NVIDIA-Nemotron-Nano-9B-v2-AWQ-8bit

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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 general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (German, French, Italian, Spanish and Japanese) are also supported. Developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Also suitable for typical instruction-following tasks.

Release Date: 08/18/2025

• Huggingface 08/18/2025 via https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2
• API Catalog 08/18/2025 via https://build.nvidia.com/nvidia/nvidia-nemotron-nano-9b-v2

References

• NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model

Input

• Input Type(s): Text
• Input Format(s): String
• Input Parameters: One-Dimensional (1D): Sequences
• Other Properties Related to Input: Context length up to 128K. Supported languages include German, Spanish, French, Italian, Korean, Portuguese, Russian, Japanese, Chinese and English.

Output

• Output Type(s): Text
• Output Format: String
• Output Parameters: One-Dimensional (1D): Sequences up to 128K

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.

Software Integration

• Runtime Engine(s): NeMo 25.07.nemotron-nano-v2
• Supported Hardware Microarchitecture Compatibility: NVIDIA A10G, NVIDIA H100-80GB, NVIDIA A100, Jetson AGX Thor
• Operating System(s): Linux

Use it with Transformers

The snippet below shows how to use this model with Huggingface Transformers (tested on version 4.48.3).

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/NVIDIA-Nemotron-Nano-9B-v2")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/NVIDIA-Nemotron-Nano-9B-v2",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

Case 1: /think or no reasoning signal is provided in the system prompt, reasoning will be set to True

messages = [
    {"role": "system", "content": "/think"},
    {"role": "user", "content": "Write a haiku about GPUs"},
]

Case 2: /no_think is provided, reasoning will be set to False

messages = [
    {"role": "system", "content": "/no_think"},
    {"role": "user", "content": "Write a haiku about GPUs"},
]

Note: /think or /no_think keywords can also be provided in “user” messages for turn-level reasoning control.

The rest of the inference snippet remains the same

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=32,
    eos_token_id=tokenizer.eos_token_id
)
print(tokenizer.decode(outputs[0]))

We recommend setting temperature to 0.6, top_p to 0.95 for reasoning True and greedy search for reasoning False, and increase max_new_tokens to 1024 or higher for reasoning True.

Use it with TRT-LLM

The snippet below shows how to use this model with TRT-LLM. We tested this on the following commit and followed these instructions to build and install TRT-LLM in a docker container.

from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
    disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
    enable_block_reuse=False,
)

model_id = "nvidia/NVIDIA-Nemotron-Nano-9B-v2"
tokenizer = AutoTokenizer.from_pretrained(model_id)

llm = LLM(
    model=model_id,
    max_seq_len=32678,
    max_batch_size=4,
    pytorch_backend_config=pytorch_config,
    kv_cache_config=kv_cache_config,
    tensor_parallel_size=8,
)
messages = [
    {"role": "system",  "content": "/think"},
    {"role": "user", "content": "Write a haiku about GPUs"},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
sampling_params = SamplingParams(
    max_tokens=512,
    temperature=0.6,
    top_p=0.95,
    add_special_tokens=False,
)
outputs = llm.generate([prompt], sampling_params)
print(outputs[0].outputs[0].text)

Use it with vLLM

The snippet below shows how to use this model with vLLM. Use the latest version of vLLM and follow these instructions to build and install vLLM.

pip install -U "vllm>=0.10.1"

Now you can run run the server with:

vllm serve nvidia/NVIDIA-Nemotron-Nano-9B-v2 \
    --trust-remote-code \
    --max-num-seqs 64 \
    --mamba_ssm_cache_dtype float32

Note:

• Remember to add `--mamba_ssm_cache_dtype float32` for accurate quality. Without this option, the model’s accuracy may degrade.
• If you encounter a CUDA OOM issue, try --max-num-seqs 64 and consider lower the value further if the error persists.

Alternativly, you can use Docker to launch a vLLM server.

export TP_SIZE=1  # Adjust this value based on the number of GPUs you want to use
docker run --runtime nvidia --gpus all \
           -v ~/.cache/huggingface:/root/.cache/huggingface \
           --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
           -p 8000:8000 \
           --ipc=host \
           vllm/vllm-openai:v0.10.1 \
           --model nvidia/NVIDIA-Nemotron-Nano-9B-v2 \
           --tensor-parallel-size ${TP_SIZE} \
           --max-num-seqs 64 \
           --max-model-len 131072 \
           --trust-remote-code \
           --mamba_ssm_cache_dtype float32

For Jetson AGX Thor, please use this vLLM container.

Using Budget Control with a vLLM Server

The thinking budget allows developers to keep accuracy high and meet response‑time targets - which is especially crucial for customer support, autonomous agent steps, and edge devices where every millisecond counts.

With budget control, you can set a limit for internal reasoning:

• max_thinking_tokens: This is a threshold that will attempt to end the reasoning trace at the next newline encountered in the reasoning trace. If no newline is encountered within 500 tokens, it will abruptly end the reasoning trace at `max_thinking_tokens + 500`.

Start a vLLM server:

vllm serve nvidia/NVIDIA-Nemotron-Nano-9B-v2 \
    --trust-remote-code \
    --mamba_ssm_cache_dtype float32

Client for supporting budget control:

from typing import Any, Dict, List

import openai
from transformers import AutoTokenizer


class ThinkingBudgetClient:
   def __init__(self, base_url: str, api_key: str, tokenizer_name_or_path: str):
       self.base_url = base_url
       self.api_key = api_key
       self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path)
       self.client = openai.OpenAI(base_url=self.base_url, api_key=self.api_key)


   def chat_completion(
       self,
       model: str,
       messages: List[Dict[str, Any]],
       max_thinking_budget: int = 512,
       max_tokens: int = 1024,
       **kwargs,
   ) -> Dict[str, Any]:
       assert (
           max_tokens > max_thinking_budget
       ), f"thinking budget must be smaller than maximum new tokens. Given {max_tokens=} and {max_thinking_budget=}"


       # 1. first call chat completion to get reasoning content
       response = self.client.chat.completions.create(
           model=model, messages=messages, max_tokens=max_thinking_budget, **kwargs
       )
       content = response.choices[0].message.content


       reasoning_content = content
       if not "</think>" in reasoning_content:
           # reasoning content is too long, closed with a period (.)
           reasoning_content = f"{reasoning_content}.\n</think>\n\n"
       reasoning_tokens_len = len(
           self.tokenizer.encode(reasoning_content, add_special_tokens=False)
       )
       remaining_tokens = max_tokens - reasoning_tokens_len
       assert (
           remaining_tokens > 0
       ), f"remaining tokens must be positive. Given {remaining_tokens=}. Increase the max_tokens or lower the max_thinking_budget."


       # 2. append reasoning content to messages and call completion
       messages.append({"role": "assistant", "content": reasoning_content})
       prompt = self.tokenizer.apply_chat_template(
           messages,
           tokenize=False,
           continue_final_message=True,
       )
       response = self.client.completions.create(
           model=model, prompt=prompt, max_tokens=remaining_tokens, **kwargs
       )


       response_data = {
           "reasoning_content": reasoning_content.strip().strip("</think>").strip(),
           "content": response.choices[0].text,
           "finish_reason": response.choices[0].finish_reason,
       }
       return response_data

Calling the server with a budget (Restricted to 32 tokens here as an example)

tokenizer_name_or_path = "nvidia/NVIDIA-Nemotron-Nano-9B-v2"
client = ThinkingBudgetClient(
   base_url="http://localhost:8000/v1",  # Nano 9B v2 deployed in thinking mode
   api_key="EMPTY",
   tokenizer_name_or_path=tokenizer_name_or_path,
)


result = client.chat_completion(
   model="nvidia/NVIDIA-Nemotron-Nano-9B-v2",
   messages=[
       {"role": "system", "content": "You are a helpful assistant. /think"},
       {"role": "user", "content": "What is 2+2?"},
   ],
   max_thinking_budget=32,
   max_tokens=512,
   temperature=0.6,
   top_p=0.95,
)
print(result)

You should see output similar to the following:

{'reasoning_content': "Okay, the user asked, What is 2+2? Let me think. Well, 2 plus 2 equals 4. That's a basic.", 'content': '2 + 2 equals **4**.\n', 'finish_reason': 'stop'}

Using Tool-Calling with a vLLM Server

Start a vLLM server with native tool-calling:

git clone https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2

vllm serve nvidia/NVIDIA-Nemotron-Nano-9B-v2 \
  --trust-remote-code \
  --mamba_ssm_cache_dtype float32 \
  --enable-auto-tool-choice \
  --tool-parser-plugin "NVIDIA-Nemotron-Nano-9B-v2/nemotron_toolcall_parser_no_streaming.py" \
  --tool-call-parser "nemotron_json"

After launching a vLLM server, you can call the server with tool-call support using a Python script like below:

from openai import OpenAI

client = OpenAI(
    base_url="http://0.0.0.0:5000/v1",
    api_key="dummy",
)

completion = client.chat.completions.create(
    model="nvidia/NVIDIA-Nemotron-Nano-9B-v2",
    messages=[
        {"role": "system", "content": ""},
        {"role": "user", "content": "My bill is $100. What will be the amount for 18% tip?"}
    ],
    tools=[
        {
            "type": "function",
            "function": {
                "name": "calculate_tip",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "bill_total": {
                            "type": "integer",
                            "description": "The total amount of the bill"
                        },
                        "tip_percentage": {
                            "type": "integer",
                            "description": "The percentage of tip to be applied"
                        }
                    },
                    "required": ["bill_total", "tip_percentage"]
                }
            }
        },
        {
            "type": "function",
            "function": {
                "name": "convert_currency",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "amount": {
                            "type": "integer",
                            "description": "The amount to be converted"
                        },
                        "from_currency": {
                            "type": "string",
                            "description": "The currency code to convert from"
                        },
                        "to_currency": {
                            "type": "string",
                            "description": "The currency code to convert to"
                        }
                    },
                    "required": ["from_currency", "amount", "to_currency"]
                }
            }
        }
    ],
    temperature=0.6,
    top_p=0.95,
    max_tokens=32768,
    stream=False
)

print(completion.choices[0].message.content)
print(completion.choices[0].message.tool_calls)

You should see output similar to the following:

<think>
Okay, let's see. The user has a bill of $100 and wants to know the amount for an 18% tip. Hmm, I need to calculate the tip based on the bill total and the percentage. The tools provided include calculate_tip, which takes bill_total and tip_percentage as parameters. So the bill_total here is 100, and the tip_percentage is 18. I should call the calculate_tip function with these values. Wait, do I need to check if the parameters are integers? The bill is $100, which is an integer, and 18% is also an integer. So that fits the function's requirements. I don't need to convert any currency here because the user is asking about a tip in the same currency. So the correct tool to use is calculate_tip with those parameters.
</think>

[ChatCompletionMessageToolCall(id='chatcmpl-tool-e341c6954d2c48c2a0e9071c7bdefd8b', function=Function(arguments='{"bill_total": 100, "tip_percentage": 18}', name='calculate_tip'), type='function')]

Model Version

• v1.0

Prompt Format

We follow the jinja chat template provided below. This template conditionally adds <think>\n to the start of the Assistant response if /think is found in either the system prompt or any user message. If no reasoning signal is added, the model defaults to reasoning "on" mode. The chat template adds <think></think> to the start of the Assistant response if /no_think is found in the system prompt. Thus enforcing reasoning on/off behavior.

{%- set ns = namespace(enable_thinking = true) %}

{%- for message in messages -%}
    {%- set content = message['content'] -%}
    {%- if message['role'] == 'user' or message['role'] == 'system' -%}
        {%- if '/think' in content -%}
            {%- set ns.enable_thinking = true -%}
        {%- elif '/no_think' in content -%}
            {%- set ns.enable_thinking = false -%}
        {%- endif -%}
    {%- endif -%}
{%- endfor -%}

{%- if messages[0]['role'] != 'system' -%}
    {%- set ns.non_tool_system_content = '' -%}
    {{- '<SPECIAL_10>System\n' -}}
{%- else -%}
    {%- set ns.non_tool_system_content = messages[0]['content']
        .replace('/think', '')
        .replace('/no_think', '')
        .strip()
    -%}
    {{- '<SPECIAL_10>System\n' + ns.non_tool_system_content }}
{%- endif -%}

{%- if tools -%}
    {%- if ns.non_tool_system_content is defined and ns.non_tool_system_content != '' -%}
        {{- '\n\n' -}}
    {%- endif -%}

    {{- 'You can use the following tools to assist the user if required:' -}}
    {{- '\n<AVAILABLE_TOOLS>[' -}}
    {%- for tool in tools -%}
        {{- (tool.function if tool.function is defined else tool) | tojson -}}
        {{- ', ' if not loop.last else '' -}}
    {%- endfor -%}
    {{- ']</AVAILABLE_TOOLS>\n\n' -}}

    {{- 'If you decide to call any tool(s), use the following format:\n' -}}
    {{- '<TOOLCALL>[{{"name": "tool_name1", "arguments": "tool_args1"}}, ' -}}
    {{- '{{"name": "tool_name2", "arguments": "tool_args2"}}]</TOOLCALL>\n\n' -}}

    {{- 'The user will execute tool-calls and return responses from tool(s) in this format:\n' -}}
    {{- '<TOOL_RESPONSE>[{{"tool_response1"}}, {{"tool_response2"}}]</TOOL_RESPONSE>\n\n' -}}

    {{- 'Based on the tool responses, you can call additional tools if needed, correct tool calls if any errors are found, or just respond to the user.' -}}
{%- endif -%}

{{- '\n' -}}

{%- set messages = messages[1:] if messages[0]['role'] == 'system' else messages -%}

{%- if messages[-1]['role'] == 'assistant' -%}
    {%- set ns.last_turn_assistant_content = messages[-1]['content'].strip() -%}
    {%- set messages = messages[:-1] -%}
{%- endif -%}

{%- for message in messages -%}
    {%- set content = message['content'] -%}

    {%- if message['role'] == 'user' -%}
        {{- '<SPECIAL_11>User\n' + content.replace('/think', '').replace('/no_think', '').strip() + '\n' }}

    {%- elif message['role'] == 'tool' -%}
        {%- if loop.first or (messages[loop.index0 - 1].role != 'tool') -%}
            {{- '<SPECIAL_11>User\n' + '<TOOL_RESPONSE>[' }}
        {%- endif -%}
        {{- message['content'] -}}
        {{- ', ' if not loop.last and (messages[loop.index0 + 1].role == 'tool') else '' -}}
        {%- if loop.last or (messages[loop.index0 + 1].role != 'tool') -%}
            {{- ']</TOOL_RESPONSE>\n' -}}
        {%- endif -%}

    {%- elif message['role'] == 'assistant' -%}
        {%- if '</think>' in content -%}
            {%- set content = content.split('</think>')[1].strip() %}
        {%- endif -%}

        {{- '<SPECIAL_11>Assistant\n' + content.strip() }}

        {%- if message.tool_calls -%}
            {%- if content.strip() != '' -%}
                {{- '\n\n' -}}
            {%- endif -%}
            {{- '<TOOLCALL>[' -}}
            {%- for call in message.tool_calls -%}
                {%- set fn = call.function if call.function is defined else call -%}
                {{- '{"name": "' + fn.name + '", "arguments": ' -}}
                {%- if fn.arguments is string -%}
                    {{- fn.arguments -}}
                {%- else -%}
                    {{- fn.arguments | tojson -}}
                {%- endif -%}
                {{- '}' + (', ' if not loop.last else '') -}}
            {%- endfor -%}
            {{- ']</TOOLCALL>' -}}
        {%- endif -%}

        {{- '\n<SPECIAL_12>\n' -}}
    {%- endif -%}
{%- endfor -%}

{%- if add_generation_prompt -%}
    {{- '<SPECIAL_11>Assistant\n' -}}
    {%- if ns.enable_thinking is defined and ns.enable_thinking is false -%}
        {{- '<think></think>' -}}
    {%- else -%}
        {{- '<think>\n' -}}
    {%- endif -%}
    {%- if ns.last_turn_assistant_content is defined and ns.last_turn_assistant_content != '' -%}
        {{- ns.last_turn_assistant_content -}}
    {%- endif -%}

{%- else -%}
    {%- if ns.last_turn_assistant_content is defined and ns.last_turn_assistant_content != '' -%}
        {{- '<SPECIAL_11>Assistant\n' -}}
        {%- if ns.enable_thinking is defined and ns.enable_thinking is false -%}
            {{- '<think></think>' -}}
        {%- else -%}
            {{- '<think>\n' -}}
        {%- endif -%}
        {{- ns.last_turn_assistant_content -}}

        {%- if continue_final_message is defined -%}
            {%- if continue_final_message is false -%}
                {{- '\n<SPECIAL_12>\n' -}}
            {%- endif -%}
        {%- else -%}
            {{- '\n<SPECIAL_12>\n' -}}
        {%- endif -%}
    {%- endif -%}
{%- endif -%}

Training, Testing, and Evaluation Datasets

Training datasets

• 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-Nano-9B-v2 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.

The pre-training corpus for NVIDIA-Nemotron-Nano-9B-v2 consists of high-quality curated and synthetically-generated data. It is trained in the English language, as well as 15 multilingual languages and 43 programming languages. Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. The corpus spans domains including legal, math, science, finance, and more. We also include a small portion of question-answering, and alignment style data to improve model accuracy. The model was pre-trained for approximately twenty trillion tokens.

Alongside the model, we release our final pretraining data, as outlined in this section. For ease of analysis, there is a sample set that is ungated. For all remaining code, math and multilingual data, gating and approval is required, and the dataset is permissively licensed for model training purposes.

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 .

Public Datasets

Private Non-publicly Accessible Datasets of Third Parties

Online Dataset Sources

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).

NVIDIA-Sourced Synthetic Datasets

Evaluation Dataset:

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

Inference

• Engines: HF, vLLM, TRT-LLM

• Test Hardware NVIDIA A10G 24GB, H100 80GB, Jetson AGX Thor

Ethical Considerations

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Citation

@misc{nvidia2025nvidianemotronnano2,
      title={NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model},
      author={NVIDIA},
      year={2025},
      eprint={2508.14444},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2508.14444},
}