NVIDIA Nemotron 3 Super: The New Flagship of Open, Efficient Intelligence

NVIDIA Nemotron 3 Super is a 120‑billion‑parameter (12B active) open‑weights reasoning model built on a hybrid Mamba‑Transformer Mixture‑of‑Experts architecture, offering up to a 1‑million‑token context window and industry‑leading throughput for agentic AI systems. It represents NVIDIA’s most capable open model to date, engineered for long‑context reasoning, multi‑agent coordination, and high‑volume enterprise workloads.

Overview

Nemotron 3 Super sits in the middle of NVIDIA’s Nemotron 3 family—Nano, Super, and Ultra—each designed to balance reasoning depth, efficiency, and throughput for modern agentic AI. The Super model is the first in the lineup to combine:

• Hybrid Mamba‑Transformer MoE architecture
• LatentMoE for improved quality
• Multi‑Token Prediction (MTP) for faster generation
• NVFP4 training precision for efficiency
• 1M‑token context window for long‑horizon tasks

These features make Nemotron 3 Super uniquely suited for multi‑agent systems, complex reasoning, and enterprise‑scale automation.

Architecture & Technical Foundations

Hybrid Mamba–Transformer MoE

Nemotron 3 Super uses a Mixture‑of‑Experts hybrid Mamba–Transformer architecture. This design blends:

• Mamba‑2 sequence modeling → efficient long‑context processing
• Transformer attention → strong reasoning and pattern recognition
• LatentMoE → improved quality with fewer active parameters

This hybrid approach enables high throughput and low inference cost, especially in multi‑agent workloads.

Parameter Count

• 120B total parameters
• 12B active parameters during inference

This allows the model to behave like a large model while running with the efficiency of a much smaller one.

Context Window

• Up to 1,000,000 tokens

Ideal for:

• Large codebases
• Multi‑agent memory
• Long‑form documents
• Complex planning tasks

Training & Post‑Training

Nemotron 3 models are post‑trained using multi‑environment reinforcement learning, enabling:

• Multi‑step tool use
• Structured reasoning traces
• Granular reasoning‑budget control

Performance & Benchmarks

Independent evaluations show that Nemotron 3 Super:

• Outperforms gpt‑oss‑120b in intelligence
• Achieves ~10% higher throughput per GPU
• Maintains strong openness and transparent methodology

This makes it one of the most efficient open‑weights models available for large‑scale inference.

Capabilities & Use Cases

1. Agentic AI Systems

Nemotron 3 Super is optimized for multi‑agent coordination, reducing inference cost and context overhead. Use cases include:

• Autonomous workflows
• Multi‑agent planning
• Distributed reasoning

2. Enterprise Automation

• IT ticket automation
• Customer support
• Workflow orchestration

3. Technical & Scientific Reasoning

• Step‑by‑step reasoning
• Tool‑assisted problem solving
• Code generation and debugging

4. Long‑Context Applications

• Large document analysis
• Legal and financial review
• Codebase‑level understanding

Comparison Table

Deployment Considerations

• Memory requirement: ~83 GB RAM for the smallest configuration
• Available in GGUF for local inference
• Optimized for H100 and next‑gen NVIDIA GPUs

FAQ: Nemotron 3 Super

1. What makes Nemotron 3 Super different from other open models?

Its hybrid Mamba‑Transformer MoE architecture and LatentMoE design give it higher throughput and better reasoning quality than similarly sized open models.

2. Is Nemotron 3 Super fully open?

Yes. NVIDIA provides open weights under a permissive license, making it suitable for research and enterprise use.

3. How large is the context window?

Up to 1,000,000 tokens.

4. What hardware do I need to run it?

At least 83 GB of RAM for the smallest GGUF variant; multi‑GPU setups recommended for full performance.

5. What tasks is it best suited for?

• Multi‑agent systems
• Long‑context reasoning
• Enterprise automation
• Coding and tool‑use reasoning

6. How does it compare to GPT‑OSS‑120B?

Nemotron 3 Super shows higher intelligence and ~10% higher throughput per GPU in evaluations.

7. Does it support tool use?

Yes. It generates reasoning traces and supports multi‑step tool use out of the box.