Here's a link that's a couple of years old now, but gives a pretty reasonable overview of where it sits in the stack: https://gpuopen.com/ported-caffe-hip-here

Here's a link that's a couple of years old now, but gives a pretty reasonable overview of where it sits in the stack: https://gpuopen.com/ported-caffe-hip-heres-happened/ https://gpuopen.com/ported-caffe-hip-heres-happened/ As to why you should care, it depends at what level you work. I'm not a graphics/HPC programmer, so I can't really comment on what you'd be using it for in those areas. For DL applications, though, CUDA/CUDNN used to be the only GPU libraries that offered some of the common/optimized operations you'd encounter in deep learning (e.g. 2d convolutions). That meant you were limited to NVIDIA GPUs within all the higher level frameworks (Tensorflow/Pytorch/etc). With ROCm more developed, it can supplant CUDA as the low-level library for those frameworks. This means you can start using AMD GPUs for training/inference, which are considerably cheaper. In fact, in the few months that have passed since I last checked, it now appears that ROCm supports TF - big step. it means you're no longer locked to NVIDIA GPUs