DLSS vs. FSR: How AI and Tensor Cores are Changing Gaming?

Since their invention, graphic cards have been used for gaming, video editing, and 3D rendering. In recent years, the innovative use of graphic cards in cryptocurrency mining and machine learning has skyrocketed their importance. It also prompted Nvidia’s developers to focus on a gaming-centric application, “upscaling.”

Understanding the upscaling technology and its current development as a gamer can help you squeeze a few more frames per second out of your existing hardware. With buttery smooth frames, you can take your GTA 5 account to new heights. Some top hardware-demanding games, such as The Witcher 3, CyberPunk 2077, and Watch Dog Legion support upscaling technologies. This article will start with the basics of upscaling how AMD and Nvidia have implemented the technology for their hardware. Then why are CPUs not used for upscaling? Finally, a comparison and verdict on which manufacturer has the best upscaling technology.

What is upscaling?

Large 4K monitors and projectors are becoming a part of every gaming setup. On the other hand, the graphic cards needed help to keep up with the rising demand for pixels, but upscaling has eliminated the limitation.

While running your game at a lower resolution, your GPU uses upscaling techniques and analyzes the game’s frame using deep learning techniques. After analyzing each frame, it turns your low-resolution game into a higher resolution as the gamer selects. It can turn a 1440p to a 4K resolution while maintaining the framerates. Running a highly demanding game like CyberPunk 2077, Call of Duty: Warzone, and Rainbow Six Siege on a large 100” projected screen at 100+fps with ray-tracing and 4K resolution is now possible with a mid-range graphics card. Which hardware specification is vital for upscaling and AI-based applications? Let’s have a look in our next section.

What are Tensor cores?

Modern Nvidia graphic cards come with Tensor cores superior to any other AI processing technology. A current RTX -4000 series graphics card has dedicated 500+ Tensor cores for AI enhancement and processing. Due to the economical price, they are famous for AI applications such as image processing, machine learning, and natural language processing.

Performing matrix-related calculations are the specialty of tensor cores. While CUDA cores or equivalents are available in all graphics, Tensor cores are features in the Nvidia RTX series. The popularity of AI chatbots and ChatGPT has paved the way for dedicated Nvidia GPUs like the H100 Tensor Core GPU, which is ideal for AI applications. Modern RTX series graphics cards are enough for gaming to power through those extra frames required for large screens. Two methods can primarily enhance the output of a graphics card DLSS (Deep Learning Super Sampling) and FSR (AMD Fidelity FX Super Resolution). Let’s learn what they are and compare them.

DLSS and FSR: A Comparison

There are currently three main competitors in gaming graphic card processors, i.e., Nvidia, AMD, and Intel. Although they all support the upscaling technique, each implements it through different software. Nvidia calls it DLSS, AMD calls it FSR, and Intel calls it XESS. XESS needs to catch up in performance compared to other upscaling strategies. Hence, we will only compare DLSS and FSR.

1. Performance

Generally, FSR performs better in terms of frames per second. FSR does not require dedicated hardware or Tensor cores for operation. Any GPU can run FSR, including an Nvidia RTX or Intel. Due to superior hardware with CUDA cores, RTX cards perform better on FSR than Radeon.

FSR should be your go-to option if you require higher or stable framerates at 4K resolution. However, one might have to look elsewhere for image quality.

2. Image quality

Casual gamers tend to look for stunning visuals rather than ultra-high framerates. Compared to FSR, a scaled-up game version from 1440p to 4K will always look visually spectacular on the DLSS technology. It’s primarily because of the extent of AI implementation and RTX graphics card hardware-based acceleration.

DLSS uses a deep learning algorithm already trained on a large dataset of images. It efficiently understands the object in a set of blurred pixels and converts them into a refined high-resolution image through Tensor cores. The one thing that primarily differentiates FSR from DLSS is the ability to introduce new frames while upscaling the graphics.

3. Compatibility

AMDs FSR is way ahead in compatibility compared to DLSS. There is no need for Tensor cores to power an FSR software-based upscaling. Intel or aftermarket GPUs can equally utilize the upscaling capabilities offered by AMD.

If we talk about hardware acceleration, there FSR does not support it. There is still a need for improvement in AMD Radeon graphics cards, and they are also way behind Nvidia in AI-based applications such as deep learning and natural language processing.

Which Upscaling Technology is Best?

Each has its specific advantages, as described in the previous section. FSR offers some unique features to dive deeper, but DLSS does not. The extent of scaling in Nvidia is lower than FSR. A user can scale a 720p resolution to 4K for a large projector screen with good quality. Whereas on DLSS, the resolutions are minimal.

For programmers and specialists, FSR is an open-source code that is readily available. Experts can create their version of FSR with better upscaling. A casual gamer probably inclines toward DLSS, whereas a competitive game will prefer FSR. Overall, Nvidia DLSS takes the crown. To summarize the differences, have a look at the table below:

Feature	FSR	DLSS
Open-source	Yes	No
Spatial Upscaling	Yes	Yes
Temporal Upscaling	No	Yes
Available Resolutions	720p to 4K	1080p, 1440p, 2160p
Image Quality	Good	Excellent
Performance	Excellent	Excellent

Why is a GPU good in DLSS/FSR than a CPU?

GPUs are equipped with a higher number of smaller cores compared to CPUs. It makes them suitable for performing highly mathematical and geographical calculations for mining and deep learning. A GPU can utilize its thousands of cores to process massive data through parallel processing, whereas a single CPU only has 16 to 32 cores maximum.

DLSS, FSR, and XESS use deep learning techniques that mimic how the human brain processes information. The artificial neural network extracts data from images, graphics, videos, etc., to reach a logical conclusion without human need.

Is DLSS or FSR good for online and competitive gaming?

If you are into casual online games like Grand Theft Auto 5, participating in heists while streaming your game online will need quality video and high framerates. In the case of streaming, DLSS can be a better choice. On the other hand, if you are interested in making your GTA game account worthy of selling later, you will need high framerates, for which FSR is better.

In competitive gaming, FSR is better than DLSS. The susceptible nature of Nvidia’s technology to blurring, ghosting, and shimmering can lead to confusing visuals that can deflect gamers’ attention. When your reputation is on the line, choosing a more secure AMD software scaling is best.

Final Verdict

Games are rapidly adopting the latest Unreal Engine, Unity, and CryEngine, leaving graphics hardware behind. Due to the crypto mining industry, the graphic card shortage led to empty shelves and sky-high prices. Nvidia tackled it with LHR (Low Hash Rate) limits and introduced graphic cards with low hardware specifications and DLSS technology. Nvidia enabled thousands of gamers to have gaming hardware of their own.

To summarize, DLSS and FSR have their specific advantages. Technology is here to stay, and the future looks bright for AI. It makes hardware affordable and gaming graphics more visually stunning. AI is the future, and it’s already enhancing gaming.