Llama-3.1-Nemotron-Ultra-253B-v1
| Property | Value |
|---|---|
| Parameter Count | 253 Billion |
| Context Length | 128K tokens |
| License | NVIDIA Open Model License |
| Release Date | April 7, 2025 |
| Developer | NVIDIA |
What is Llama-3.1-Nemotron-Ultra-253B-v1?
Llama-3.1-Nemotron-Ultra-253B-v1 is a cutting-edge large language model that represents a significant evolution in AI efficiency and performance. Derived from Meta's Llama-3.1-405B-Instruct, this model has been optimized through innovative Neural Architecture Search (NAS) to deliver exceptional reasoning capabilities while maintaining computational efficiency. The model supports an impressive context length of 128K tokens and can run on a single 8xH100 node for inference.
Implementation Details
The model employs sophisticated architectural innovations including skip attention mechanisms, variable FFN layers, and FFN fusion techniques. It underwent a comprehensive training process including knowledge distillation for 65 billion tokens and continual pretraining for 88 billion tokens. The model supports multiple languages and features two distinct reasoning modes (ON/OFF) controlled via system prompts.
- Innovative NAS-based architecture optimization
- Multi-phase post-training process for enhanced reasoning
- Supports multiple languages including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai
- Optimized for commercial deployment
Core Capabilities
- Advanced reasoning and problem-solving abilities
- Enhanced performance in mathematics and coding tasks
- RAG and tool-calling support
- High-efficiency inference with reduced memory footprint
- Flexible deployment options with transformers and vLLM support
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its optimized architecture achieved through Neural Architecture Search, allowing for significant efficiency gains while maintaining high performance. It offers an excellent balance between model accuracy and computational efficiency, making it ideal for commercial applications.
Q: What are the recommended use cases?
The model is particularly well-suited for AI Agent systems, chatbots, RAG systems, and instruction-following tasks. It excels in reasoning tasks, mathematical problem-solving, and code generation, making it versatile for various commercial applications.
