Unfortunately it is slop, beyond the comprehension of the author unless they are experienced with DLSS internals to explain it in depth.

This is how I feel about every single project announcement on HN recently, they are already bragging about models all over the place, why shouldn't they go full way down being replaced by the Borg?

But the line is drawn when it involves CUDA and any part of their closed source compilers (nvcc).

There are obvious reasons why they are closed source, but it’s becoming pointless since Deepseek have open sourced their AI compiler and compute libraries with DeepGEMM and eventually they will catch up.

there's no way that's true!?

They don't. Only DLSS 1 was trained specifically per each game.

> Isn't the mote that Nvidia has is they work with studios to generate the training data from the game, then they ship a model per game?

That was true for the very first version of DLSS, from DLSS 2 on the models have been universal - the per-game adjustments are done on the inference end by changing the effect intensity or masking out objects

They have a technical report on the neural rendering part of DLSS 5 which goes into it: https://research.nvidia.com/labs/adlr/files/DLSS5_Report.pdf

RTX 5060: 9.9 ms at 1080p

RTX 5070: 10.2 ms at 1440p

RTX 5080: 13.7 ms at 2160p

RTX 5090: 8.2 ms at 2160p

Source: https://www.youtube.com/watch?v=3EfLjmdG29Q&t=600

The current implementation is more of a tech demo than a practical way to play games (+ officially it's available in 1 game). It's _fast enough_ to make some impressive YouTube videos but you most likely won't want to play anything with it yet.

Nvidia has stated that they're still working on improving the performance. No doubt future hardware generations will also include further hardware optimisations.

The potential for this kind of technology is pretty awesome, especially given that people have also found ways to add this to emulators.