Sparsity in INT8: Training Workflow and Best Practices for NVIDIA TensorRT Acceleration – NVIDIA Technical Blog News and tutorials for developers, data scientists, and IT admins 2025-03-24T20:52:54Z http://www.open-lab.net/blog/feed/ Gwena Cunha Sergio <![CDATA[Sparsity in INT8: Training Workflow and Best Practices for NVIDIA TensorRT Acceleration]]> http://www.open-lab.net/blog/?p=64658 2023-06-09T20:26:40Z 2023-05-16T16:00:00Z The training stage of deep learning (DL) models consists of learning numerous dense floating-point weight matrices, which results in a massive amount of...]]> The training stage of deep learning (DL) models consists of learning numerous dense floating-point weight matrices, which results in a massive amount of...

The training stage of deep learning (DL) models consists of learning numerous dense floating-point weight matrices, which results in a massive amount of floating-point computations during inference. Research has shown that many of those computations can be skipped by forcing some weights to be zero, with little impact on the final accuracy. In parallel to that, previous posts have shown that��

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