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Megatron-LM & Megatron Core
GPU-optimized library for training transformer models at scale
⚡ Quick Start
# 1. Install Megatron Core with required dependencies
pip install --no-build-isolation megatron-core[mlm,dev]
# 2. Clone repository for examples
git clone https://github.com/NVIDIA/Megatron-LM.git
cd Megatron-LM
pip install --no-build-isolation .[mlm,dev]
→ Complete Installation Guide - Docker, pip variants (dev,lts,etc.), source installation, and system requirements
Latest News
- 📣 NEW! Megatron Dev Branch - early access branch with experimental features.
- 🔄 Megatron Bridge - Bidirectional converter for interoperability between Hugging Face and Megatron checkpoints, featuring production-ready recipes for popular models.
- [2025/08] MoE Q3-Q4 2025 Roadmap - Comprehensive roadmap for MoE features including DeepSeek-V3, Qwen3, advanced parallelism strategies, FP8 optimizations, and Blackwell performance enhancements.
- [2025/08] GPT-OSS Model - Advanced features including YaRN RoPE scaling, attention sinks, and custom activation functions are being integrated into Megatron Core.
- [2025/06] Megatron MoE Model Zoo - Best practices and optimized configurations for training DeepSeek-V3, Mixtral, and Qwen3 MoE models with performance benchmarking and checkpoint conversion tools.
- [2025/05] Megatron Core v0.11.0 brings new capabilities for multi-data center LLM training (blog).
Previous News
- [2024/07] Megatron Core v0.7 improves scalability and training resiliency and adds support for multimodal training (blog).
- [2024/06] Megatron Core added supports for Mamba-based models. Check out our paper An Empirical Study of Mamba-based Language Models and code example.
- [2024/01 Announcement] NVIDIA has released the core capabilities in Megatron-LM into Megatron Core in this repository. Megatron Core expands upon Megatron-LM's GPU-optimized techniques with more cutting-edge innovations on system-level optimizations, featuring composable and modular APIs. Explore the [Megatron Core intro](#Megatron Core) for more details.
Table of Contents
Getting Started
Core Features
Training
Resources
- Examples - Training scripts and tutorials
- Documentation - Official docs
- Roadmaps - Development roadmaps and feature tracking
- Community & Support - Get help and contribute
Megatron Overview
Project Structure
Megatron-LM/
├── megatron/
│ ├── core/ # Megatron Core (kernels, parallelism, building blocks)
│ │ ├── models/ # Transformer models
│ │ ├── transformer/ # Transformer building blocks
│ │ ├── tensor_parallel/ # Tensor parallelism
│ │ ├── pipeline_parallel/ # Pipeline parallelism
│ │ ├── distributed/ # Distributed training (FSDP, DDP)
│ │ ├── optimizer/ # Optimizers
│ │ ├── datasets/ # Dataset loaders
│ │ ├── inference/ # Inference engines
│ │ └── export/ # Model export (e.g. TensorRT-LLM)
│ ├── training/ # Training scripts
│ ├── inference/ # Inference server
│ ├── legacy/ # Legacy components
│ └── post_training/ # Post-training (RLHF, etc.)
├── examples/ # Ready-to-use training examples
├── tools/ # Utility tools
├── tests/ # Comprehensive test suite
└── docs/ # Documentation
Megatron-LM: Reference Implementation
Reference implementation that includes Megatron Core plus everything needed to train models.
Best for:
- Training state-of-the-art foundation models at scale with cutting-edge performance on latest NVIDIA hardware
- Research teams exploring new architectures and training techniques
- Learning distributed training concepts and best practices
- Quick experimentation with proven model configurations
What you get:
- Pre-configured training scripts for GPT, LLama, DeepSeek, Qwen, and more.
- End-to-end examples from data prep to evaluation
- Research-focused tools and utilities
Megatron Core: Composable Library
Composable library with GPU-optimized building blocks for custom training frameworks.
Best for:
- Framework developers building on top of modular and optimized components
- Research teams needing custom training loops, optimizers, or data pipelines
- ML engineers requiring fault-tolerant training pipelines
What you get:
- Composable transformer building blocks (attention, MLP, etc.)
- Advanced parallelism strategies (TP, PP, DP, EP, CP)
- Pipeline schedules and distributed optimizers
- Mixed precision support (FP16, BF16, FP8)
- GPU-optimized kernels and memory management
- High-performance dataloaders and dataset utilities
- Model architectures (LLaMA, Qwen, GPT, Mixtral, Mamba, etc.)
Ecosystem Libraries
Libraries used by Megatron Core:
- Megatron Energon 📣 NEW! - Multi-modal data loader (text, images, video, audio) with distributed loading and dataset blending
- Transformer Engine - Optimized kernels and FP8 mixed precision support
- Resiliency Extension (NVRx) - Fault tolerant training with failure detection and recovery
Libraries using Megatron Core:
- Megatron Bridge - Training library with bidirectional Hugging Face ↔ Megatron checkpoint conversion, flexible training loops, and production-ready recipes
- NeMo RL - Scalable toolkit for efficient reinforcement learning with RLHF, DPO, and other post-training methods
- NeMo Framework - Enterprise framework with cloud-native support and end-to-end examples
- TensorRT Model Optimizer (ModelOpt) - Model optimization toolkit for quantization, pruning, and distillation
Compatible with: Hugging Face Accelerate, Colossal-AI, DeepSpeed
Installation
🐳 Docker (Recommended)
We strongly recommend using the previous releases of PyTorch NGC Container rather than the latest one for optimal compatibility with Megatron Core release and testing. Our releases are always based on the previous month's NGC container, so this ensures compatibility and stability.
Note: The NGC PyTorch container constraints the python environment globally via PIP_CONSTRAINT. In the following examples we will unset the variable.
This container comes with all dependencies pre-installed with compatible versions and optimized configurations for NVIDIA GPUs:
- PyTorch (latest stable version)
- CUDA, cuDNN, NCCL (latest stable versions)
- Support for FP8 on NVIDIA Hopper, Ada, and Blackwell GPUs
- For best performance, use NVIDIA Turing GPU architecture generations and later
# Run container with mounted directories
docker run --runtime --nvidia --gpus all -it --rm \
-v /path/to/megatron:/workspace/megatron \
-v /path/to/dataset:/workspace/dataset \
-v /path/to/checkpoints:/workspace/checkpoints \
-e PIP_CONSTRAINT= \
nvcr.io/nvidia/pytorch:25.04-py3
Pip Installation
Megatron Core offers support for two NGC PyTorch containers:
dev: Moving head that supports the most recent upstream dependencieslts: Long-term support of NGC PyTorch 24.01
Both containers can be combined with mlm which adds package dependencies for Megatron-LM on top of Megatron Core.
# Install the latest release dependencies
pip install "setuptools<80.0.0,>=77.0.0" "packaging>=24.2"
pip install --no-build-isolation megatron-core[dev]
# For running an M-LM application:
pip install "setuptools<80.0.0,>=77.0.0" "packaging>=24.2"
pip install --no-build-isolation megatron-core[mlm,dev]
# Install packages for LTS support NGC PyTorch 24.01
pip install "setuptools<80.0.0,>=77.0.0" "packaging>=24.2"
pip install --no-build-isolation megatron-core[lts]
# For running an M-LM application:
pip install "setuptools<80.0.0,>=77.0.0" "packaging>=24.2"
pip install --no-build-isolation megatron-core[mlm,lts]
For a version of Megatron Core with only torch, run:
pip install megatron-core
System Requirements
Hardware Requirements
- FP8 Support: NVIDIA Hopper, Ada, Blackwell GPUs
- Recommended: NVIDIA Turing architecture or later
Software Requirements
- CUDA/cuDNN/NCCL: Latest stable versions
- PyTorch: Latest stable version
- Transformer Engine: Latest stable version
- Python: 3.12 recommended
Performance Benchmarking
For our latest performance benchmarking results, please refer to NVIDIA NeMo Framework Performance Summary.
Our codebase efficiently trains models from 2B to 462B parameters across thousands of GPUs, achieving up to 47% Model FLOP Utilization (MFU) on H100 clusters.
Benchmark Configuration:
- Vocabulary size: 131,072 tokens
- Sequence length: 4096 tokens
- Model scaling: Varied hidden size, attention heads, and layers to achieve target parameter counts
- Communication optimizations: Fine-grained overlapping with DP (
--overlap-grad-reduce,--overlap-param-gather), TP (--tp-comm-overlap), and PP (enabled by default)
Key Results:
- 6144 H100 GPUs: Successfully benchmarked 462B parameter model training
- Superlinear scaling: MFU increases from 41% to 47-48% with model size
- End-to-end measurement: Throughputs include all operations (data loading, optimizer steps, communication, logging)
- Production ready: Full training pipeline with checkpointing and fault tolerance
- Note: Performance results measured without training to convergence
Weak Scaling Results
Our weak scaled results show superlinear scaling (MFU increases from 41% for the smallest model considered to 47-48% for the largest models); this is because larger GEMMs have higher arithmetic intensity and are consequently more efficient to execute.
Strong Scaling Results
We also strong scaled the standard GPT-3 model (our version has slightly more than 175 billion parameters due to larger vocabulary size) from 96 H100 GPUs to 4608 GPUs, using the same batch size of 1152 sequences throughout. Communication becomes more exposed at larger scale, leading to a reduction in MFU from 47% to 42%.
Training
Getting Started
Simple Training Example
# Distributed training example (2 GPUs, mock data)
torchrun --nproc_per_node=2 examples/run_simple_mcore_train_loop.py
LLama-3 Training Example
# 8 GPUs, FP8 precision, mock data
./examples/llama/train_llama3_8b_fp8.sh
Data Preparation
JSONL Data Format
{"text": "Your training text here..."}
{"text": "Another training sample..."}
Basic Preprocessing
python tools/preprocess_data.py \
--input data.jsonl \
--output-prefix processed_data \
--tokenizer-type HuggingFaceTokenizer \
--tokenizer-model /path/to/tokenizer.model \
--workers 8 \
--append-eod
Key Arguments
--input: Path to input JSON/JSONL file--output-prefix: Prefix for output binary files (.bin and .idx)--tokenizer-type: Tokenizer type (HuggingFaceTokenizer,GPT2BPETokenizer, etc.)--tokenizer-model: Path to tokenizer model file--workers: Number of parallel workers for processing--append-eod: Add end-of-document token
Parallelism Strategies
Data Parallelism (DP)
Standard Data Parallel
# Standard DDP - replicate model on each GPU
torchrun --nproc_per_node=8 pretrain_gpt.py \
--data-parallel-sharding-strategy no_shard
Fully Sharded Data Parallel (FSDP)
# Megatron's optimized FSDP (~15% faster than PyTorch FSDP2)
--use-custom-fsdp
# PyTorch FSDP2
--use-torch-fsdp2
# Sharding strategies
--data-parallel-sharding-strategy optim # Shard optimizer states (ZeRO-1)
--data-parallel-sharding-strategy optim_grads # Shard gradients + optimizer (ZeRO-2)
--data-parallel-sharding-strategy optim_grads_params # Shard parameters + gradients + optimizer (ZeRO-3)
Tensor Parallelism (TP)
Split individual model layers across GPUs:
--tensor-model-parallel-size 4 # 4-way tensor parallelism
--sequence-parallel # Enable sequence parallelism (recommended with TP)
Pipeline Parallelism (PP)
Split model depth across GPUs:
--pipeline-model-parallel-size 8 # 8 pipeline stages
--virtual-pipeline-model-parallel-size 4 # Virtual pipeline for better load balancing
Context Parallelism (CP)
Split long sequences across GPUs for handling long contexts:
--context-parallel-size 2 # 2-way context parallelism
--cp-comm-type p2p # Communication: p2p, a2a, allgather, a2a+p2p
--hierarchical-context-parallel-sizes 2 4 # Hierarchical context parallelism
Expert Parallelism (EP)
For Mixture of Experts (MoE) models:
--expert-model-parallel-size 4 # 4-way expert parallelism
--num-experts 8 # 8 experts per MoE layer
--moe-grouped-gemm # Optimize expert computation
Combining Parallelism Strategies
Parallelism Selection Guide
Based on NVIDIA NeMo production configurations:
| Model | Size | GPUs | TP | PP | CP | EP | Notes |
|---|---|---|---|---|---|---|---|
| LLama-3 | 8B | 8 | 1 | 1 | 2 | 1 | CP for long seqlen (8K) |
| LLama-3 | 70B | 64 | 4 | 4 | 2 | 1 | TP+PP |
| LLama-3.1 | 405B | 1024 | 8 | 8 | 2 | 1 | 3D parallelism for scale |
| GPT-3 | 175B | 128-512 | 4 | 8 | 1 | 1 | Large model config |
| Mixtral | 8x7B | 64 | 1 | 4 | 1 | 8 | EP for MoE |
| Mixtral | 8x22B | 256 | 4 | 4 | 8 | 8 | Combined TP+EP for large MoE |
| DeepSeek-V3 | 671B | 1024 | 2 | 16 | 1 | 64 | Large MoE config |
MoE-Specific Requirements
Important: When combining Expert Parallelism (EP) with Tensor Parallelism (TP), Sequence Parallelism (SP) must be enabled.
Performance Optimizations
| Feature | Flag | Benefit |
|---|---|---|
| FlashAttention | --attention-backend |
Faster attention and lower memory usage |
| FP8 Training | --fp8-hybrid |
Faster training |
| Activation Checkpointing | --recompute-activations |
Reduced memory usage |
| Data Parallelism Communication Overlap | --overlap-grad-reduce |
Faster distributed training |
| Distributed Optimizer | --use-distributed-optimizer |
Reduced checkpointing time |
→ NVIDIA NeMo Framework Performance Tuning Guide - Comprehensive performance optimization guide covering advanced tuning techniques, communication overlaps, memory optimizations, and profiling options.
FlashAttention
FlashAttention is a fast and memory-efficient attention algorithm. We recommend the default usage, which uses cuDNN for attention via Transformer Engine and provides up to 50% speedups on forward and 84% on backward propagation with FP8 kernels. The flash-attn package is also supported via --use-flash-attn.
Mixed Precision Training
--fp16 # Standard FP16
--bf16 # BFloat16 (recommended for large models)
--fp8-hybrid # FP8 training (Hopper, Ada, and Blackwell GPUs)
Activation Checkpointing and Recomputation
# For limited memory
--recompute-activations
# For extreme memory constraints
--recompute-granularity full \
--recompute-method uniform
Data Parallelism Communication Overlap
--overlap-grad-reduce
--overlap-param-gather
Distributed Optimizer
--use-distributed-optimizer
Roadmaps
Stay up-to-date with our development roadmaps and planned features:
- MoE Q3-Q4 2025 Roadmap - Comprehensive MoE feature development including DeepSeek-V3, Qwen3, advanced parallelism, FP8 optimizations, and Blackwell enhancements
- GPT-OSS Implementation Tracker - Advanced features including YaRN RoPE scaling, attention sinks, and custom activation functions
More roadmap trackers will be added soon.
Community & Support
Getting Help
- 📖 Documentation - Official documentation
- 🐛 Issues - Bug reports and feature requests
Contributing
We ❤️ contributions! Ways to contribute:
- 🐛 Report bugs - Help us improve reliability
- 💡 Suggest features - Shape the future of Megatron Core
- 📝 Improve docs - Make Megatron Core more accessible
- 🔧 Submit PRs - Contribute code improvements
Citation
@article{megatron-lm,
title={Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism},
author={Shoeybi, Mohammad and Patwary, Mostofa and Puri, Raul and LeGresley, Patrick and Casper, Jared and Catanzaro, Bryan},
journal={arXiv preprint arXiv:1909.08053},
year={2019}
}
5 maintainers
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