Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
We present our techniques for training very large transformer models and implement a simple, efficient intra-layer model parallel approach.
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Memory as the bottleneck
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Transformer models grow faster than GPU memory. Shoeybi et al. split layers across devices so no single GPU holds the full parameter set. The approach is intra-layer model parallelism: partition weight matrices, insert all-reduce communication, stay in native PyTorch.
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Engineering over novelty
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The paper's strength is systems work. They report 8.3B-parameter models on 512 GPUs with strong scaling efficiency. Layer normalization placement matters for BERT-like models at scale. Details that theory papers skip become the difference between convergence and failure.
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Lineage
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Megatron-LM underpins much of NVIDIA's large-model stack and influenced how the industry thinks about training infrastructure. Attention gave the architecture; papers like this made it trainable at scale.