NeMo Framework

NeMo Framework

Logo for NeMo Framework
NVIDIA NeMo™ framework supports enterprise development of LLMs and generative AI models with automated data processing, model training techniques, and flexible deployment options.
Latest Tag
May 21, 2024
Compressed Size
24.12 GB
Multinode Support
Multi-Arch Support
dev.framework (Latest) Security Scan Results

Linux / amd64

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Which container to use

For CodeGemma, please use the container.
For other large language models or multimodal models, please use the container.
For Speech, please use the container.

For Developer, please use the container.
The NeMo Framework Developer container allows users to access the latest version which is not yet in the major release.

  • Cadence: Weekly
  • If you encounter problems, we recommend that you either experiment with the latest Developer containers or wait for the release of a Stable version at a later date
  • Specify [Dev] [Date] for github issue reporting

What is the NeMo Framework Container?

NVIDIA NeMo™ is an end-to-end platform for development of custom generative AI models anywhere. NVIDIA NeMo framework is designed for enterprise development, it utilizes NVIDIA's state-of-the-art technology to facilitate a complete workflow from automated distributed data processing to training of large-scale bespoke models using sophisticated 3D parallelism techniques, and finally, deployment using retrieval-augmented generation for large-scale inference on an infrastructure of your choice, be it on-premises or in the cloud.

For enterprises running their business on AI, NVIDIA AI Enterprise provides a production-grade, secure, end-to-end software platform that includes NeMo as well as generative AI reference applications and enterprise support to streamline adoption. Now organizations can integrate AI into their operations, streamlining processes, enhancing decision-making capabilities, and ultimately driving greater value.

What You Get with NVIDIA NeMo Framework Container

At the heart of the NeMo framework lies the unification of distributed training and advanced parallelism. NeMo expertly uses GPU resources and memory across nodes, leading to groundbreaking efficiency gains. By dividing the model and training data, NeMo enables seamless multi-node and multi-GPU training, significantly reducing training time and enhancing overall productivity. A standout feature of NeMo is its incorporation of various parallelism and memory saving techniques:

Parallelism Techniques

  • Data Parallelism
  • Fully Sharded Data Parallelism (FSDP)
  • Tensor Parallelism
  • Pipeline Parallelism
  • Sequence Parallelism
  • Expert Parallelism
  • Context Parallelism

Memory-Saving Techniques

  • Selective Activation Recompute (SAR)
  • CPU offloading (Activation, Weights)
  • Attention: Flash Attention (FA), Grouped Query Attention (GQA), Multi-Query Attention (MQA), Sliding Window Attention (SWA)

NeMo framework container is the leading solution to support multimodality training at scale. The platform supports language and multimodal models including Llama 2, Falcon, and CLIP, Stable Diffusion, LLAVA, and various text-based generative AI architectures including GPT, T5, BERT, MoE, RETRO. In addition to large language models (LLM), NeMo supports several pretrained models for Computer Vision (CV), Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text to Speech (TTS).

The NeMo framework container offers an array of techniques to refine pretrained LLMs for specialized use cases including p-tuning, LoRA, Supervised fine-tuning (SFT), Reinforcement learning from human feedback (RLHF), SteerLM, and more. Through these diverse customization options, NeMo offers wide-ranging flexibility that is crucial in meeting varying business requirements.

Getting Started With NVIDIA NeMo

Refer to the NVIDIA NeMo playbooks page for step-by-step instructions on how to get started quickly with the NeMo framework.

Technical Blogs


More detailed documentation is available on the Nemo framework.


NeMo is licensed under the NVIDIA AI Product Agreement. By pulling and using the container, you accept the terms and conditions of this license.