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The performance numbers are not representative of your proposed workload. They are 1) images either from RAM or sequentially on disk that are 2) batched and tested together with vector operations all the way (memory bandwidth also dominates the latency for this reason), and 3) the images are only 256 x 256 pixels (much smaller than a website).

Also, the neural net occupies something like 1.2 gigs of RAM, and something like 3 gigs of GPU memory.

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> much smaller than a website

The character-based net reads the URL, not the website. The common alphabet used in the model made public is of size 69, URL length can be for example 256.

Not only this is below the image size you are talking about, but after the first convolution the alphabet (in one-hot vector encoding) is collapsed, which leaves convolutions across length 256 in 1D.

Not that terrible. In proxy-like territory, passing batches of URLs to the GPUs could pave the way to new ad-blockers at large. To be tested :)

I was referring to the hypothetical image classifier, not the URL text classifier (the URL text classifier, as I mentioned, would perform very poorly).

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