Individual skill
training-llms-megatron
Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.
backend/cli/skills/ml-training/megatron-core/SKILL.md
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- Repository last updated
- Nov 6, 2026
- License
- Apache-2.0
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- agent Repository topic
- ai Repository topic
- bun Repository topic
- cli Repository topic
- llm Repository topic
- ml Repository topic
- open-source Repository topic
- research Repository topic
- science Repository topic
- ai-agent Repository topic
- megatron-core Declared skill tag
- large-scale-training Declared skill tag
- nvidia Declared skill tag
- tensor-parallelism Declared skill tag
- pipeline-parallelism Declared skill tag
- model-parallelism Declared skill tag
- h100 Declared skill tag
- distributed-training Declared skill tag
- production Declared skill tag
- engineering Derived category
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# Megatron-Core - Large-Scale LLM Training
## Quick start
Megatron-Core trains LLMs from 2B to 462B parameters with up to 47% Model FLOP Utilization on H100 GPUs through advanced parallelism strategies.
**Installation**:
```bash
# Docker (recommended)
docker run --gpus all -it --rm nvcr.io/nvidia/pytorch:25.04-py3
# Or pip
pip install megatron-core
```