Announced during CES 2025, NVIDIA’s GeForce RTX 50 Series, powered by the Blackwell architecture, isn’t just another iterative step forward—it’s a fundamental shift in how graphics, AI, and gaming performance are handled at a hardware level with NVIDIA dubbing this generation the start of neural rendering. The RTX 50 Series doesn’t merely throw more raw power at rendering problems; instead, it introduces entirely new ways to process visuals and leverage AI, taking GPU design in a direction that was unimaginable just a few years ago.
With the GeForce RTX 50 series, powered by NVIDIA’s Blackwell architecture, we are witnessing a fundamental transformation in GPU computing. Instead of just increasing performance, NVIDIA has redesigned core GPU functionality, making AI an integral part of graphics processing rather than just an add-on. Technologies like DLSS 4, Mega Geometry, RTX Neural Shaders, and enhanced AI-driven ray tracing are not simply evolutionary improvements; they are rewriting the rules of real-time rendering.
So what exactly makes Blackwell the most advanced gaming GPU architecture ever created? How does DLSS 4 use Transformer models instead of CNNs? Why does Mega Geometry make fully ray-traced open-world games feasible? During CES 2025, NVIDIA held an editor’s session for select members of the press for a deeper dive into Blackwell and its new technology stack making this all happen. Let’s break it all down, with a focus on the new core technologies that enable these revolutionary features in quick summary of the new Blackwell tool kit for the GeForce RTX 50 series cards.
Blackwell Architecture: A Fundamental Shift in GPU Design
Every major GPU generation brings incremental improvements, but Blackwell is not just another step forward—it’s a radical shift in how graphics, AI, and ray tracing workloads are handled. While Ada Lovelace introduced DLSS 3 and improved ray tracing, Blackwell pushes the AI-first approach even further, making AI an integral part of how pixels are drawn, how frames are generated, and how light is simulated.
The AI Management Processor (AMP): A Smarter Way to Handle AI Workloads
At the heart of Blackwell’s design is the AI Management Processor (AMP), a dedicated controller that allows multiple AI models to run in parallel without interfering with rendering.
Think of AMP as a dedicated AI scheduler that ensures real-time upscaling, frame generation, AI-driven physics, and neural materials all execute without disrupting traditional rasterization and ray tracing tasks. Previously, AI workloads were handled through Tensor Cores, but had to share resources with shader execution and rasterization pipelines. With AMP, DLSS 4’s Transformer models, AI-based texture upscaling, and physics simulations can operate independently—allowing for massive AI-driven enhancements with zero impact on performance.
5th-Gen Tensor Cores: FP4 Precision and the Neural Processing Revolution
Tensor Cores are NVIDIA’s specialized AI accelerators, first introduced with Turing (RTX 20 Series) to handle deep learning operations at high speed. With Blackwell, we now have 5th-Gen Tensor Cores that introduce FP4 (4-bit floating point), a breakthrough in AI efficiency.
To understand why FP4 matters, we need to break down how AI inference works. AI models process vast amounts of floating point numbers, which are decimal-based computations used for high-precision math. More bits mean greater accuracy, but also higher memory usage and lower efficiency. AI doesn’t always need full 32-bit precision, which is why FP16 (16-bit floating point) became standard for machine learning.
With Ampere (RTX 30) and Ada Lovelace (RTX 40), NVIDIA introduced FP8, cutting memory requirements in half while maintaining AI accuracy. Now, Blackwell halves that again with FP4, allowing AI inference to run at double the speed of FP8, while using even less memory.
This has huge implications for AI-enhanced gaming features, including:
- DLSS 4’s Transformer-based upscaling runs at twice the efficiency of Ada Lovelace’s CNN-based models.
- RTX Neural Shaders can apply AI-generated materials in real-time with lower computational cost.
- AI-driven physics simulations can be applied to thousands of objects without degrading performance.
Simply put, FP4 makes AI processing so fast and efficient that it can be integrated into nearly every aspect of rendering, not just upscaling.
Fourth-Gen RT Cores: The Biggest Leap in Ray Tracing Performance
Ray tracing has always been a performance bottleneck, because it requires GPUs to calculate the path of every light ray in a 3D environment. Until now, real-time ray tracing was limited by how quickly GPUs could process these complex light interactions.
The 4th-Gen RT Cores in Blackwell introduce three key advancements that significantly accelerate path tracing and real-time global illumination:
1. Triangle Cluster Intersection Engines: Reducing Ray Processing Overhead
Traditional ray tracing calculates individual ray-triangle intersections, meaning every object in a scene needs to be processed separately. Blackwell changes this by introducing Triangle Cluster Intersection Engines, which group multiple triangles into clusters, allowing the RT Cores to process them in parallel rather than one at a time.
This results in:
- 8x faster ray-triangle intersection rates
- Significantly reduced processing overhead in complex environments
- Massive improvements to path tracing in open-world games
2. Shader Execution Reordering (SER) 2.0: Fixing Ray Tracing Bottlenecks
Shader Execution Reordering (SER) was first introduced in Ada Lovelace, but Blackwell enhances it with SER 2.0, making it twice as effective at reducing ray tracing workload inefficiencies.
To illustrate why SER is crucial, imagine a sorting facility handling millions of packages. If packages are sorted in a random, inefficient order, workers must jump between tasks inefficiently, leading to delays. Traditional ray tracing has a similar problem, as shaders execute rays in an unpredictable order, causing performance slowdowns.
SER 2.0 dynamically reorganizes workloads to ensure shaders process rays in the most efficient sequence possible, leading to:
- Faster BVH traversal (Bounding Volume Hierarchies, used in ray tracing)
- Lower latency and better performance in path-traced lighting scenarios
DLSS 4: Transformers Replace CNNs for Superior AI Rendering
DLSS has been a game-changer since its introduction in 2018, but DLSS 4 represents the biggest evolution yet. The biggest changes in DLSS 4 are:
- Multi Frame Generation: DLSS 4 can generate up to three extra AI frames per rendered frame, compared to DLSS 3’s single-frame generation.
- Transformer-Based AI Models: DLSS 4 replaces CNNs with Transformers, allowing for more stable image reconstruction, higher motion fidelity, and reduced artifacts.
Why Transformers are Superior to CNNs in DLSS 4
Previous versions of DLSS used CNNs (Convolutional Neural Networks) to enhance and upscale frames. However, CNNs process data in small spatial regions, meaning they lack a full-frame understanding of motion over time. This led to ghosting, flickering, and image instability.
Transformers, on the other hand, process the entire image holistically. They use attention mechanisms to analyze both spatial and temporal information across multiple frames, resulting in:
- Better motion stability (no flickering or ghosting)
- Higher detail retention in fast-moving objects
- Superior reconstruction of lighting and fine details
DLSS 4, combined with Multi Frame Generation, enables 8x performance improvements in path-traced games, making 8K 120Hz gaming with full ray tracing a reality.
Final Thoughts: The Most Advanced GPU Yet… but not conventionally
The GeForce RTX 50 is a very polarizing release and it’s going to be a very noisy launch for NVIDIA yet possibly a good one as it faces no competition aside from itself and its RTX 40-series to contend with. The Blackwell GPU has already been expected to be a massive shift when it lands on GeForce and it does arrive with a splash but not as one would want.
But as much as everyone would want gen-on-gen performance uplift to get two-folds, technology as a whole isn’t seeing this pattern recently if any of the new CPUs, GPUs, mobile phones, and pretty much everything else is to serve as an example of this pattern.
For its technology, the RTX 50 series is promising but with prohibitive pricing becoming higher and higher, PC gaming has been out of reach of audiences which the PC space has been gladly handing over to the mobile market or the budding handheld gaming PC segment.
Regardless, neural rendering is a method favoring NVIDIA and this is their product after all. Should AMD come out with a faster traditional raster and that’s more you’re tea, then we’ll just have to see. For now, 1:1 the RTX 50 series is a parallel upgrade in some form to the RTX 40 series with the RTX 5090 being the only exception.
