Nemotron Nano 9B V2 API

Nemotron Nano 9B V2 API Overview

NVIDIA Nemotron Nano 9B V2 is a state-of-the-art large language model (LLM) designed for efficient and high-throughput text generation, particularly excelling in complex reasoning tasks. Leveraging a hybrid Mamba-Transformer architecture, this model balances inference speed, accuracy, and moderate resource consumption.

Technical Specifications

• Architecture: Hybrid Mamba-Transformer
• Training data: 20 trillion tokens, FP8 training precision
• Context window: 131,072 tokens

Performance Benchmarks

• Reasoning Accuracy: Matches or exceeds similarly sized models across benchmarks like GSM8K, MATH, AIME, MMLU, and GPQA.
• Code Generation: 71.1% accuracy on LiveCodeBench, supporting 43 programming languages.
• Memory Efficiency: INT4 quantization allows deployment on GPUs with 22 GiB memory while supporting massive context windows.

Key Features

• Hybrid Mamba-Transformer Architecture: Combines efficient Mamba-2 state space layers with selective Transformer self-attention to accelerate long-context reasoning without sacrificing accuracy.
• High Throughput: Achieves up to 6x faster inference speed compared to similar-sized models, such as Qwen3-8B, in reasoning-heavy scenarios.
• Long Context Support: Can process sequences up to 128,000 tokens on commodity hardware, enabling extensive document comprehension and multi-document summarization.

Comparison with Other Models

vs Qwen3-8B: Nemotron Nano uses a hybrid Mamba-Transformer architecture replacing most self-attention layers with Mamba-2 layers, resulting in up to 6x faster inference on reasoning-heavy tasks. It supports extremely long contexts (128K tokens) on a single GPU versus Qwen3-8B’s conventional transformer design with shorter context windows.

vs GPT-3.5: While GPT-3.5 is widely adopted for general NLP tasks with broad integration, Nemotron Nano 9B V2 specializes in efficient long-context reasoning and multi-step problem solving with better throughput on NVIDIA hardware.

vs Claude 2: Claude 2 focuses on safety and instruction-following with broad conversational abilities, but Nemotron Nano places more emphasis on mathematical/scientific reasoning and coding accuracy with dedicated controllable reasoning budget features.

vs PaLM 2: PaLM 2 targets high accuracy on broad AI benchmarks and multi-lingual tasks but generally demands more extensive hardware resources. Nemotron Nano excels in deployability with a smaller footprint, supporting effectively longer contexts and faster inference speeds specifically on NVIDIA GPU architectures, making it pragmatic for large-scale enterprise or edge applications.

Use cases

• Mathematical and Scientific Reasoning: Ideal for tutoring systems, complex problem-solving, and academic research assistance.
• AI Agent Systems: Suitable for controllable multi-step reasoning workflows and function calling within AI pipelines.
• Enterprise Customer Support: Fast, accurate, multilingual chatbots with reasoning and content safety features.
• Document Summarization & Analysis: Efficient processing of large documents or collections for research and knowledge extraction.
• Code Development & Debugging: High-accuracy code generation across dozens of languages aiding developers.
• Content Moderation: Trained with specialized safety datasets for reliable output quality in sensitive environments.

Capabilities

• Function calling
• Streaming
• Structured outputs