Megatron-LM

Megatron-LM is an open-source LLM training framework developed by NVIDIA, originally introduced in 2019 and expanded with the Megatron-Turing NLG 530B collaboration with Microsoft. The framework provides highly optimized implementations of tensor parallelism, pipeline parallelism, and data parallelism at frontier scale, often used in combination as 3D parallelism for trillion-parameter training. Megatron-LM's tensor parallel implementation splits attention heads and MLP layers across GPUs within a node, while pipeline parallelism splits transformer layers across nodes, and data parallelism replicates the resulting shards. NVIDIA's NeMo Framework wraps Megatron-LM with a higher-level configuration interface and pretrained model recipes. Together with DeepSpeed's ZeRO and PyTorch's FSDP, Megatron-LM forms the trio of major large-scale training frameworks used by frontier labs and academic supercomputing centers. AI governance teams encounter Megatron-LM in the context of self-hosted LLM training projects, particularly at organizations with NVIDIA DGX clusters or substantial cloud GPU commitments.

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