Nvidia RTX DLSS: Everything you need to know

Nvidia RTX DLSS: Everything you need to know

Alongside the fastest graphics cards ever built for consumers, Nvidia’s Turing generation of GPUs also made possible some intriguing new features for gamers everywhere. Ray tracing is the easiest to wrap your head around, but deep learning supersampling, or DLSS, is a little more nebulous.

Even if it’s more complicated to understand though, DLSS has the potential to be the greatest feature of Nvidia’s 2000-series graphics cards, improving visuals and increasing performance in the same breath. To help you understand just how it works, here’s our guide to everything you need to know about Nvidia’s RTX DLSS technology, so you can decide whether it’s enough of a reason to upgrade to a new RTX, or even RTX Super GPU.

What is DLSS?

Deep learning super sampling uses artificial intelligence and machine learning to produce an image that looks like a higher-resolution image, without the rendering overhead. Nvidia’s algorithm learns from tens of thousands of rendered sequences of images that were created using a supercomputer. That trains the algorithm to be able to produce similarly beautiful images, but without requiring the graphics card to work as hard to do it.

DLSS also incorporates more traditional beautifying techniques like anti-aliasing to create an eventual image that looks like it was rendered at a much higher resolution and detail level, without sacrificing frame rate.

Where it originally launched with little competition, though, other sharpening techniques from both AMD and Nvidia itself now compete with DLSS for mindshare and effective utilization in 2020.

What does DLSS actually do?

DLSS is the end result of an exhaustive process of teaching Nvidia’s A.I. algorithm to generate better-looking games. After rending the game at a lower resolution, DLSS infers information from its knowledge base of super-resolution image training, to generate an image that still looks like it was running at a higher resolution. The idea is to make games rendered at 1440p look like they’re running at 4K, or 1080p games to look like 1440p.

More traditional super-resolution techniques can lead to artifacts and bugs in the eventual picture, but DLSS is designed to work with those errors to generate an even better-looking image. It’s still being optimized, and Nvidia claims that DLSS will continue to improve over the months and years to come, but in the right circumstances, it can deliver substantial performance uplifts, without affecting the look and feel of a game.

Where early DLSS games like Final Fantasy XV delivered modest frame rate improvements of just five to 15 FPS, more recent releases have seen far greater improvements. With games like Deliver us the Moon, and Wolfenstein: Youngblood, Nvidia introduced a new A.I. engine for DLSS, which we’re told improves image quality, especially at lower resolutions like 1080p, and can increase frame rates in some cases by over 50%.

There are also new quality adjustment modes that DLSS users can make, picking between Performance, Balanced, and Quality, each focusing the RTX GPU’s Tensor core horsepower on a different aspect of DLSS.

This is a good thing, too, as early implementations of DLSS weren’t so subtle. In our testing of Metro Exodus, we found that both ray tracing and DLSS required some sacrifice — framerate and detail, respectively. Enabling them both at the same time left behind a strangely blurred image that detracted from the graphical gains made by ray tracing in the first place. In the end, turning both off leads to a better overall game experience.

How does DLSS work?

DLSS forces a game to render at a lower resolution (typically 1440p) and then uses its trained A.I. algorithm to infer what it would look like if it were rendered at a higher one (typically 4K). It does this by utilizing some anti-aliasing effects (likely Nvidia’s own TAA) and some automated sharpening. Visual artifacts that wouldn’t be present at higher resolutions are also ironed out and even used to infer the details that should be present in an image.

As Eurogamer explains, the A.I. algorithm is trained to look at certain games at extremely high resolutions (supposedly 64x supersampling) and is distilled down to something just a few megabytes in size, before being added to the latest Nvidia driver releases and made accessible to gamers all over the world. It’s something that must be done on a game by game basis.

In effect, DLSS is a real-time version of Nvidia’s screenshot-enhancing Ansel technology. It renders the image at a lower resolution to provide a performance boost, then applies various effects to deliver a relatively comparable overall effect to raising the resolution.

The end result can be a mixed bag but in general, it leads to higher frame rates without a substantial loss in visual fidelity. Nvidia claims frame rates can improve by as much as 75% in Remedy Entertainment’s Control when using both DLSS and ray tracing. It’s usually less pronounced than that, and not everyone is a fan of the eventual look of a DLSS game, but the option is certainly there for those who want to beautify their games without the cost of running at a higher resolution.

Useful, but far from perfect

Deep learning supersampling has the potential to give gamers who can’t quite reach comfortable frame rates at resolutions above 1080p, the ability to do so with inference. DLSS could end up being the most impactful feature of Nvidia’s RTX Turing cards. They aren’t as powerful as we might have hoped and the ray-tracing effects are pretty but tend to have a sizable impact on performance, but DLSS could give us the best of both worlds: Better-looking games that perform better, too.

The best place for this kind of technology could be in lower-end cards but unfortunately, it’s only supported by RTX graphics cards, the weakest of which is the RTX 2060 — a $300 card. If Nvidia made this technology available on GTX GPUs, it might find more success for it.

The real problem, though, is that the list of supported games is still limited, totaling just 10 as of early 2020. Although that may change, there’s little suggestion as yet that DLSS will see widespread adoption. With limited growth for DLSS support over the past couple of years too, it’s possible that image sharpening techniques offered by AMD will prove more popular too, as they don’t have any kind of hardware restrictions.

It could be that in a year or two DLSS is a commonplace feature in most games due to its ease of implementation and the dominance of RTX GPUs in gamer systems. But if game developers don’t implement DLSS en masse, it may end up as something far more niche and unsupported. It could end up like the often (surprisingly) controversial Nvidia Hairworks, which is nice to have, but not a must-have feature.

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Published at Fri, 14 Feb 2020 23:03:32 +0000