# NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 GPU requirements

NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 has 32B parameters. To serve it, plan for about 76 GB of GPU memory in 16-bit, 38 GB in 8-bit or 19 GB in 4-bit, including about 20% for the KV cache and activations.

It is a mixture of experts: 128 experts, 6 of them active for each token. All of them stay in GPU memory, so plan memory for all 32B parameters.

[Deploy it on Arrakis](/console/#/models?model=nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16).

## What it takes to serve it

| Precision | Memory and the smallest setup |
|---|---|
| 16-bit | 76 GB: one [H100](/gpus/h100.html.md) or [A100](/gpus/a100.html.md), H100 from $1.65 per GPU-hour |
| 8-bit | 38 GB: one [L40S](/gpus/l40s.html.md), [RTX 6000 Ada](/gpus/rtx-6000-ada.html.md) or [RTX A6000](/gpus/rtx-a6000.html.md), L40S from $0.44 per GPU-hour |
| 4-bit | 19 GB: one [RTX 4090](/gpus/rtx-4090.html.md) or [RTX 3090](/gpus/rtx-3090.html.md), RTX 3090 from $0.19 per GPU-hour |

## What it takes to fine-tune it

| Method | Memory and the smallest setup |
|---|---|
| QLoRA | 22 GB: one [RTX 4090](/gpus/rtx-4090.html.md) or [RTX 3090](/gpus/rtx-3090.html.md), RTX 3090 from $0.19 per GPU-hour |
| LoRA | 76 GB: one [H100](/gpus/h100.html.md) or [A100](/gpus/a100.html.md), H100 from $1.65 per GPU-hour |
| Full fine-tuning | 505 GB: 2 × [B300](/gpus/b300.html.md), from $9.20 per hour for all 2 |

## About the model

| | |
|---|---|
| Parameters | `32B` |
| Active for each token | `3B` |
| Experts | `128` |
| Experts for each token | `6` |
| Context window (tokens) | `262,144` |
| Published in | `BF16` |
| License | `other` |
| Released | `2025-12-04` |
| Weights | [nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16) |

## Serve it yourself

```
vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --tensor-parallel-size 1
```

## Questions

**How much GPU memory does NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 need?** To serve it, about 76 GB in 16-bit, 38 GB in 8-bit, 19 GB in 4-bit, including about 20% for the KV cache and activations. To fine-tune it, about 22 GB with QLoRA, 76 GB with LoRA and 505 GB for full fine-tuning with Adam, before activations.

**Can I run NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 on one GPU?** Yes. In 16-bit it needs about 76 GB, which fits on one H100 or A100.

**What does it cost to run NVIDIA-Nemotron-3-Nano-30B-A3B-BF16?** In 16-bit it needs one H100 or A100; in 4-bit, one RTX 4090 or RTX 3090. Each setup on this page shows the lowest price per GPU-hour on Arrakis right now, and you pay by the hour from a prepaid balance.

**How do I deploy NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 on Arrakis?** Open Models in the Arrakis console, paste nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16, choose vLLM, SGLang or llama.cpp, and rent a GPU it fits on. You get an OpenAI-compatible endpoint with its own API key, and root SSH to the machine.


More models: [/models](/models.html.md).