# Glossary of NVIDIA GPU Terminology

Term | Definition | Importance
--- | --- | ---
CUDA Core | The standard NVIDIA parallel processing unit. | YES. The number of CUDA cores is the standard measure of NVIDIA GPU processing. The more CUDA cores, the faster the VisionPro Deep Learning processing and training.
ECC Memory | Error correcting code (ECC) memory involves hardware support for verifying that memory read/writes do not contain errors. | NO. Due to the huge number of computations involved in training and processing neural networks, the likelihood of a memory error affecting a tool result is very low.
TCC | Tesla Compute Cluster (TCC) driver mode A high-performance driver that is optimized for computational use of an NVIDIA GPU. TCC considerations: Not supported by all cards. Disables video output from the card. Diminishes or eliminates the advantages of using Deep Learning optimized memory. Configured using the NVIDIA-SMI utility. | YES. Whenever possible, you should select cards that support the TCC driver mode, and they should be enabled.
Tensor Core | Full-precision, mixed-precision (and eventually integer math) parallel processing unit that is dedicated to matrix multiply operations. | YES. Introduced in version VisionPro Deep Learning 3.2.0, VisionPro Deep Learning automatically takes advantage of Tensor Cores for faster processing and training, as long as the user has a Standard or Advanced license.
Tensor RT | The NVIDIA framework for optimizing (by using low-precision and integer math) runtime performance of TensorFlow, Caffe and other standard framework networks running on a GPU with Tensor Cores. | NO. VisionPro Deep Learning uses a proprietary network architecture that is not compatible with Tensor RT.