arXiv CS
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Beyond the Trial-and-Error Loop: Hybrid Projection and Automated Tuning for Distributed Training
Configuring distributed LLM training across hundreds of GPUs requires jointly optimizing tensor, pipeline, expert, context, and data parallelism alongside micro-batch size, recomputation, sharding, and overlap, a combinatorial space where memory legality can hinge on a single byte of activation acco
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