How to Run NVIDIA’s Nemotron 3 Nano Locally on 24GB RAM

Released on December 15, 2025, NVIDIA’s Nemotron 3 Nano represents a breakthrough in efficient AI model deployment. This 30B-parameter hybrid reasoning model delivers exceptional performance while running comfortably on just 24GB of RAM—making it accessible to developers without access to expensive GPU clusters. With up to 3.3x higher inference throughput than competing models like Qwen3-30B and support for 1M-token context windows, Nemotron 3 Nano is positioned as the ideal solution for cost-effective AI agent development and local deployment.

What makes Nemotron 3 Nano special?

Nemotron 3 Nano employs a sophisticated hybrid Mamba-Transformer Mixture-of-Experts (MoE) architecture that combines the efficiency of Mamba-2 for long-context processing with the precision of transformer attention layers for detailed reasoning. The model contains 31.6B total parameters but activates only approximately 3.6B parameters per token, achieving remarkable efficiency without sacrificing accuracy.

Key architectural innovations include:

• Hybrid MoE design: 23 Mamba-2 layers combined with 6 attention layers and MoE routing
• Intelligent parameter activation: 6 of 128 experts activated per token for optimal performance
• NoPE training: Eliminates positional embeddings, enabling seamless context extension up to 1M tokens
• Reasoning controls: Configurable thinking budget with ON/OFF modes for cost optimization

Hardware requirements and compatibility

One of Nemotron 3 Nano’s most compelling features is its modest hardware requirements. The model can run effectively on systems with:

• Minimum RAM: 24GB (unified memory or VRAM)
• GPU support: NVIDIA RTX 3090/4090, RTX 6000 Ada, H100, A100, or any CUDA-capable GPU
• CPU-only operation: Compatible with modern multi-core processors
• Edge devices: Supported on NVIDIA Jetson platforms and RTX AI PCs

For optimal performance, NVIDIA recommends using CUDA-enabled hardware, but the model runs effectively on CPU-only systems with sufficient RAM. The GGUF quantization format allows for flexible deployment across various hardware configurations.

Step-by-step setup guide

Follow this comprehensive guide to get Nemotron 3 Nano running on your local system with 24GB RAM.

Prerequisites

Before beginning, ensure your system meets these requirements:

• Linux, Windows, or macOS operating system
• 24GB available RAM (system RAM or GPU VRAM)
• Python 3.8+ installed
• Git installed
• CUDA toolkit (recommended for GPU acceleration)

Method 1: Using llama.cpp (Recommended for most users)

llama.cpp provides the most accessible way to run Nemotron 3 Nano locally. Follow these steps:

1. Install dependencies:

   sudo apt-get update
   sudo apt-get install build-essential cmake curl libcurl4-openssl-dev

2. Clone and build llama.cpp with Nemotron 3 support:

   git clone https://github.com/ggml-org/llama.cpp
   cd llama.cpp
   git fetch origin pull/18058/head:nemotron-support
   git checkout nemotron-support
   mkdir build && cd build
   cmake .. -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON
   cmake --build . --config Release -j

3. Download the quantized model:

   pip install huggingface_hub hf_transfer
   export HF_HUB_ENABLE_HF_TRANSFER=1
   huggingface-cli download unsloth/Nemotron-3-Nano-30B-A3B-GGUF Nemotron-3-Nano-30B-A3B-UD-Q4_K_XL.gguf --local-dir ./models

4. Run the model:

   ./llama-cli -m models/Nemotron-3-Nano-30B-A3B-UD-Q4_K_XL.gguf \
     --threads -1 \
     --ctx-size 16384 \
     --n-gpu-layers 99 \
     --temp 1.0 \
     --top-p 1.0 \
     --jinja

Method 2: Using Transformers with Python

For developers preferring Python integration, here’s the Transformers approach:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

# Example inference
messages = [
    {"role": "user", "content": "Write a Python function to calculate factorial"},
]

tokenized_chat = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    tokenized_chat,
    max_new_tokens=1024,
    temperature=1.0,
    top_p=1.0,
    eos_token_id=tokenizer.eos_token_id
)
print(tokenizer.decode(outputs[0]))

Performance optimization tips

To maximize Nemotron 3 Nano’s performance on your 24GB system:

• Use quantization: GGUF Q4_K_XL provides excellent quality-to-size ratio
• Adjust context size: Start with 16K context and increase as needed
• Enable GPU layers: Use –n-gpu-layers to offload computation to GPU
• Optimize temperature settings: Use temperature=1.0, top_p=1.0 for reasoning tasks
• Monitor memory usage: Keep an eye on RAM/VRAM usage during inference

Benchmark comparison: Nemotron 3 Nano vs Qwen3-30B

Nemotron 3 Nano demonstrates significant advantages over competing models:

These benchmarks demonstrate Nemotron 3 Nano’s superior performance across coding, reasoning, and throughput metrics while maintaining competitive accuracy.

Use cases and applications

Nemotron 3 Nano excels in several key applications:

• AI agent development: Multi-step reasoning and tool calling capabilities
• Coding assistance: Superior performance on SWE-Bench and coding tasks
• Long-context RAG: 1M token support for comprehensive document analysis
• Multi-language support: English, German, Spanish, French, Italian, Japanese
• Cost-effective deployment: Ideal for startups and individual developers

Troubleshooting common issues

If you encounter issues during setup:

• Out of memory errors: Reduce context size or use more aggressive quantization
• Slow inference: Enable GPU acceleration or reduce model layers
• Installation failures: Ensure all dependencies are installed correctly
• Model not loading: Verify model file integrity and compatibility

Conclusion

NVIDIA’s Nemotron 3 Nano represents a significant advancement in accessible AI deployment. With its ability to run effectively on 24GB RAM systems while delivering up to 3.3x higher throughput than competitors like Qwen3-30B, it provides an ideal solution for developers seeking cost-effective AI capabilities. The hybrid Mamba-Transformer MoE architecture, combined with 1M-token context support and sophisticated reasoning controls, makes Nemotron 3 Nano a versatile tool for AI agent development, coding assistance, and long-context applications.

By following the setup guides provided, you can quickly deploy Nemotron 3 Nano on your local system and begin leveraging its advanced capabilities. Whether you’re building AI agents, developing coding tools, or exploring long-context applications, Nemotron 3 Nano offers an exceptional balance of performance, efficiency, and accessibility that makes advanced AI capabilities available to a wider range of developers.

The model is available now on Hugging Face under the NVIDIA Open Model License, with comprehensive documentation and community support available through NVIDIA’s developer resources and the broader open-source community.