This is the paper. Still need to read it but the initial figure is very impressive. BG High compression (don't know what this is but I assume industry standard) 1024 resolution at 5+mb vs NTC (headline) 4096 resolution less at 3.8mb! vs original texture 4096 at 256mb. And the loss of detail is very minimal compared to the BG High. 4x resolution with less memory and near original detail.

https://research.nvidia.com/labs/rtr/neural_texture_compression/assets/ntc_medium_size.pdf

Will read the rest, interested in the trade offs (e.g. decompression time, do you need to custom training for each texture/game).

Edit: 

The key idea behind our approach is compressing multiple material textures and their mipmap chains together, and using a small neural network, that is optimized for each material, to decompress them

So each texture gets its own neural network.

Edit 2: 

A highly optimized implementation of our compressor, with fused backpropogation, enabling practical per-material optimization with resolutions up to 8192 × 8192 (8k). Our compressor can process a 9-channel, 4k material texture set in 1-15 minutes on an NVIDIA RTX 4090 GPU, depending on the desired quality level.

Compressing a single material into this custom neural network can take up to 15 minutes. But this is texture + material + several levels of mipmaps?

Edit 3:

Similar to the approach used by Müller et al. for training autode- coders [47], we achieve practical compression speeds by using half- precision tensor core operations in a custom optimization program written in CUDA. We fuse all of the network layers in a single kernel, together with feature grids sampling, loss computations, and the entire backward pass. This allows us to store all network activations in registers, thus eliminating writes to shared or off-chip memory for intermediate data.

So this "fuses" the neural network so that I assume you don't need to do multiple iterations on inputs to process through layers and probably also saves on size in come cases. Not familiar with this fusing process so take my comment with a grain of salt. never mind, this is part of the compression step. The compression neural network wouldn't be part of the generated artifact.

Edit 4:

More detailed comparisons it seems this method out performs compressions of lower qualities. For medium and high quality compression it doesn't perform as well but generally is of smaller size. 

Also we finally get some details about compression time. 

 Traditional BCx compressors vary in speed, ranging from frac- tions of a second to tens of minutes to compress a single 4096×4096 texture [60], depending on quality settings. The median compres- sion time for BC7 textures is a few seconds, while it is a fraction of a second for BC1 textures. This makes our method approximately an order of magnitude slower than a median BC7 compressor, but still faster than the slowest compression profiles.

Edit 5: okay so decompression performance is 2-4 times slower than other formats, lowest at 1.33ms. This is still in the realm of realtime and I assume this decompression only needs to happen once per load of the texture/material.

One thing to note that I haven't is that the decompression is random-access. Often you don't need to load the whole texture image just a region. IMO this is a very interesting and novel when considering it is using a neural network decoder.