At the RTX "Blackwell" Editor's Day during CES 2025, NVIDIA pulled back the curtain on one of its most powerful tools: a dedicated supercomputer that has been continuously improving DLSS (Deep Learning Super Sampling) for the past six years. Brian Catanzaro, NVIDIA's VP of applied deep learning research, disclosed that thousands of the company's latest GPUs have been working round-the-clock, analyzing and perfecting the technology that has revolutionized gaming graphics. "We have a big supercomputer at NVIDIA that is running 24/7, 365 days a year improving DLSS," Catanzaro explained during his presentation on DLSS 4. The supercomputer's primary task involves analyzing failures in DLSS performance, such as ghosting, flickering, or blurriness across hundreds of games. When issues are identified, the system augments its training data sets with new examples of optimal graphics and challenging scenarios that DLSS needs to address.

DLSS 4 is the first move from convolutional neural networks to a transformer model that runs locally on client PCs. The continuous learning process has been crucial in refining the technology, with the dedicated supercomputer serving as the backbone of this evolution. The scale of resources allocated to DLSS development is massive, as the entire pipeline for a self-improving DLSS model must consist of not only thousands but tens of thousands of GPUs. Of course, a company making 100,000 GPU data centers (xAI's Colossus) must save some for itself and is proactively using it to improve its software stack. NVIDIA's CEO Jensen Huang famously said that DLSS can predict the future. Of course, these statements are to be tested when the Blackwell series launches. However, the approach of using massive data centers to improve DLSS is quite interesting, and with each new GPU generation NVIDIA release, the process is getting significantly sped up.