Summary
Macrocosmos has introduced ResBM, a novel transformer architecture optimized for low-bandwidth pipeline-parallel training. It utilizes a residual encoder-decoder bottleneck to significantly reduce inter-stage communication, achieving a state-of-the-art 128x activation compression. This innovation allows for more efficient training of large models without substantial loss in convergence compared to uncompressed methods.
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