Many state-of-art models won't train well on FP16. But for inferencing it's extraordinarily good. 2x1080Ti is the sweet spot for FP32 training on "budget" at the moment.
"When I first looked at fp16 Inception3 was the largest model I could train. Inception4 blew up until I went back to fp32. Mixed precision needs extra care, scaling of gradients and such. Still I think it is a good thing. What I really want to test is model size reduction for inference with TensorRT targeted to tensorcores. I think that is probably the best use case. Non-linear optimization is just too susceptible to precision loss."
There was also some NVidia video presentation recommending mixed FP32/FP16 training instead of pure FP16.
Forms matter. Colloquial meanings also matter, but not as much, particularly when they're an egregious violation of English and decency.