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:

# Docker (recommended)
docker run --gpus all -it --rm nvcr.io/nvidia/pytorch:25.04-py3

# Or pip
pip install megatron-core

Simple distributed training:

# Train with 2 GPUs using data parallelism
torchrun --nproc_per_node=2 examples/run_simple_mcore_train_loop.py

# Or LLaMA-3 8B training
./examples/llama/train_llama3_8b_fp8.sh

Common workflows

Workflow 1: Train LLaMA-style model with 3D parallelism

Copy this checklist:

LLaMA Training Setup:
- [ ] Step 1: Choose parallelism configuration
- [ ] Step 2: Configure training hyperparameters
- [ ] Step 3: Launch distributed training
- [ ] Step 4: Monitor performance metrics

Step 1: Choose parallelism configuration

Model size determines parallelism strategy:

Model Size	GPUs	Tensor Parallel	Pipeline Parallel	Data Parallel	Context Parallel
7B	8	1	1	8	1
13B	8	2	1	4	1
70B	64	4	4	4	1
405B	128	8	8	2	2

Step 2: Configure training hyperparameters

#!/bin/bash
# train_llama_70b.sh

GPUS_PER_NODE=8
NNODES=8  # 64 GPUs total
TP=4      # Tensor parallel
PP=4      # Pipeline parallel
CP=1      # Context parallel

# LLaMA 70B configuration
MODEL_SIZE=70  # Billion parameters
HIDDEN_SIZE=8192
NUM_LAYERS=80
NUM_HEADS=64
SEQ_LENGTH=4096

# Training hyperparameters
MICRO_BATCH=1
GLOBAL_BATCH=1024
LR=3e-4