Nvidia DLSS 4: Debunking Myths About Frame Generation and AI

Nvidia's DLSS 4: Debunking the Myths and Understanding the Technology

Nvidia's recent announcement of DLSS 4 at CES 2025, alongside the RTX 5090, has generated significant buzz and, as is often the case with new technology, a fair amount of misunderstanding. This article aims to clarify how DLSS 4 actually works, addressing common misconceptions about its capabilities, particularly regarding "predicting the future" and its impact on latency.

The "Predicting the Future" Misconception

Nvidia CEO Jensen Huang's statement that DLSS 4 "predicts the future" rather than "interpolating the past" has led to confusion. While a catchy phrase for a general audience, it's technically inaccurate. DLSS 4, like its predecessor DLSS 3 and other frame generation technologies such as AMD's FSR 3 and Lossless Scaling, utilizes frame interpolation. This process involves rendering two frames and then using an algorithm to calculate and generate an intermediate frame based on the differences between the two. This creates the illusion of smoother motion and higher frame rates.

While research into frame extrapolation (a more advanced technique) is ongoing, current evidence and industry sources confirm that DLSS 4 relies on frame interpolation. This doesn't diminish its capabilities but rather provides a more accurate understanding of its underlying mechanism.

Understanding DLSS 4's Latency

A common concern is that frame interpolation, especially with multiple generated frames as in DLSS 4, significantly increases input latency. While frame interpolation does introduce a small amount of latency due to the rendering and calculation process, it does not increase linearly with the number of interpolated frames.

For instance, if a game runs at 60 frames per second (16.6ms per frame), DLSS 3 might double the output to 120 fps, but the latency between rendered frames remains 16.6ms. Similarly, DLSS 4, even when generating three additional frames, still operates on the same fundamental principle of interpolating between two rendered frames. The added latency is largely consistent, primarily stemming from the initial frame buffering and the overhead of the DLSS process itself, rather than a compounding effect of each interpolated frame.

Digital Foundry's testing supports this, indicating that while there's an initial latency increase, adding more intermediate frames has a minimal impact. The core latency remains tied to the base frame rate and the DLSS overhead. Nvidia's Reflex technology, while beneficial, is not strictly required for DLSS 4, similar to DLSS 3, as the fundamental latency characteristics are managed by the base frame rate and the interpolation process.

A New AI Model: Vision Transformers

DLSS 4 represents a significant departure from DLSS 3 not just in its multi-frame generation but also in its underlying AI architecture. Nvidia has moved away from its previous Convolutional Neural Network (CNN) model to a vision transformer model. This new model incorporates several key advancements:

• Self-Attention: This allows the AI to track the importance of different pixels across multiple frames, enabling better focus on problematic areas like thin details that might otherwise shimmer.
• Scalability: Transformer models are more scalable, allowing Nvidia to significantly increase the number of parameters. DLSS 4's transformer model reportedly has double the parameters of the previous CNN approach.

These improvements are expected to lead to better stability and preservation of fine details compared to the older CNN method. Importantly, these benefits will be available to all RTX graphics cards, not just the new RTX 50-series, for features supported by their respective generations.

The Future of DLSS

While DLSS 4's capabilities are impressive, its true performance and impact will be fully realized upon the launch of Nvidia's next-generation GPUs. Early demonstrations and analyses suggest a significant leap forward in AI-driven graphics enhancement. The shift to a vision transformer model signifies a more robust and adaptable AI framework for future iterations of DLSS, promising even greater detail, stability, and performance gains in the evolving landscape of PC gaming.

Key Takeaways:

• DLSS 4 uses frame interpolation, not future prediction.
• Latency increases are minimal and not linearly dependent on the number of generated frames.
• A new vision transformer AI model offers improved detail and stability.
• DLSS 4 benefits will be accessible across various RTX cards.

This evolution in DLSS technology underscores Nvidia's commitment to pushing the boundaries of visual fidelity and performance in gaming.