In 2018, NVIDIA introduced the breathtaking DLSS. This app stands for deep learning supersampling. This is a nice way to make video games look fun. Since time has passed, DLSS has improved with each new version, and NVIDIA thinks that in the future, DLSS 10 is able to make graphics look great with artificial intelligence.

We had a talk about AI in video games arranged by Digital Foundry not long ago. They spoke with Bryan Catanzaro from NVIDIA who is a big-mom and knows a lot about deep learning. They asked her what she thought would happen with DLSS in the future and how they can make things even cooler.

DLSS 10 can play with an autonomous network.

In 2018, during the NeurIPS conference, Bryan Catanzaro and his team at the NVIDIA drew an impressive demonstration. They showed a virtual world that is entirely developed by a neural network and with a unique twist: a game engine can lead the whole process.

The game engine was intended for simplicity. It showed that objects are located in the virtual world. It was then fed these data into a neuronal network, which was responsible for rendering the whole scene. The neural network handled every aspect of the rendering of the graphics. The visionary achievement of Catanzaro and his team was to achieve results in real time with this innovative approach in 2018.

Catanzaro mentioned that the quality of the rendered games was just as good as Cyberpunk 2077. Nevertheless, he believes that this is the direction the graphics industry will take in the long run.

DDR technologies made a very long difference after its introduction to the RTX 20-series GPUs. In the beginning, people couldn’t see how the tensor cores were included in the gaming GPUs. The first games were a great deal. But DLSS 2.X made a lot of progress, while giving it more valuable and practical value. It became more popular and even inspired a lot of innovative techniques such as FSR2 and XeSS, which were later introduced by other companies.

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Generative AI becomes increasingly important in graphics. The rationale behind this shift is compatible with the broad application of AI in many fields. It’s about leveraging AI’s ability to learn complex functions by analysing vast data sets. Instead of trying from scratch to build algorithms, it’s about exploiting the elaboration of AI data.

In about ten years, DLSS can replace games as a whole. NVIDIA works on new tricks such as radial caching and texture compression that could be added to DLSS to do even more work. But to do that, they might need to put more special cores into their GPUs.

Can DLSS 10 take over GPUs in the gaming industry?

Deep Learning Super Sampling (DLSS), which helps your computer with graphics card performance, helps you improve game visuals, while gaining knowledge and technique.

In the future, DLSS may become even more smarter, allowing it to enhance game visuals without having to have a touch of a touch. Nevertheless, the graphics card won’t completely replace it, because the graphics card remains the primary driver of higher graphics. The DLSS helps us reduce the printing costs rather than replace the graphics card.

In view of the future of the GPUs in light of this advanced technology, DLSS 10 can really be useful in enhancing gaming graphics while still relying on GPUs. It is working with GPUs to improve their performance, thereby achieving stunning visuals with low power consumption. The GPU will still be indispensable when it comes to tasks such as rendering and running games, but not for heavy-duty tasks. It won’t replace GPUs in the future, while DLSS 10 can improve performance on the GPU.

Source: Neowin