The quality and open source nature of their tools has resulted in much of my research group (real-time vision) increasingly and voluntarily moving to work on AMD platforms (we were previously almost exclusively using CUDA).

Also, AMD does not limit FP performance on consumer cards.

I don't understand this meme. The consumer cards are different chips with slow fp64 hardware. In what sense is that "limiting" performance relative to the enterprise cards?

"For their consumer cards, NVIDIA has severely limited FP16 CUDA performance. GTX 1080’s FP16 instruction rate is 1/128th its FP32 instruction rate, or after you factor in vec2 packing, the resulting theoretical performance (in FLOPs) is 1/64th the FP32 rate, or about 138 GFLOPs."

https://www.anandtech.com/show/10325/the-nvidia-geforce-gtx-...

"FP16 performance is 1/64th and FP64 is 1/32th of FP32 performance."

https://blog.inten.to/hardware-for-deep-learning-part-3-gpu-...