Tensor Cores are specialized hardware units embedded in GPUs (Graphics Processing Units) designed to accelerate operations on tensors — multi-dimensional arrays of numbers. First introduced by NVIDIA in the Volta architecture, Tensor Cores have since become a core feature in the Turing, Ampere, and Hopper GPU series. They significantly boost performance in machine learning and artificial intelligence tasks.
Purpose and Features of Tensor Cores
Tensor Cores are optimized for high-throughput matrix operations, particularly Matrix Multiply and Accumulate (MMA) — a key operation in neural networks, especially for convolutions and linear transformations.
Key Features:
- Hardware-level support for FP16, BF16, TF32, INT8, and more — enabling faster calculations with minimal precision loss.
- Massive parallelism — capable of executing hundreds or thousands of operations per clock cycle.
- Deep integration with NVIDIA software — including CUDA, cuDNN, TensorRT, PyTorch, and TensorFlow.
- Designed for both training and inference, accelerating both the development and deployment of AI models.
Applications of Tensor Cores
- Neural network training — accelerating large-scale models and LLMs (Large Language Models).
- Production inference — enabling low-latency AI responses in real-time systems.
- Image and video processing — speeding up tasks such as filtering, recognition, and segmentation.
- NLP and generative models — powering technologies like GPT, BERT, and Stable Diffusion.
- Cloud-based AI services — many cloud providers offer GPU instances with Tensor Core support.
Tensor Cores have revolutionized AI acceleration, making it feasible to train and deploy complex models in real time while reducing infrastructure costs. They are instrumental in advancing applications in science, healthcare, finance, security, and creative industries.