What is DLSS Nvidia And How Does it Improve Your Gameplay? Get Easy Details Here

Do you know what makes Nvidia DLSS stand out from others? Well, here it is- Nvidia’s RTX graphics cards come with two important features and highlights that make them stand out from their GTX-series predecessors. Let me tell you that the first one is the very well-documented Ray Tracing, however, the second one, known as DLSS, remains largely ambiguous for mainstream gamers. So, if you have landed in this article, you will through this article, get to know about Nvidia DLSS like what is the definition, what it does, and how it can help improve your gameplay.

In this article, we are going to discuss Nvidia DLSS in detail and will tell you exactly how it boosts up your gaming experience with GeForce RTX-series graphics cards. So now without any further delay, let’s dive right into our main topic.

What is Nvidia DLSS?

DLSS, also known as Deep Learning Super Sampling, is Nvidia’s AI-based upscaling algorithm that utilizes dedicated Tensor Cores on RTX cards to increase frame rates without compromising the quality of the image. The technology uses the power of AI to boost in-game FPS with graphically intensive workloads. You know what, gamers can utilize high graphics settings and higher resolutions when DLSS turned on while still maintaining impressive framerates. Let me tell you another thing that Nvidia said that this technology boosts images with quality same as that of rendering the image natively in the higher-resolution, however, with less computation done by the video card permitting for higher graphical settings, and frame rates for a given resolution.

The official website of Nvidia has described DLSS as a “groundbreaking AI rendering technology that increases graphics performance using dedicated Tensor Core AI processors on GeForce RTX GPUs.” If the high-tech company is to be believed, “DLSS taps into the power of a deep learning neural network to boost frame rates and generate beautiful, sharp images for your games.”

How Does DLSS Work?

In this section, we are going to talk about how DLSS works. In order to put DLSS into action, first of all, the AI algorithm will extract several aliased frames from the target game. After that, it will generate a matching perfect frame by utilizing either super-sampling or accumulation rendering. These combined or paired frames are then taken care of on a PC, which prepares the DLSS model to recognize aliased inputs and create top-notch anti-aliased images that match the ‘perfect frame’ as closely as could be expected.

After the above-mentioned step, the process is repeated to prepare the AI model to create extra pixels rather than simply applying anti-aliasing to frames. That further helps boost up the resolution of the input. Joining both of these techniques enables the GPU to deliver the game in full resolution at higher FPS. The AI algorithm then intelligently ‘learns’ about a game as you play and gets better with time.

Advantages of DLSS: How Does DLSS Improve Gameplay?

DLSS utilizes progressed AI rendering to create picture quality that Nvidia claims is comparable to native resolution. The organization additionally says that the innovation is even fit for delivering ‘better image resolution’ while just traditionally rendering a small amount of the pixels. Apart from that, the Advanced temporal feedback techniques provide sharper images, finer details, and improved stability from frame to frame.

You know what, DLSS runs on dedicated Tensor Cores, which push up frame rates and give the headroom required to maximize graphics settings and resolution, even up to an incredible 8K. With DLSS 2.0 in the RTX 30-series cards, the fresher or newer and faster AI model all the more productively utilizes Tensor Cores to execute 2x quicker than the original. This further develops frame rates and eliminates limitations on supported GPUs, settings, and goals. It is because of the new technology, which offers an overall solution, so the AI model does not have to prepare for each game independently.

Now, a question arises: why is DLSS Not Available for All Resolutions?

You know what, the results of DLSS may differentiate from one game to another due to each game having unique and different characteristics which are based on the game engine and the time spent on training the AI model. But, truth to be told, it is not available under all conditions and at all resolutions. Want to know why? Well, because there are times when it is just faster and qualitatively better to utilize the native, non-AI technology to render frames.

In case your game is already running at high frame rates, then the time of the GPU’s frame rendering will be shorter than the DLSS execution time. In such cases, deep learning super sampling (DLSS) is not available because it would not boost your frame rate.

Indeed, sometimes, it would reduce rendering times, thereby influencing gameplay. But, in case the game is already taxing the GPU heavily, DLSS gives an optimal performance boost. You can wrench up your settings to boost your FPS gains.

Explaining the technicality, Nvidia on its official website said, “DLSS requires a fixed amount of GPU time per frame to run the deep neural network. Thus, games that run at lower frame rates or higher resolutions, benefit more from DLSS. For games running at high frame rates or low resolutions, DLSS may not boost performance.”

Nvidia further added on its website, “When your GPU’s frame rendering time is shorter than what it takes to execute the DLSS model, we don’t enable DLSS. We only enable DLSS for cases where you will receive a performance gain. DLSS availability is game-specific, and depends on your GPU and selected display resolution.”

Drawbacks of DLSS:

Well, bad things come with good things and DLSS is no different. You know what, the DLSS 2.0 is a great boost or improvement over the 1st-gen release. But, truth to be told, it is still not ideal, and complaints about lag and blurry frames continue to come in Nvidia forums, online message boards, and social media.  The issue is especially visible at lower resolutions, where the additional frames seem to come at the expense of image sharpness.