Llama 3.1 Nemotron Nano 8B
Smallest of the Nemotron reasoning trio. NAS-optimized for inference efficiency on RTX hardware.
Overview
Smallest of the Nemotron reasoning trio. NAS-optimized for inference efficiency on RTX hardware.
Family & lineage
How this model relates to others in its lineage. Family members share architecture and training-data roots; parent / children edges record direct distillation or fine-tune relationships.
Strengths
- RTX-optimized
- Reasoning at 8B
Weaknesses
- NVIDIA license terms
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 4.9 GB | 6 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of Llama 3.1 Nemotron Nano 8B.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run Llama 3.1 Nemotron Nano 8B?
Can I use Llama 3.1 Nemotron Nano 8B commercially?
What's the context length of Llama 3.1 Nemotron Nano 8B?
Source: huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Llama 3.1 Nemotron Nano 8B runs on your specific hardware before committing money.