Tensor Parallelism

Tensor parallelism is a technique used in distributed deep learning to split large tensor operations across multiple computing devices, enabling the training of massive models that wouldn't fit on a single device. By parallelizing tensor computations, developers can scale up their AI workloads, reduce training times, and tackle complex problems in areas like natural language processing and computer vision, making it a crucial tool for the tech community as AI model sizes continue to grow.