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Tensor Cores

Tensor Cores are specialized processing units within NVIDIA GPUs designed to accelerate deep learning and artificial intelligence (AI) applications. They are particularly adept at performing mixed-precision matrix multiply-and-accumulate operations, which are fundamental to the training and inference of neural networks. The key features of Tensor Cores include:


  1. Mixed Precision Training: Tensor Cores support mixed precision training, which allows the use of both FP16 (16-bit floating point) and FP32 (32-bit floating point) data types. This capability enables the acceleration of deep learning computations while maintaining the precision necessary for the training of neural networks[1][2][3].
  2. Fused Multiply-Add (FMA) Operations: Tensor Cores can perform fused multiply-add operations on matrices. For example, they can multiply two FP16 matrices and add the result to an FP16 or FP32 matrix in a single operation, which significantly speeds up the calculations required for deep learning[1][2].
  3. Generational Improvements: Since their introduction in the Volta GPU microarchitecture, Tensor Cores have evolved through several generations, with each new generation offering improvements in performance and support for additional data types such as INT8, INT4, and bfloat16, among others[1][2][3].
  4. Acceleration of AI Workloads: Tensor Cores are optimized for the large matrix operations that are common in AI workloads, such as those found in deep learning training and inference. They enable higher throughput and faster computation times compared to traditional CUDA cores, which are designed for general-purpose parallel computing[2][4].
  5. Integration with AI Frameworks: Tensor Cores are supported by popular AI frameworks and libraries, such as TensorFlow and PyTorch, which are optimized to take advantage of their capabilities for accelerating AI model training and inference[1][3].
  6. Versatility for HPC & AI: The latest generations of Tensor Cores are versatile accelerators for both AI and high-performance computing (HPC) applications, providing significant speedups across a range of workloads[3].


Tensor Cores have become a critical component of NVIDIA’s data center solutions, contributing to the company’s performance leadership in AI and HPC benchmarks. They are a key factor in the ability of NVIDIA GPUs to handle the increasing complexity and computational demands of modern AI models[3].

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