You're taking things way too seriously. Besides, there really aren't any other practical deep learning solutions right now besides NVIDIA. And that's because

You're taking things way too seriously. Besides, there really aren't any other practical deep learning solutions right now besides NVIDIA. And that's because AMD and Intel dropped the ball NVIDIA carried for the last 10 years until they finally knocked one out of the park here. No sympathy whatsoever to the pack of narrow-minded neck beards that didn't see the locomotive chugging down the tunnel until it ran them over. Even NVIDIA almost missed this one. Just ask Geoffrey Hinton about this some time. The OpenCL port Caffe has abysmal performance on AMD GPUs (see Caffe-CLGreenTea on Soumith's chart). https://github.com/BVLC/caffe/pull/2195 https://github.com/BVLC/caffe/pull/2195 Intel isn't even on the map yet, and there's no good training code for FPGAs. And ASICs? Maybe when Moore's Law slows down. And it's not a trap at all. Now that DNNs are seen as a multibillion dollar industry, the status quo will change in a few years. But until then, it's NVIDIA's gravy train, and they earned it by taking a huge risk on GPGPU. Now if you think you can build the competitor to NVIDIA and save us all from the looming trap, go for it! Meanwhile, all other things equal, use Caffe with cuDNN until someone earns your business with something better. As long as you're running at the API level, it really doesn't matter what's going on under the hood.