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The GPU (Graphics Processing Unit) has become the backbone of modern AI, especially for training deep learning models. Originally designed for rendering graphics, GPUs evolved into powerful compute engines with the introduction of platforms like CUDA, enabling developers to harness their parallel processing capabilities for general-purpose computing. Unlike CPUs, which focus on sequential execution, GPUs are built to handle thousands of operations simultaneously—making them exceptionally well-suited for the matrix multiplications and tensor operations that power neural networks. This architectural shift is precisely why GPUs dominate AI training workloads today.
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