Overview

Nemotron 3 Nano 30B delivers high-performance inference with 30B parameters optimized for complex reasoning and extended context understanding within the LLM Resayil ecosystem. With a massive 128,000-token context window, this model handles extensive documentation and long-form content generation without losing coherence or attention. Developers can integrate immediately via our standardized API, enabling first calls within minutes of reading our documentation. The FP16 quantization ensures balanced precision and speed, making it ideal for production pipelines requiring reliable throughput and low latency during peak usage periods.

Engineered for multilingual proficiency, Nemotron 3 Nano excels in both Arabic and English tasks, outperforming comparable models in linguistic nuance and technical accuracy. While specific benchmark tables are available in our documentation, early testing confirms superior retention across long contexts compared to alternative architectures. Operating on a starter tier with a 3x credit multiplier, it offers a cost-effective solution for scaling applications without compromising quality. This proprietary model is production-ready, providing the stability enterprise environments demand while maintaining the flexibility researchers need for experimental workflows and rigorous evaluation pipelines. Business leaders can deploy confidently knowing the proprietary license ensures compliance and data security standards required for enterprise-grade applications.

Specifications

Display Name Nemotron 3 Nano 30B

Family Nemotron

Category Chat

Parameters 30B

Context Window 128,000 tokens

Quantization FP16

License PROPRIETARY

Min Tier Starter

Status Available

Pricing

3×

credits per token

1K 3,000 Credits

10K 30,000 Credits

100K 300,000 Credits

View Pricing Plans

Code Examples

from openai import OpenAI

client = OpenAI(
    base_url="https://llmapi.resayil.io/v1/",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="nemotron-3-nano:30b",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

import anthropic

client = anthropic.Anthropic(
    base_url="https://llmapi.resayil.io/v1",
    api_key="YOUR_API_KEY"
)

message = client.messages.create(
    model="nemotron-3-nano:30b",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(message.content[0].text)

const response = await fetch(
    "https://llmapi.resayil.io/v1/chat/completions",
    {
        method: "POST",
        headers: {
            "Content-Type": "application/json",
            "Authorization": "Bearer YOUR_API_KEY"
        },
        body: JSON.stringify({
            model: "nemotron-3-nano:30b",
            messages: [
                { role: "user", content: "Hello!" }
            ]
        })
    }
);

const data = await response.json();
console.log(data.choices[0].message.content);

curl https://llmapi.resayil.io/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "nemotron-3-nano:30b",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Analyzing extensive legal documents for key clauses

Powering intelligent customer support chatbot interactions

Summarizing lengthy technical reports and research papers

Querying large enterprise knowledge bases efficiently

Generating detailed responses for complex user inquiries

In-Depth Guide

Full Guide

Complete Guide to Nemotron 3 Nano 30B — LLM Resayil

Nemotron 3 Nano 30B