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When NVIDIA launched the second iteration of its Deep Learning Super Sampling (DLSS) technique used to upscale lower resolutions using deep learning, everyone was impressed by the quality of the rendering it is putting out. However, have you ever wondered how it all looks from the developer side of things? Usually, games need millions of lines of code and even some small features are not so easy to implement. Today, thanks to Tom Looman, a game developer working with Unreal Engine, we have found out just how the integration process of DLSS 2.0 looks like, and how big are the performance benefits coming from it.
In the blog post, you can take a look at the example game shown by the developer. The integration with Unreal Engine 4.26 is easy, as it just requires that you compile your project with a special UE4 RTX branch, and you need to apply your AppID which you can apply for at NVIDIA's website. Right now you are probably wondering how is performance looking like. Well, the baseline for the result was TXAA sampling techniques used in the game demo. The DLSS 2.0 has managed to bring anywhere from 60-180% increase in frame rate, depending on the scene. These are rather impressive numbers and it goes to show just how well NVIDIA has managed to build its DLSS 2.0 technology. For a full overview, please refer to the blog post.
View at TechPowerUp Main Site
In the blog post, you can take a look at the example game shown by the developer. The integration with Unreal Engine 4.26 is easy, as it just requires that you compile your project with a special UE4 RTX branch, and you need to apply your AppID which you can apply for at NVIDIA's website. Right now you are probably wondering how is performance looking like. Well, the baseline for the result was TXAA sampling techniques used in the game demo. The DLSS 2.0 has managed to bring anywhere from 60-180% increase in frame rate, depending on the scene. These are rather impressive numbers and it goes to show just how well NVIDIA has managed to build its DLSS 2.0 technology. For a full overview, please refer to the blog post.
![](https://www.techpowerup.com/img/awQfPeTMfCD84zyQ_thm.jpg)
View at TechPowerUp Main Site