What is NVIDIA: Nemotron 3 Super?

120B total parameter MoE LLM with 12B active. Uses hybrid Mamba-Transformer for agentic tasks. Supports 1M context length.

How does NVIDIA: Nemotron 3 Super API work?

Integrate via LLM endpoint for inference. Delivers 5x throughput over prior models. Optimized for NVFP4 on Blackwell.

What makes nvidia nemotron 3 super model efficient?

Latent MoE activates 4x experts at same cost. Multi-token prediction speeds generation. 4x memory efficiency from Mamba layers.

Is NVIDIA: Nemotron 3 Super alternative to closed models?

Open weights model leads efficiency benchmarks. Tops Artificial Analysis for same-size accuracy. Suited for multi-agent apps.

What context length supports nvidia: nemotron 3 super api?

Native 1M tokens prevent goal drift in long workflows. Enables coherent multi-step reasoning. Ideal for autonomous agents.

Where to access nvidia nemotron 3 super model?

Available via API here or Hugging Face. Run on NVIDIA hardware for max speed. Fine-tune with open recipes.

Available now on ModelsLab · Language Model

NVIDIA: Nemotron 3 Super
Agentic AI Maximum Efficiency

Try NVIDIA: Nemotron 3 Super API Documentation

Run Nemotron 3 Super

Hybrid MoE

120B Total 12B Active

Activates 12B of 120B parameters via Latent MoE for 5x throughput.

1M Context

Persistent Agent Memory

Handles million-token workflows without goal drift in NVIDIA: Nemotron 3 Super API.

Multi-Token Prediction

3x Faster Inference

Predicts multiple tokens per pass with Mamba-Transformer hybrid backbone.

Examples

See what NVIDIA: Nemotron 3 Super can create

Copy any prompt below and try it yourself in the playground.

Code Review

“Review this Python function for bugs and optimize for performance: def fibonacci(n): if n <= 1: return n else: return fibonacci(n-1) + fibonacci(n-2). Suggest improvements using memoization.”

Data Analysis

“Analyze sales data trends from this CSV snippet: date,sales;2025-01,1000;2025-02,1200;2025-03,900. Forecast Q2 and identify anomalies.”

Tech Summary

“Summarize key innovations in hybrid MoE architectures for LLMs, including throughput gains and context handling up to 1M tokens.”

Workflow Plan

“Plan a multi-step agent workflow for IT ticket triage: classify issue, query database, suggest resolution, escalate if needed.”

For Developers

A few lines of code.
Agentic reasoning. One call.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

Serverless: scales to zero, scales to millions
Pay per token, no minimums
Python and JavaScript SDKs, plus REST API

API Documentation

import requests

response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about NVIDIA: Nemotron 3 Super

Read the docs

Ready to create?

Start generating with NVIDIA: Nemotron 3 Super on ModelsLab.