konkreterevolver/Llama-3.1-Nemotron-Nano-8B-v1

TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kPublished:Apr 11, 2026License:nvidia-open-model-licenseArchitecture:Transformer Open Weights Cold

Llama-3.1-Nemotron-Nano-8B-v1 is an 8 billion parameter large language model developed by NVIDIA, derived from Meta Llama-3.1-8B-Instruct. This reasoning model is post-trained for enhanced reasoning, human chat preferences, RAG, and tool calling, offering a strong balance between accuracy and efficiency. It supports a 128K token context length and is designed for commercial use in AI agent systems, chatbots, and instruction-following tasks.

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Model Overview

Llama-3.1-Nemotron-Nano-8B-v1 is an 8 billion parameter large language model developed by NVIDIA, based on Meta Llama-3.1-8B-Instruct. It is specifically post-trained to enhance reasoning capabilities, human chat preferences, and tasks like RAG and tool calling, aiming for an optimal balance between accuracy and computational efficiency. The model supports a substantial context length of 128K tokens and can run on a single RTX GPU, making it suitable for local deployment.

Key Capabilities & Features

  • Enhanced Reasoning: Underwent multi-phase post-training, including supervised fine-tuning for Math, Code, Reasoning, and Tool Calling, and multiple reinforcement learning stages.
  • Flexible Reasoning Modes: Supports distinct "Reasoning On" and "Reasoning Off" modes, controlled via the system prompt, with specific recommendations for temperature and top_p settings.
  • Performance Improvements: Demonstrates significant improvements in reasoning benchmarks like MATH500 (95.4% pass@1 in Reasoning On) and AIME25 (47.1% pass@1 in Reasoning On) compared to its "Reasoning Off" mode.
  • Multilingual Support: Primarily intended for English and coding languages, with additional support for German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
  • Commercial Use: Ready for commercial applications, governed by the NVIDIA Open Model License and Llama 3.1 Community License.

Ideal Use Cases

  • AI Agent Systems: Designed to power intelligent agents requiring robust reasoning.
  • Chatbots: Optimized for human chat preferences and instruction-following.
  • RAG Systems: Suitable for retrieval-augmented generation applications.
  • Instruction Following: Excels in general instruction-following tasks, balancing accuracy and compute efficiency.

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