NVIDIA NeMo Framework Overview

NeMo Framework is NVIDIA's GPU accelerated, fully open-source, end-to-end training framework for large language models (LLMs), multi-modal models, diffusion and speech models. It enables seamless scaling of pretraining, post-training, and reinforcement learning workloads from single GPU to thousand-node clusters for both 🤗Hugging Face/PyTorch and Megatron models. This GitHub organization includes a suite of libraries and recipe collections to help users train models from end to end.

NeMo Framework is also a part of the NVIDIA NeMo software suite for managing the AI agent lifecycle.

Latest 📣 announcements and 🗣️ discussions

💬 NeMo Assist

• [4/13/2026]Try NeMo Assist to chat with the NeMo Framework docs and code.

Getting Started

	Installation	Checkpoint Conversion HF<>Megatron	LLM example recipes and scripts	VLM example recipes and scripts
1 ～ 1,000 GPUs	NeMo Automodel, NeMo RL	No Need	Pre-training, SFT, LoRA, DPO, GRPO	SFT, LoRA, GRPO
Over 1,000 GPUs	NeMo Megatron-Bridge, NeMo RL	Conversion	Pretrain, SFT, and LoRA, DPO with megatron_cfg, GRPO with megatron_cfg	SFT, LoRA, GRPO megatron config

Repo organization under NeMo Framework

Summary of key functionalities and container strategy of each repo

Visit the individual repos to find out more 🔍, raise 🐛, contribute ✍️ and participate in discussion forums 🗣️!

Note: The NeMo Framework is currently in the process of restructuring. The original NeMo 2.0 repository will now focus specifically on speech-related components, while other parts of the framework are being modularized into separate libraries such as NeMo Automodel, NeMo Gym, NeMo RL, and more. This transition aims to make NeMo more modular and developer-friendly.

Repo	Key Functionality & Documentation Link	Training Loop	Training Backends	Inference Backends	Model Coverage	Container
NeMo Megatron-Bridge	Pretraining, LoRA, SFT	PyT native loop	Megatron-core	NA	LLM & VLM	NeMo Framework Container
NeMo AutoModel	Pretraining, LoRA, SFT	PyT native loop	PyTorch	NA	LLM, VLM, Omni, VFM	NeMo AutoModel Container
Previous NeMo 2.0 Repo -> will be repurposed to focus on Speech	Pretraining,SFT	PyTorch Lightning Loop	Megatron-core & PyTorch	RIVA	Speech	NA
NeMo RL	SFT, RL	PyT native loop	Megatron-core & PyTorch	vLLM	LLM, VLM	NeMo RL container
NeMo Gym	RL Environment, integrate with RL Framework	NA	NA	NA	NA	NeMo RL Container (WIP)
NeMo Curator	Data curation	NA	NA	NA	Agnostic	NeMo Curator Container
NeMo Evaluator	Model evaluation	NA	NA		Agnostic	NeMo Framework Container
NeMo Export-Deploy	Export to Production	NA	NA	vLLM, TRT, TRTLLM, ONNX	Agnostic	NeMo Framework Container
NeMo Run	Experiment launcher	NA	NA	NA	Agnostic	NeMo Framework Container
NeMo Guardrails	Guardrail model response	NA	NA	NA		NA
NeMo Skills	Reference pipeline for SDG & Eval	NA	NA	NA	Agnostic	NA
NeMo Emerging Optimizers	Collection of Optimizers	NA	Agnostic	NA	NA	NA
Nemotron	Developer asset hub for Nemotron models	NA	NA	NA	Nemotron models	NA
NeMo Data Designer	Synthetic data generation library	NA	NA	NA	NA	NA
NeMo Safe Synthesizer	Privacy-preserving synthetic tabular data	HF Trainer	PyTorch, Opacus	vLLM	LLM	NA
NeMo Anonymizer	PII detection and anonymization	NA	NA	NA	Agnostic	NA

Table 1. NeMo Framework Repos

Diagram Ilustration of Repos under NeMo Framework (WIP)

Figure 1. NeMo Framework Repo Overview

Some background motivations and historical contexts

The NeMo GitHub Org and its repo collections are created to address the following problems

• Need for composability: The Previous NeMo 2.0 version is monolithic and encompasses too many things, making it hard for users to find what they need. Container size is also an issue. Breaking down the Monolithic repo into a series of functional-focused repos to facilitate code discovery.
• Need for customizability: The Previous NeMo 2.0 version uses PyTorch Lighting as the default trainer loop, which provides some out of the box functionality but making it hard to customize. NeMo Megatron-Bridge, NeMo AutoModel, and NeMo RL have adopted pytorch native custom loop to improve flexibility and ease of use for developers.

License

Apache 2.0 licensed with third-party attributions documented in each repository.