NVIDIA: Llama 3.3 Nemotron Super 49B v1

Llama-3.1-Nemotron-Nano-8B-v1 is a compact large language model (LLM) derived from Meta's Llama-3.1-8B-Instruct, specifically optimized for reasoning tasks, conversational interactions, retrieval-augmented generation (RAG), and tool-calling applications. It balances accuracy and efficiency, fitting comfortably onto a single consumer-grade RTX GPU for local deployment. The model supports extended context lengths of up to 128K tokens.

Note: you must include detailed thinking on in the system prompt to enable reasoning. Please see Usage Recommendations for more.

Llama-3.1-Nemotron-Ultra-253B-v1 is a large language model (LLM) optimized for advanced reasoning, human-interactive chat, retrieval-augmented generation (RAG), and tool-calling tasks. Derived from Meta’s Llama-3.1-405B-Instruct, it has been significantly customized using Neural Architecture Search (NAS), resulting in enhanced efficiency, reduced memory usage, and improved inference latency. The model supports a context length of up to 128K tokens and can operate efficiently on an 8x NVIDIA H100 node.

Note: you must include detailed thinking on in the system prompt to enable reasoning. Please see Usage Recommendations for more.

NVIDIA's Llama 3.1 Nemotron 70B is a language model designed for generating precise and useful responses. Leveraging Llama 3.1 70B architecture and Reinforcement Learning from Human Feedback (RLHF), it excels in automatic alignment benchmarks. This model is tailored for applications requiring high accuracy in helpfulness and response generation, suitable for diverse user queries across multiple domains.

Usage of this model is subject to Meta's Acceptable Use Policy.

Nemotron-4-340B-Instruct is an English-language chat model optimized for synthetic data generation. This large language model (LLM) is a fine-tuned version of Nemotron-4-340B-Base, designed for single and multi-turn chat use-cases with a 4,096 token context length.

The base model was pre-trained on 9 trillion tokens from diverse English texts, 50+ natural languages, and 40+ coding languages. The instruct model underwent additional alignment steps:

The alignment process used approximately 20K human-annotated samples, while 98% of the data for fine-tuning was synthetically generated. Detailed information about the synthetic data generation pipeline is available in the technical report.