Overview

Nemotron 3 Nano 30B delivers high-performance inference optimized for complex reasoning and extended context understanding. With 30 billion parameters and a 128,000-token context window, this model excels at processing lengthy documents and maintaining coherence across multi-turn conversations. Developers can integrate it immediately via our standard API endpoints, ensuring seamless deployment into existing pipelines without extensive configuration. The FP16 precision balances accuracy with efficiency, making it ideal for production environments requiring reliable output quality. Benchmark testing confirms superior performance on both English and Arabic tasks compared to similar parameter classes, validating its suitability for research pipelines requiring strict linguistic accuracy.

Designed for enterprise-grade applications, this model offers robust bilingual proficiency catering to diverse linguistic requirements. The proprietary license ensures commercial viability, while the starter tier accessibility allows teams to validate performance before scaling. Operating at a 3x credit multiplier, it provides a transparent cost structure for high-volume tasks without compromising on sophistication. Choose Nemotron 3 Nano 30B to leverage cutting-edge architecture that meets strict production standards while supporting critical regional language nuances. Our platform ensures you have immediate access to pricing details and capability metrics, empowering decision-makers to approve budgets confidently without additional sales consultations.

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 long documents and contracts for key insights

Customer support chatbot interactions and query resolution

Code review and debugging assistance for developers

Summarizing lengthy meeting transcripts into actionable points

Enterprise knowledge retrieval using large context windows

In-Depth Guide

Full Guide

Complete Guide to Nemotron 3 Nano 30B — LLM Resayil

Nemotron 3 Nano 30B