What is a Tensor Core? Explanation of Nvidia's GPU technology.
Have you ever wondered what drives your RTX graphics card?
Tensor Cores are hardware units specifically developed within Nvidia graphics cards and are designed to accelerate computational tasks that require artificial intelligence, such as deep learning. These cores enable mixed-precision computing, allowing them to handle operations that would take significantly more time on regular CUDA cores found in graphics cards.
The history of Tensor Cores began with Nvidia's Volta line in 2017, initially used in data centers and later incorporated into graphics cards. The first GPUs to feature this technology were the Nvidia Titan V and the Nvidia Quadro GB100, which utilized 640 first-generation Tensor Cores limited to 16-bit floating-point (FP16) operations. The second generation of Tensor Cores was introduced with the Turing series graphics cards, which expanded precision capabilities. This generation pioneered the integration of ray tracing cores, enabling real-time lighting techniques in video games.
The applications for these powerful units are not limited to gaming, as they are also used in workstations for CAD tasks, content creation, and scientific calculations. With the move towards the Ampere series (RTX 30), even more advanced computing capabilities were introduced, including new enhanced precision formats that are ideal for machine learning.
As Nvidia progressed towards a focus on artificial intelligence software, the fourth-generation Tensor Cores enabled features such as frame generation through DLSS 3, exclusive to the Ada series (RTX 40). This innovation allows developers to enhance the graphical performance of their games through dynamic analysis and generation of additional frames.
The recent introduction of graphics cards based on the Blackwell architecture, which include fifth-generation Tensor Cores, promises a significant advancement in graphical capabilities, offering up to 30 times more performance than previous generations. This development is considered crucial for gaming at 8K and 4K, as it allows RTX to generate playable frames at high resolutions while executing complex rendering techniques like ray tracing.
Tensor Cores are, ultimately, essential components within Nvidia graphics cards, as they enable the acceleration of a variety of processes necessary for artificial intelligence, facilitating tasks ranging from gaming to training deep learning models. Their evolution from 2017 to 2025 demonstrates how technology has advanced in terms of speed and efficiency, with applications that continue to expand in the realm of modern computing.