for most boards since it shows the bare CPU performance and is a good comparison between boards. PyTorch and TensorFlow for Machine Learning tests.
#Cpu speed accelerator 8.0 review install#
#Cpu speed accelerator 8.0 review plus#
GPU: NVIDIA Volta with 384 NVIDIA CUDA cores and 48 Tensor Cores, plus 2x NVDLA.CPU: 6-core Carmel Arm 64-bit CPU, 6MB L2 + 4MB 元.Also the benchmarks I used are in no way comprehensive on all workload available. They were made in my home lab using open source tools and equipment available in the marked. The app can be installed with sudo -H pip install -U jetson-stats and run with sudo jtop.ĭisclaimer: These tests and benchmarks are not scientific and validated in a tight laboratory spec. It’s a utility similar to htop but fetching NVIDIA stats. To measure the CPU and GPU use, I’ve installed jtopfrom jetson-stats repo.
All boards were set using Debian or Ubuntu and no Graphical Interface enabled.
Of course the price range varies a lot, from $79 for the Odroid N2 and the RockPro64 to $399 to the Xavier NX we cannot expect similar performance or features.
Here I will do a similar approach and add some GPU and power consumption tests and comparisons. A while back, I’ve benchmarked some ARM boards comparing their performance on Java and other workloads. Here I will do some benchmarks and compare the performance between the Jetson NX and other SBCs.
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