NVIDIA Releases Nemotron-3-Nano-4B, an Edge-Ready Hybrid Reasoning Model

Model Developer: NVIDIA Corporation

Model Dates:

Dec 2025 - Jan 2026

Data Freshness:

September 2024

The pretraining data has a cutoff date of September 2024.

NVIDIA-Nemotron-3-Nano-4B-GGUF is quantized version (Q4_K_M) of NVIDIA-Nemotron-3-Nano-4B-BF16 and is a small language model (SLM) 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 has been compressed from NVIDIA-Nemotron-Nano-9B-v2 using the Nemotron Elastic framework. The details of the parent model NVIDIA-Nemotron-Nano-9B-v2 can be found in (Nemotron-H tech report). The model uses a hybrid architecture consisting primarily of Mamba-2 and MLP layers combined with just four Attention layers.

The supported languages include: English. Improved using Qwen.

This model is ready for commercial use.

Governing Terms: Use of this model is governed by the NVIDIA Nemotron Open Model License.

We evaluated our model in Reasoning-off mode across these benchmarks

All evaluations were done using NeMo-Skills & Orak. For Orak we evaluated on three games (Super Mario, Darkest Dungeon & StarDew Valley)

NVIDIA-Nemotron-3-Nano-4B is an edge-ready small language model intended for Agentic AI in edge platforms (Jetson Thor, GeForce RTX, DGX Spark). It targets key-uses including AI gaming NPCs (teammates / companions), local voice assistants (for devices, apps, and games), and IoT automation. It is to be used in English and coding languages.

Huggingface 3/16/2026 via https://huggingface.co/

• Architecture Type: Mamba2-Transformer Hybrid

• Network Architecture: Nemotron-Hybrid

  • This model was compressed from nvidia/NVIDIA-Nemotron-Nano-9B-v2
  • Number of model parameters 3.97 x 10^9

• Input Type(s): Text

• Input Format(s): String

• Input Parameters: One-Dimensional (1D): Sequences

• Other Properties Related to Input: Context length up to 262K. Supported languages include English.

• Output Type(s): Text

• Output Format: String

• Output Parameters: One-Dimensional (1D): Sequences

• Other properties Related to Output: Sequences up to 262K

Our models are designed and optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

• Runtime Engine(s): NeMo 25.07
• Supported Hardware Microarchitecture Compatibility: NVIDIA A10G, NVIDIA H100-80GB, NVIDIA A100, GeForce RTX
• Operating System(s): Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Launch OpenAI-compatible API server

./llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF:Q4_K_M -c 0 --alias my_model -ngl 999 --port 5000 --host 0.0.0.0

• v1.0

• Data Modality: Text

• Text Training Data Size: More than 10 Trillion Tokens

• Train/Test/Valid Split: We used 100% of the corpus for pre-training and relied on external benchmarks for testing.

• Data Collection Method by dataset: Hybrid: Automated, Human, Synthetic

• Labeling Method by dataset: Hybrid: Automated, Human, Synthetic

Properties: The post-training corpus for NVIDIA-Nemotron-3-Nano-4B consists of English and multilingual text (German, Spanish, French, Italian, Korean, Portuguese, Russian, Japanese, Chinese and English). Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. The corpus spans domains including code, legal, math, science, finance, and more. We also include a small portion of question-answering, and alignment style data to improve model accuracies. For several of the domains listed above we used synthetic data, specifically reasoning traces, from DeepSeek R1/R1-0528, Qwen3-235B-A22B, Nemotron 4 340B, Qwen2.5-32B-Instruct-AWQ, Qwen2.5-14B-Instruct, Qwen 2.5 72B.

More details on the datasets and synthetic data generation methods can be found in the technical report NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model .

The English Common Crawl data was downloaded from the Common Crawl Foundation (see their FAQ for details on their crawling) and includes the snapshots CC-MAIN-2013-20 through CC-MAIN-2025-13. The data was subsequently deduplicated and filtered in various ways described in the Nemotron-CC paper.

Additionally, we extracted data for fifteen languages from the following three Common Crawl snapshots: CC-MAIN-2024-51, CC-MAIN-2025-08, CC-MAIN-2025-18. The fifteen languages included were Arabic, Chinese, Danish, Dutch, French, German, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Swedish, and Thai. As we did not have reliable multilingual model-based quality classifiers available, we applied just heuristic filtering instead—similar to what we did for lower quality English data in the Nemotron-CC pipeline, but selectively removing some filters for some languages that did not work well. Deduplication was done in the same way as for Nemotron-CC.

The GitHub Crawl was collected using the GitHub REST API and the Amazon S3 API. Each crawl was operated in accordance with the rate limits set by its respective source, either GitHub or S3. We collect raw source code and subsequently remove any having a license which does not exist in our permissive-license set (for additional details, refer to the technical report).

• Data Collection Method by dataset: Hybrid: Human, Synthetic

• Labeling Method by dataset: Hybrid: Automated, Human, Synthetic

• Engines: HF, vLLM, llama-cpp, TRT-LLM, SGLang

• Test Hardware: NVIDIA GeForce RTX, H100 80GB, DGX Spark, Jetson Thor/Orin Nano.

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