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const { OpenAI } = require('openai');
const api = new OpenAI({
baseURL: 'https://api.ai.cc/v1',
apiKey: '',
});
const main = async () => {
const result = await api.chat.completions.create({
model: 'nvidia/nemotron-nano-9b-v2',
messages: [
{
role: 'system',
content: 'You are an AI assistant who knows everything.',
},
{
role: 'user',
content: 'Tell me, why is the sky blue?'
}
],
});
const message = result.choices[0].message.content;
console.log(`Assistant: ${message}`);
};
main();
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.ai.cc/v1",
api_key="",
)
response = client.chat.completions.create(
model="nvidia/nemotron-nano-9b-v2",
messages=[
{
"role": "system",
"content": "You are an AI assistant who knows everything.",
},
{
"role": "user",
"content": "Tell me, why is the sky blue?"
},
],
)
message = response.choices[0].message.content
print(f"Assistant: {message}")
-
AI Playground
Test all API models in the sandbox environment before you integrate.
We provide more than 300 models to integrate into your app.
Product Detail
NVIDIA Nemotron Nano 9B V2 is a cutting-edge large language model (LLM) engineered for incredibly efficient and high-throughput text generation. It particularly excels in tackling complex reasoning tasks, offering a robust solution for developers and enterprises. By leveraging an innovative hybrid Mamba-Transformer architecture, this model strikes an optimal balance between rapid inference speeds, precision, and moderate resource consumption, making it a powerful choice for diverse AI applications.
✨ Key Technical Specifications
- • Architecture: Hybrid Mamba-Transformer
- • Parameter Count: 9 Billion
- • Training Data: 20 trillion tokens, FP8 training precision
- • Context Window: 131,072 tokens
🚀 Unmatched Performance Benchmarks
- • Reasoning Accuracy: Achieves or surpasses the performance of similarly sized models across critical benchmarks such as GSM8K, MATH, AIME, MMLU, and GPQA.
- • Code Generation: Boasts 71.1% accuracy on LiveCodeBench, offering robust support for 43 distinct programming languages.
- • Memory Efficiency: Leveraging INT4 quantization, Nemotron Nano 9B V2 can be deployed on GPUs with just 22 GiB memory, all while maintaining support for exceptionally massive context windows.
💡 Core Features & Innovations
- • Hybrid Mamba-Transformer Architecture: This innovative design integrates efficient Mamba-2 state space layers with selective Transformer self-attention, significantly accelerating long-context reasoning without compromising accuracy.
- • High Throughput: Experience up to 6x faster inference speeds compared to similar-sized models, such as Qwen3-8B, particularly in scenarios demanding intensive reasoning.
- • Long Context Support: Capable of processing sequences up to 128,000 tokens on commodity hardware, this feature enables extensive document comprehension and sophisticated multi-document summarization.
💰 API Pricing Details
- • Input: $0.04431 / 1M tokens
- • Output: $0.17724 / 1M tokens
🌟 Diverse Use Cases for Nemotron Nano 9B V2
- • Mathematical & Scientific Reasoning: Ideal for advanced tutoring systems, intricate problem-solving, and accelerating academic research.
- • AI Agent Systems: Perfectly suited for developing controllable, multi-step reasoning workflows and efficient function calling within complex AI pipelines.
- • Enterprise Customer Support: Powers fast, accurate, and multilingual chatbots, complete with advanced reasoning capabilities and content safety features.
- • Document Summarization & Analysis: Enables efficient processing of vast documents or collections for deep research and rapid knowledge extraction.
- • Code Development & Debugging: Facilitates high-accuracy code generation across dozens of programming languages, significantly aiding developers.
- • Content Moderation: Trained with specialized safety datasets, ensuring reliable and high-quality output in sensitive environments.
💻 Code Sample Placeholder
// Example API call for Nemotron Nano 9B V2
import openai
client = openai.OpenAI(api_key="YOUR_API_KEY")
response = client.chat.completions.create(
model="nvidia/nemotron-nano-9b-v2",
messages=[
{"role": "user", "content": "Explain the Mamba architecture in simple terms."}
],
max_tokens=150
)
print(response.choices[0].message.content)
🧠 Comparing Nemotron Nano 9B V2 to Other Leading LLMs
Nemotron Nano 9B V2 vs. Qwen3-8B
Nemotron Nano 9B V2 employs its hybrid Mamba-Transformer architecture, replacing most self-attention layers with Mamba-2 layers. This results in up to 6x faster inference on reasoning-heavy tasks. It also supports significantly longer contexts (128K tokens) on a single GPU, unlike Qwen3-8B's conventional Transformer design with typically shorter context windows.
Nemotron Nano 9B V2 vs. GPT-3.5
While GPT-3.5 is widely adopted for general natural language processing (NLP) tasks and boasts broad integration, Nemotron Nano 9B V2 specializes in efficient long-context reasoning and multi-step problem solving. It offers superior throughput specifically when deployed on NVIDIA hardware.
Nemotron Nano 9B V2 vs. Claude 2
Claude 2 emphasizes safety and instruction-following with comprehensive conversational abilities. In contrast, Nemotron Nano 9B V2 focuses more intensely on mathematical/scientific reasoning and coding accuracy, featuring dedicated controllable reasoning budget features.
Nemotron Nano 9B V2 vs. PaLM 2
PaLM 2 aims for high accuracy across broad AI benchmarks and multilingual tasks, often requiring more extensive hardware resources. Nemotron Nano 9B V2 excels in deployability with a smaller footprint, supporting effectively longer contexts and faster inference speeds specifically on NVIDIA GPU architectures. This makes it a pragmatic choice for large-scale enterprise or edge applications.
❓ Frequently Asked Questions (FAQs)
Q1: What is Nemotron Nano 9B V2?
Nemotron Nano 9B V2 is NVIDIA's state-of-the-art large language model (LLM) designed for efficient, high-throughput text generation, particularly strong in complex reasoning tasks. It uses a unique hybrid Mamba-Transformer architecture.
Q2: What are its key performance advantages?
It offers up to 6x faster inference speeds compared to similar models in reasoning-heavy tasks, exceptional accuracy in reasoning and code generation (71.1% on LiveCodeBench), and impressive memory efficiency, allowing deployment on GPUs with just 22 GiB memory.
Q3: Can Nemotron Nano 9B V2 handle long documents?
Yes, it supports an extremely long context window of 131,072 tokens, capable of processing sequences up to 128,000 tokens on commodity hardware, making it ideal for extensive document comprehension and multi-document summarization.
Q4: What are the primary use cases for this model?
Its primary use cases include mathematical and scientific reasoning, AI agent systems, enterprise customer support, document summarization and analysis, high-accuracy code development, and content moderation due to its specialized training.
Q5: How does its architecture differ from traditional LLMs?
Nemotron Nano 9B V2 uses a unique hybrid Mamba-Transformer architecture, replacing most self-attention layers with efficient Mamba-2 state space layers. This design is crucial for its accelerated long-context reasoning and high throughput capabilities.
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