NVIDIA NeMo Framework¶
NVIDIA NeMo Framework is an end-to-end training framework for large language models (LLMs), multi-modal models and speech models designed to run on NVIDIA accelerated infrastructure. It enables seamless scaling of both pre-training and post-training workloads from single GPU to thousand-node clusters for Hugging Face, Megatron, and PyTorch models.
This site hosts developer updates, tutorials, and insights about NeMo's latest core components and innovations.
Latest Blog Posts¶
Guide to Fine-tune Nvidia NeMo models with Granary Data¶
August 13, 2025
NeMo-RL: Journey of Optimizing Weight Transfer in Large MoE Models by 10x¶
August 12, 2025
🚀 NeMo Framework Now Supports Google Gemma 3n: Efficient Multimodal Fine-tuning Made Simple¶
August 11, 2025
NeMo Framework Components¶
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🚀 NeMo-RL
Scalable toolkit for efficient model reinforcement learning and post-training. Includes algorithms like DPO, GRPO, and support for everything from single-GPU prototypes to thousand-GPU deployments.
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🚀 NeMo-Automodel
Day-0 support for any Hugging Face model leveraging PyTorch native functionalities while providing performance and memory optimized training and inference recipes.
License¶
Apache 2.0 licensed with third-party attributions documented in each repository.