![]() 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. In effect, DLSS is a real-time version of Nvidia’s screenshot-enhancing Ansel technology. In DLSS 2.0, Nvidia provides a general solution, so the AI model no longer needs to be trained for each game. Originally, Nvidia had to go through this process on a game-by-game basis. 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.Īs Eurogamer explains, the AI 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 does this by utilizing some anti-aliasing effects (likely Nvidia’s own TAA) and some automated sharpening. We’ll discuss DLSS 3 in greater detail later.ĭLSS forces a game to render at a lower resolution (typically 1440p) and then uses its trained AI algorithm to infer what it would look like if it were rendered at a higher one (typically 4K). Previous implementations of DLSS just had the Tensor cores make frames look better, but now frames can be rendered using just AI. With the latest iteration of DLSS 3, the frame rate gains might be even more substantial thanks to the new frame-generation feature. With games like Deliver us the Moon and Wolfenstein: Youngblood, Nvidia introduced a new AI 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%. Where early DLSS games like Final Fantasy XV delivered modest frame rate improvements of just 5 frames per second (fps) to 15 fps, more recent releases have seen far greater improvements. In the right circumstances, it can deliver substantial performance uplifts without affecting the look and feel of a game on the contrary, it can make the game look even better. 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. ![]() ![]() DLSS 2.0 offers four times the resolution, allowing you to render games at 1080p while outputting them at 4K. The idea is to make games rendered at 1440p look like they’re running at 4K or 1080p games to look like 1440p. After rendering 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. What does DLSS actually do? Image used with permission by copyright holderĭLSS is the result of an exhaustive process of teaching Nvidia’s AI algorithm to generate better-looking games. ![]() Even Intel has its own supersampling technology called Intel XeSS, or Intel Xe Super Sampling. Nvidia is leading the charge in this area, though AMD’s new FidelityFX Super Resolution feature could provide some stiff competition. Thanks to the new 8-bit floating point tensor engine, the cores have had their throughput increased by as much as five times compared to the previous generation. This makes the DLSS boost even more powerful. ![]() Nvidia’s newest graphics cards from the RTX 40-Series lineup bring the Tensor cores up to their fourth generation. Although RTX 20 series GPUs have Tensor cores inside, the RTX 3060, 3060 Ti, 3070, 3080, and 3090 come with Nvidia’s second-generation Tensor cores, which offer greater per-core performance. This is all possible thanks to Nvidia’s Tensor cores, which are only available in RTX GPUs (outside of data center solutions, such as the Nvidia A100). NVIDIA DLSS - Image Processing Algorithm vs. ![]()
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