ResBM: New Transformer Architecture Cuts AI Training Data Transfer by 128×, Making Massive Models Cheaper to Train

Macrocosmos just dropped a paper on ResBM (Residual Bottleneck Models), a clever new transformer design that solves a real pain point in AI training: when you split a huge model across multiple GPUs, they have to constantly talk to each other, which wastes a ton of bandwidth and slows everything down. ResBM uses a residual encoder-decoder bottleneck to compress the data flying between pipeline stages by 128×—basically squeezing the information down to 1/128th of its original size—while keeping the model's learning ability intact. The catch? It actually works without tanking performance. This could make training massive AI models way cheaper and faster, which is huge for labs that don't have unlimited compute budgets.