Happy Horse 1.0 is now on ModelsLab

Try Now
Skip to main content
Available now on ModelsLab · Language Model

NVIDIA: Nemotron 3 SuperAgentic AI Maximum Efficiency

Try NVIDIA: Nemotron 3 SuperAPI 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

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

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

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

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

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

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

Ready to create?

Start generating with NVIDIA: Nemotron 3 Super on ModelsLab.

Try NVIDIA: Nemotron 3 SuperAPI Documentation