The author recommends Titan V - without justifying its $3k price. The 1080 series is less than half that price with comparable benchmarks. Am I missing something?
I am doing experimental work where I really need to have double precision i.e. FP64. The Titan V offers the same stellar FP64 performance as the server oriented Tesla V100.
Forms matter. Colloquial meanings also matter, but not as much, particularly when they're an egregious violation of English and decency.
"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.