Model reference · open weights
NVIDIA-Nemotron-Nano is an open-weight language model from nvidia, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
NVIDIA-Nemotron-Nano-9B-v2 Model Developer: NVIDIA Corporation Model Dates: June 2025 \- August 2025 Data Freshness: September 2024 The pretraining data has a cutoff date of September 2024. Model Overview NVIDIA-Nemotron-Nano-9B-v2 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so, albeit with a slight decrease in accuracy for harder prompts that require reasoning. Conversely, allowing the model to generate reasoning traces first generally results in higher-quality final solutions to queries and tasks. The model uses a hybrid architecture consisting primarily of Mamba-2 and MLP layers combined with just four Attention layers. For the architecture, please refer to the Nemotron-H tech report. The model was trained using Megatron-LM and NeMo-RL. The supported languages include: English, German, Spanish, French, Italian, and Japanese. Improved using Qwen. This model is ready for commercial use. Feature Voting We want to hear from you! Share your ideas, vote on what matters, and help shape the future of Nemotron. License/Terms of Use Governing Terms: Use of this model is governed by the NVIDIA Open Model License Agreement. Evaluation Results Benchmark Results (Reasoning On) We evaluated our model in Reasoning-On mode across all benchmarks, except RULER, which is evaluated in Reasoning-Off mode. All evaluations were done using NeMo-Skills. We published a tutorial with all details necessary to reproduce our evaluation results. Reasoning Budget Control This model supports runtime “thinking” budget control. During inference, the user can specify how many tokens the model is allowed to "think". Model Architecture - Architecture Type: Mamba2-Transformer Hybrid - Network Architecture: Nemotron-Hybrid Deployment Geography: Global Use Case NVIDIA-Nemotron-Nano-9B-v2 is a ge
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | nvidia |
|---|---|
| Type | Language models |
| Parameters (lead) | 8.9B |
| Variants | 1 |
| Runs with | transformers |
| Based on | nvidia/NVIDIA-Nemotron-Nano-12B-v2-Base, nvidia/NVIDIA-Nemotron-Nano-12B-v2 |
| Released | 2025-08-12 |
| Popularity | 412k downloads / month |
| Likes | 515 |
| Licence | Commercial licence needed |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| NVIDIA-Nemotron-Nano-9B-v2 | 8.9B | BF16 | ~20.4 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys nvidia-nemotron-nano for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (nvidia-nemotron-nano below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"nvidia-nemotron-nano","messages":[{"role":"user","content":"Hello"}]}'
Details
Languages
Trained / evaluated on
Tags
Papers
Licence
The weights are open but its licence needs a commercial agreement for business use. AxForge can arrange that licence and host the model for you — you pay AxForge, we settle with the model’s maker. Ask us for a quote. Read the licence ↗