Sakana AI and Nvidia have open-sourced TwELL, a sparse data format that enables H100 GPUs to skip 80% of invalid computations in large language models without sacrificing accuracy. The solution delivers up to 30% faster inference and 24% faster training on H100s while reducing peak memory usage. In testing on a 1.5-billion-parameter model, the approach reduced active neurons to below 2% through lightweight regularization during training, with no performance degradation across seven downstream tasks.
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