NVIDIA Nemotron 3 — Full Breakdown, Benchmarks & Setup Guide

What is Nemotron 3 — and why it matters

NVIDIA is known as a chip company. They make GPUs. That's their business. So when they released a family of open-weight AI models — with training data, training recipes, and reinforcement learning environments all included — the AI community paid attention.

Nemotron 3 is a family of three open-weight language models built specifically for agentic AI. Not for chat. Not for question answering. For agents — AI systems that plan, use tools, and keep working on long complex tasks without stopping to ask for help.

NVIDIA released it in three stages: Nano in December 2025, Super in March 2026, Ultra in June 2026. All three share the same underlying architecture. All three are genuinely open — weights, training data, training recipes, RL environments. Everything.

"Open innovation is the foundation of AI progress. With Nemotron, we're transforming advanced AI into an open platform that gives developers the transparency and efficiency they need to build." — Jensen Huang, CEO of NVIDIA, December 2025

How the architecture actually works

The key to understanding Nemotron is the architecture — it's called Hybrid Mamba-Transformer MoE. That sounds complicated. It isn't.

MoE stands for Mixture of Experts. Think of it like a company with 31 billion employees. For each task, only 3 billion of them show up to work. The rest stay home. The result — you get the intelligence of a 31B model but the speed and cost of a 3B model.

The Mamba component replaces the standard Transformer attention mechanism for long context. Traditional Transformers get slow and expensive on very long inputs. Mamba handles long sequences efficiently — Nemotron 3 Nano supports a 1 million token context window with 87.5% accuracy maintained at 512K tokens.

Combined — this architecture delivers 4x higher throughput than the previous Nemotron generation at the same accuracy level.

The three models — Nano, Super, Ultra

For most people reading this — Nano is your model. It runs on a 24GB GPU. RTX 3090, RTX 4090, RTX 4080. It's fast. It's free. And it's capable enough for most real-world agentic tasks.

Benchmarks

Coding — SWE-Bench Verified

Long context — Nemotron 3 Nano vs Qwen3 30B

Long context is where Nano wins clearly. 1 million token context window with accuracy maintained across the range — Qwen3 doesn't even support contexts beyond 128K.

Complete setup guide

nvidia-smiCopy

# macOS / Linux
curl -fsSL https://ollama.com/install.sh | sh

# Windows — download from
# https://ollama.com/download/windowsCopy

ollama run nemotron3-nanoCopy

# Install huggingface_hub first
pip install huggingface_hub

# Download the model
huggingface-cli download nvidia/Nemotron-3-Nano-30B-A3B \
  --local-dir ./nemotron-nanoCopy

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:11434/v1",
    api_key="ollama",  # required but ignored locally
)

response = client.chat.completions.create(
    model="nemotron3-nano",
    messages=[
        {"role": "user", "content": "Explain MoE architecture simply."}
    ]
)

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

# Download LM Studio from:
# https://lmstudio.ai

# Then search: nvidia/nemotron-3-nano
# Click download. Click load. Chat.Copy

No GPU? Use the free API

If you don't have a compatible GPU — NVIDIA provides free API access through their AI Catalog. No download needed. No GPU needed. Just an API key.

pip install openai

from openai import OpenAI

client = OpenAI(
    base_url="https://integrate.api.nvidia.com/v1",
    api_key="YOUR_NVIDIA_API_KEY",  # free at build.nvidia.com
)

response = client.chat.completions.create(
    model="nvidia/nemotron-3-nano-30b-a3b",
    messages=[{"role": "user", "content": "Your prompt here"}],
    max_tokens=1024,
)

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

Get your free API key at build.nvidia.com. The free tier is generous enough for development and experimentation.

All official links

Frequently asked questions

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