Nvidia Xavier Benchmark

Following that are a variety of cpu only arm linux benchmarks just for seeing how these nvidia carmel cores compare to the arm cpu performance on other socs lower cost developer boards.

Nvidia xavier benchmark. Nvidia has released a series of jetson hardware modules for embedded applications. Nvidia turing gpus and our xavier system on a chip posted leadership results in mlperf inference 0 5 the first independent benchmarks for ai inference. With the nvidia jetson agx xavier developer kit you can easily create and deploy end to end ai robotics applications for manufacturing delivery retail smart cities and more. Jetson xavier nx delivers up to 21 tops making it ideal for high performance compute and ai in embedded and edge systems.

Max n mode for jetson agx xavier. Nvidia xavier extended its performance leadership demonstrated in the first ai inference tests held last year while supporting all new use cases added for energy efficient edge compute soc. Inferencing for intelligent vehicles is a full stack problem. Supported by nvidia jetpack and deepstream sdks as well as cuda cudnn and tensorrt software libraries the kit provides all the tools you need to get started right away.

Input image resolution. The nvidia xavier nx is 150 faster than the amlogic and 187 faster than the. On jetson xavier nx and jetson agx xavier both nvidia deep learning accelerator nvdla engines and the gpu were run simultaneously with int8 precision while on jetson nano and jetson tx2 the gpu was run with fp16 precision. Before today the industry was hungry for objective metrics on inference because its expected to be the largest and most competitive slice of the ai market.

For this round of nvidia agx xavier benchmarking is a look at the tensorrt inference performance with vgg16 alexnet resnet50 googlenet at int8 and fp16 with a variety of batch sizes. Benchmark comparison for jetson nano tx1 tx2 and agx xavier. Nvidia jetson is the world s leading embedded platform for image processing and dl ai tasks. You get the performance of 384 nvidia cuda cores 48 tensor cores 6 carmel arm cpus and two nvidia deep learning accelerators nvdla engines.

Each jetson module was run with maximum performance.