<p>Almost all deep learning frameworks (MXNet included) will run much faster with a CUDA-capable GPU from NVIDIA.  GPU's will often speed up the kinds of vector math needed for deep learning by 100x.  Apple stopped building machines with NVIDIA GPUs several years ago (2012 IIRC).  If you have one of those make sure you have <a href="http://docs.nvidia.com/cuda/cuda-installation-guide-mac-os-x/" rel="nofollow noreferrer">CUDA working on your Mac</a>.  I'm not aware of any way right now to get MXNet to make use of the AMD or Intel GPUs that ship with Apple machines.  Also know that even with the fastest GPU's deep learning jobs will often take hours, days, or even weeks to complete.  So patience is definitely part of the game, regardless of what hardware you're using.</p>

<p>That said, GPU's aren't the only way to run deep learning systems.  Particularly for making predictions (inference) with pre-trained models, CPUs are often just fine.  So this can be useful for a task like <a href="http://mxnet.io/tutorials/python/predict_imagenet.html" rel="nofollow noreferrer">semantic image processing</a>.
Or when training, using smaller datasets and smaller models can make them run faster.  Also, to make sure you're getting the most out of your CPU, check that you have installed a good BLAS library like <a href="https://software.intel.com/en-us/articles/performance-tools-for-software-developers-how-can-i-download-the-intel-ipp-and-intel-mkl-for-mac-os-x" rel="nofollow noreferrer">Intel's MKL</a>.</p>

<p>But to get any useful work out of a raspberry pi is going to take some careful optimization, even for inference.  This is an area of active scientific research.  See for example <a href="https://arxiv.org/abs/1510.00149" rel="nofollow noreferrer">this paper</a>.  Or look at adding a <a href="https://www.engadget.com/2016/04/28/movidius-fathom-neural-compute-stick/" rel="nofollow noreferrer">USB hardware accelerator</a>.</p>
What is the optimum hardware requirements to run MXNET smoothly
<p>I am using my MacBookPro. I am trying to run the mxnet python demo code and the execution time is extremely slow. It takes a lot time to execute the code. Is this normal? Also i want to run mxnet on Raspberry Pi 3.</p>