Deep Learning Super Sampling. This probably sounds like some kind of hokey way to balance
your energy field or something, but it's actually a new AI-powered method from NVIDIA to make your
games look better. Now, you might have already been familiar with traditional super sampling. That is,
when your graphics cards render frames at a higher resolution than what your monitor can support,
but then downscales the image to fit your display. Although it's very computationally taxing,
it can provide a significant quality boost that other anti-aliasing methods may not be able to
achieve. DLSS, however, is quite different than standard super sampling, and it's actually
less demanding on your GPU. Instead of simply forcing your GPU to render higher resolution
frames from scratch, it uses a neural network to predict what the frames should look like.
The neural network is trained by an NVIDIA supercomputer that feeds it correct frames
from certain games to help it learn how to generate extra pixels accurately. These
correct frames are actually 16k images, so the AI will have a very granular level of detail to
learn from. The AI model is then sent to your GPU via driver updates so it can be run locally. The
idea here is that it's easier for your GPU to run this neural network when you're playing a
graphically intense game running at a low frame rate than it is to keep drawing new frames from
scratch. Regardless of how difficult the game is to run, DLSS uses a fixed amount of time for frame,
so it often doesn't take as long for your GPU to spit out a DLSS-assisted frame
as it does to render one the old-fashioned way, partly thanks to newer RTX GPUs having specialized
tensor cores that are supposed to be optimized for running AI. They kind of are.
And a new version, DLSS 2.0, was recently rolled out and features a few key improvements. For
starters, DLSS 2.0 aims to provide near native resolution quality while the GPU renders well
under half the pixels it would otherwise need to handle. There are also efficiency improvements to
the neural network that should help it process images faster, ultimately boosting frame rates.
Another big strength is that now the neural network is much more generalized. Instead of
needing to separately train it for every game, NVIDIA now uses more general visual content that's
supposed to be more representative of a variety of different games, which means improvements can
be delivered to users more quickly and more games will end up supporting DLSS. Users can now decide
how much they want to utilize DLSS technology rather than just leaving it completely up to
the GPU and driver. Gamers can now choose between three modes for image quality, prioritizing either
greater image quality or higher frame rates. The benefits of all of this can mostly been seen when
looking at the finer details in a scene, not just at the edges of objects where you traditionally
see jaggies, if the anti-aliasing isn't up to par. Things like text, chain link fences, and
details on faraway buildings should be a lot clearer with DLSS without game breaking slowdowns.
And in some cases, DLSS even appears to be able to make visual elements look more detailed
than they even would be with native resolution, despite rendering fewer pixels from scratch,
if the AI figures out how to enhance a texture or shadow to a greater extent than what the
game's code originally calls for. Of course, with other parts of an image, it might still be
inferior to standard rendering. But the hope is as processing power increases and the algorithm
continues to be fine-tuned, it'll become easier and easier to run more and more games at buttery
smooth frame rates without compromising quality. Thanks for watching. Like, dislike,
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