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GOVERNING DOWNLOAD TERMS: Use of the model is governed by the OpenMDW License Agreement, version 1.1.

ADDITIONAL INFORMATION: Apache License, Version 2.0.

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NVIDIA-Nemotron-3.5-Bolt-E4B-4GB-BF16

\n \n $\"Homepage\"$ \n \n \n $\"Discord\"$ \n \n \n $\"Paper\"$ \n \n

\n \n $\"License\"$ \n \n

Description

NVIDIA-Nemotron-3.5-Bolt-E4B-4GB-BF16 is a small language model (SLM) further post-trained from Gemma-4-E4B-it by NVIDIA. This is the full-precision (BF16) release. Nemotron-3.5-Bolt-E4B-4GB carries forward Gemma’s strong conversational capabilities and further improves multi-turn tool use, instruction following, and input grounding for fast local voice/device assistants, interactive personas, and embedded/Edge interfaces. Designed for low-memory, low-TTFT operation, the model allows agents to respond quickly while preserving headroom for applications or adjacent model pipelines.

This model is ready for commercial or non‑commercial use.

Model Summary

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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Total Parameters	8B (4B effective)
Architecture	Gemma-4-E4B
Precision	BF16
Context Length	Input: up to 64K tokens; output: up to 64K tokens
Inputs / Outputs	Text / text
Deployment	vLLM; full-precision customization, post-training, and evaluation
Reasoning Mode	Chat-template controls (`enable_thinking=True/False`) and runtime thinking budget
Conversational Mode	Chat-template controls (`conversational_mode=True/False`)
Recommended Sampling	Temperature 1.0, top_p 0.95 in both thinking modes
Best For	Local voice and device assistants, interactive personas, and embedded interfaces
License	OpenMDW-1.1

Model Overview

Model Developer: NVIDIA Corporation

Model Dates: December 2025 - May 2026

Data Freshness:

The post-training data has a cutoff date of May 2026.

What is Nemotron?

NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents.

Quick Start

To get quickly started on a single H100 you can use the following command.

Run it with vLLM! (vLLM v0.31.0: vllm/vllm-openai:v0.31.0)

Without Speculative Decoding

export MODEL=nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB-BF16\n\nvllm serve \"$MODEL\" \\\n  --served-model-name \"nemotron-bolt\" \\\n  --host 0.0.0.0 \\\n  --port 8000 \\\n  --dtype bfloat16 \\\n  --tensor-parallel-size 1 \\\n  --gpu-memory-utilization 0.85 \\\n  --kv-cache-dtype auto \\\n  --max-model-len 65536 \\\n  --max-num-seqs 64 \\\n  --max-num-batched-tokens 65536 \\\n  --chat-template \"$MODEL/chat_template.jinja\" \\\n  --enable-auto-tool-choice \\\n  --tool-call-parser gemma4 \\\n  --reasoning-parser gemma4 \\\n  --limit-mm-per-prompt '{\"image\":0,\"video\":0,\"audio\":0}'\n

With DFlash Speculative Decoding

Use the same downloaded BF16 checkpoint with its companion DFlash drafter. Run one serving configuration at a time. This example uses a single concurrent sequence and seven speculative tokens; tune these settings for your workload.

export MODEL=nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB-BF16\nexport DRAFTER_CKPT=nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB-DFlash-BF16\n\nvllm serve \"$MODEL\" \\\n  --served-model-name \"nemotron-bolt\" \\\n  --host 0.0.0.0 \\\n  --port 8000 \\\n  --dtype bfloat16 \\\n  --tensor-parallel-size 1 \\\n  --gpu-memory-utilization 0.85 \\\n  --kv-cache-dtype auto \\\n  --max-model-len 65536 \\\n  --max-num-seqs 1 \\\n  --max-num-batched-tokens 65536 \\\n  --chat-template \"$MODEL/chat_template.jinja\" \\\n  --enable-auto-tool-choice \\\n  --tool-call-parser gemma4 \\\n  --reasoning-parser gemma4 \\\n  --limit-mm-per-prompt '{\"image\":0,\"video\":0,\"audio\":0}' \\\n  --speculative-config \"{\n    \\\"method\\\": \\\"dflash\\\",\n    \\\"model\\\": \\\"$DRAFTER_CKPT\\\",\n    \\\"num_speculative_tokens\\\": 7\n  }\"\n

Sample Request

The same request works with either serving configuration.

:8000/v1/chat/completions" \\\n -H 'Content-Type: application/json' \\\n -d '{\n "model": "nemotron-bolt",\n "messages": [\n {"role": "user", "content": "What is 17 multiplied by 23?"}\n ],\n "temperature": 0.0,\n "max_tokens": 512,\n "skip_special_tokens": false,\n "chat_template_kwargs": {\n "enable_thinking": true\n }\n }'\n\">

curl -s \"http://<host>:8000/v1/chat/completions\" \\\n  -H 'Content-Type: application/json' \\\n  -d '{\n    \"model\": \"nemotron-bolt\",\n    \"messages\": [\n      {\"role\": \"user\", \"content\": \"What is 17 multiplied by 23?\"}\n    ],\n    \"temperature\": 0.0,\n    \"max_tokens\": 512,\n    \"skip_special_tokens\": false,\n    \"chat_template_kwargs\": {\n      \"enable_thinking\": true\n    }\n  }'\n

For more details on how to deploy and use the model — see the Quick Start Guide below!

License/Terms of Use

GOVERNING DOWNLOAD TERMS: Use of the model is governed by the OpenMDW License Agreement, version 1.1.

ADDITIONAL INFORMATION: Apache License, Version 2.0.

Benchmarks

Reasoning Benchmark Evaluations

We evaluated our model on the following benchmarks:

Accuracy numbers are measured by NVIDIA under a consistent harness (NeMo Gym / Nemo Evaluator SDK); they may differ from vendors' self-reported numbers.

TODO: Placeholder (Accuracy table)

Deployment Geography: Global

Use Case

NVIDIA-Nemotron-3.5-Bolt-E4B-4GB-BF16 is the full-precision release of a general purpose reasoning and chat model, and is intended primarily for customization and post-training rather than direct production inference. It is intended to be used by developers who want to: post-train the model on their own data (SFT, RL via NeMo RL and NeMo Gym, or distillation), adapt it to a domain or task, produce quantized variants (NVFP4, W4A16, GGUF) for their own deployment targets, or run full-precision research and evaluation. English and coding languages are the primary languages, with Spanish, French, German, Italian, and Japanese also supported.

Release Date

Hugging Face 10/23/2026 via https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB-BF16

Model Architecture

Architecture Type: Gemma 4
Network Architecture: Gemma-4-E4B
Number of model parameters: 8B (4B effective parameters)

Model Design

The model was further post-trained from Gemma-4-E4B-it.

Training Methodology

Reinforcement Learning

The model underwent multi-environment reinforcement learning using GRPO (Group Relative Policy Optimization) across instruction following, multi-step tool use, multi-turn conversations, and structured output environments. It utilized an asynchronous RL architecture that decouples training from inference.
Software used for reinforcement learning: NeMo RL, NeMo Gym

NVIDIA-Nemotron-3.5-Bolt-E4B-4GB-BF16 is a result of the above work.

Input

Input Type(s): Text
Input Format(s): String
Input Parameters: One-Dimensional (1D): Sequences
Other Properties Related to Input: Maximum context length up to 1M tokens. Supported languages include English, Spanish, French, German, Italian, and Japanese.

Output

Output Type(s): Text
Output Format: String
Output Parameters: One-Dimensional (1D): Sequences
Other Properties Related to Output: Maximum context length up to 128K tokens

Our AI models are designed and/or 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): PyTorch
Supported Hardware Microarchitecture Compatibility: NVIDIA Ampere - A100; NVIDIA Blackwell; NVIDIA Hopper - H100-80GB
Supported 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.

Model Version(s)

GA (10/23/2026)

Quick Start Guide

All deployment snippets below assume:

export MODEL_CKPT=nvidia/Gemma-Nemotron-4B-BF16\n

vLLM

vLLM: vllm/vllm-openai:v0.31.0

1x H100 / A100 / GB200

For max throughput deployments, use the following configuration, no speculative decoding strategy is best for this serving configuration, and due to memory constraints the Mamba cache dtype is set as FP16:

vllm serve \"$MODEL\" \\\n  --served-model-name \"nemotron-bolt\" \\\n  --host 0.0.0.0 \\\n  --port 8000 \\\n  --dtype bfloat16 \\\n  --tensor-parallel-size 1 \\\n  --gpu-memory-utilization 0.85 \\\n  --kv-cache-dtype auto \\\n  --max-model-len 65536 \\\n  --max-num-seqs 64 \\\n  --max-num-batched-tokens 65536 \\\n  --chat-template \"$MODEL/chat_template.jinja\" \\\n  --enable-auto-tool-choice \\\n  --tool-call-parser gemma4 \\\n  --reasoning-parser gemma4 \\\n  --limit-mm-per-prompt '{\"image\":0,\"video\":0,\"audio\":0}'\n

API Client

The examples below use the OpenAI-compatible client and work with the serving backend above. Recommended sampling settings are Temperature 1.0 and Top_P 0.95 in both thinking enabled and disabled modes.

from openai import OpenAI\nclient = OpenAI(base_url=\"http://localhost:8000/v1\", api_key=\"EMPTY\")\nMODEL = \"nemotron-bolt\"\n

Gemma-Nemotron exposes reasoning control through chat-template kwargs: thinking enabled (the default), thinking disabled for direct answers, and a runtime thinking budget.

Reasoning On

response = client.chat.completions.create(\n    model=MODEL,\n    messages=[{\"role\": \"user\", \"content\": \"Write a haiku about GPUs\"}],\n    max_tokens=16000,\n    temperature=1.0,\n    top_p=0.95,\n    extra_body={\n        \"skip_special_tokens\": False,\n        \"chat_template_kwargs\": {\"enable_thinking\": True},\n    }\n)\nprint(response.choices[0].message.content)\n

Reasoning Off

response = client.chat.completions.create(\n    model=MODEL,\n    messages=[{\"role\": \"user\", \"content\": \"What is the capital of Japan?\"}],\n    max_tokens=16000,\n    temperature=1.0,\n    top_p=0.95,\n    extra_body={\n        \"skip_special_tokens\": False,\n        \"chat_template_kwargs\": {\"enable_thinking\": False},\n    }\n)\nprint(response.choices[0].message.content)\n

Streaming

stream = client.chat.completions.create(\n    model=MODEL,\n    messages=[{\"role\": \"user\", \"content\": \"Explain speculative decoding in two sentences\"}],\n    max_tokens=16000,\n    temperature=1.0,\n    top_p=0.95,\n    stream=True,\n    extra_body={\"skip_special_tokens\": False},\n)\nfor chunk in stream:\n    print(chunk.choices[0].delta.content or \"\", end=\"\", flush=True)\n

Tool Calling

For vLLM, add the following to any serve command above:

--enable-auto-tool-choice \\\n--tool-call-parser gemma4 \\\n--reasoning-parser gemma4 \\\n

Sample request with tool-calling enabled:

tools = [{\n    \"type\": \"function\",\n    \"function\": {\n        \"name\": \"get_weather\",\n        \"description\": \"Get the current weather for a city\",\n        \"parameters\": {\n            \"type\": \"object\",\n            \"properties\": {\"city\": {\"type\": \"string\"}},\n            \"required\": [\"city\"],\n        },\n    },\n}]\n\nresponse = client.chat.completions.create(\n    model=MODEL,\n    messages=[{\"role\": \"user\", \"content\": \"What's the weather in Santa Clara?\"}],\n    tools=tools,\n    max_tokens=16000,\n    temperature=1.0,\n    top_p=0.95,\n    extra_body={\n        \"skip_special_tokens\": False,\n        \"chat_template_kwargs\": {\"enable_thinking\": True},\n    },\n)\nprint(response.choices[0].message.tool_calls)\n

Conversational Mode

Gemma-Nemotron supports a conversational mode. If enabled, Gemma-Nemotron responds concisely and without as much textual formatting. This mode is suitable for conversational and spoken interaction-based applications. Conversational mode can be toggled as follows:

response = client.chat.completions.create(\n    model=MODEL,\n    messages=[{\"role\": \"user\", \"content\": \"What is the capital of Japan?\"}],\n    max_tokens=16000,\n    temperature=1.0,\n    top_p=0.95,\n    extra_body={\n        \"skip_special_tokens\": False,\n        \"chat_template_kwargs\": {\"conversational_mode\": True},\n    }\n)\nprint(response.choices[0].message.content)\n

Conversational mode can be combined with thinking enabled and disabled mode, as well as tool-calling.

Runtime Thinking Budget Control

Client implementation and usage

In thinking enabled mode, you can control the thinking budget that Gemma-Nemotron uses with a two-step inference client. The client stops Gemma-Nemotron’s reasoning process at a defined token budget and gets a final response from the model. The budget control client can be implemented as following:

import copy\nimport requests\n\nclass GemmaNemotronThinkingBudgetClient:\n    def __init__(self, base_url):\n        self.base_url = base_url.rstrip(\"/\")\n\n    def _post(self, payload):\n        response = requests.post(\n            f\"{self.base_url}/chat/completions\",\n            json=payload,\n            timeout=300,\n        )\n        response.raise_for_status()\n        return response.json()\n\n    def chat_completion(\n        self,\n        model,\n        messages,\n        max_thinking_budget=512,\n        max_tokens=1024,\n        tools=None,\n        temperature=1.0,\n        top_p=0.95,\n        seed=None,\n    ):\n        if max_tokens <= max_thinking_budget:\n            raise ValueError(\n                \"max_tokens must be larger than max_thinking_budget\"\n            )\n\n        original_messages = copy.deepcopy(messages)\n        sampling = {\n            \"temperature\": temperature,\n            \"top_p\": top_p,\n        }\n        if seed is not None:\n            sampling[\"seed\"] = seed\n\n        first_payload = {\n            \"model\": model,\n            \"skip_special_tokens\": False,\n            \"messages\": original_messages,\n            \"max_tokens\": max_thinking_budget,\n            \"chat_template_kwargs\": {\"enable_thinking\": True},\n            **sampling,\n        }\n        if tools is not None:\n            first_payload[\"tools\"] = tools\n            first_payload[\"tool_choice\"] = \"auto\"\n\n        first = self._post(first_payload)\n        choice = first[\"choices\"][0]\n        message = choice[\"message\"]\n        reasoning = (\n            message.get(\"reasoning_content\")\n            or message.get(\"reasoning\")\n            or \"\"\n        )\n        partial_answer = message.get(\"content\") or \"\"\n        tool_calls = message.get(\"tool_calls\") or []\n\n        if choice.get(\"finish_reason\") == \"stop\" and (\n            partial_answer or tool_calls\n        ):\n            return {\n                \"reasoning_content\": reasoning,\n                \"content\": partial_answer,\n                \"tool_calls\": tool_calls,\n                \"finish_reason\": choice.get(\"finish_reason\"),\n                \"budget_forced\": False,\n            }\n\n        used_tokens = first.get(\"usage\", {}).get(\n            \"completion_tokens\",\n            max_thinking_budget,\n        )\n        remaining_tokens = max_tokens - used_tokens\n        if remaining_tokens <= 0:\n            raise ValueError(\"Increase max_tokens or lower the budget\")\n\n        continuation_messages = copy.deepcopy(original_messages)\n        continuation_messages.append(\n            {\n                \"role\": \"assistant\",\n                \"reasoning_content\": reasoning,\n                \"content\": partial_answer,\n            }\n        )\n\n        second_payload = {\n            \"model\": model,\n            \"skip_special_tokens\": False,\n            \"messages\": continuation_messages,\n            \"max_tokens\": remaining_tokens,\n            \"add_generation_prompt\": False,\n            \"continue_final_message\": True,\n            \"chat_template_kwargs\": {\n                \"enable_thinking\": True,\n                \"preserve_thinking\": True,\n            },\n            **sampling,\n        }\n        if tools is not None:\n            second_payload[\"tools\"] = tools\n            second_payload[\"tool_choice\"] = \"auto\"\n\n        second = self._post(second_payload)\n        second_choice = second[\"choices\"][0]\n        second_message = second_choice[\"message\"]\n        return {\n            \"reasoning_content\": reasoning,\n            \"content\": partial_answer + (\n                second_message.get(\"content\") or \"\"\n            ),\n            \"tool_calls\": second_message.get(\"tool_calls\") or [],\n            \"finish_reason\": second_choice.get(\"finish_reason\"),\n            \"budget_forced\": True,\n        }\n

You can then use the client as:

client = GemmaNemotronThinkingBudgetClient(\n    base_url=\"http://localhost:8000/v1\", api_key=\"EMPTY\"\n)\nMODEL = \"nemotron-bolt\"\n\nresult = client.chat_completion(\n    model=MODEL,\n    messages=[\n        {\"role\": \"system\", \"content\": \"\"},\n        {\"role\": \"user\", \"content\": \"What is 2+2?\"},\n    ],\n    max_thinking_budget=32,\n    max_tokens=512,\n    temperature=1.0,\n    top_p=0.95,\n    seed=17,\n)\n\nprint(result[\"content\"])\n# Tool-Calling Example\n\ncalculate_tip = {\n    \"type\": \"function\",\n    \"function\": {\n        \"name\": \"calculate_tip\",\n        \"description\": \"Calculate the tip for a bill.\",\n        \"parameters\": {\n            \"type\": \"object\",\n            \"properties\": {\n                \"bill_total\": {\"type\": \"number\"},\n                \"tip_percentage\": {\"type\": \"number\"},\n            },\n            \"required\": [\"bill_total\", \"tip_percentage\"],\n            \"additionalProperties\": False,\n        },\n    },\n}\n\ntool_result = client.chat_completion(\n    model=MODEL,\n    messages=[\n        {\"role\": \"system\", \"content\": \"\"},\n        {\n            \"role\": \"user\",\n            \"content\": (\n                \"My bill is $100. Use calculate_tip to calculate an 18% tip.\"\n            ),\n        },\n    ],\n    tools=[calculate_tip],\n    max_thinking_budget=32,\n    max_tokens=512,\n    temperature=1.0,\n    top_p=0.95,\n    seed=23,\n)\n\nif not tool_result[\"tool_calls\"]:\n    raise RuntimeError(\"The model did not return a parsed tool call.\")\n\ntool_call = tool_result[\"tool_calls\"][0]\nprint(tool_call[\"function\"][\"name\"])\nprint(tool_call[\"function\"][\"arguments\"])\n\n# Example output:\n# calculate_tip\n# {\"bill_total\":100,\"tip_percentage\":18}\n

How Runtime Budget Control Works

Phase 1 executes an initial request to /v1/chat/completions, configuring enable_thinking=True within chat_template_kwargs alongside the designated token limit via max_thinking_budget.
Should execution terminate standardly and return complete output, the system yields this output without triggering secondary inference.
If execution halts upon reaching the designated ceiling, extract reasoning_content directly from the message object.
Construct and append an assistant role turn incorporating the extracted thought traces along with any generated output snippets.
Re-pass the function schemas while assigning preserve_thinking=True inside the template parameters.
Assign the secondary max_tokens allocation to match the remaining generation threshold.
Phase 2 dispatches to /v1/chat/completions using continue_final_message=True and add_generation_prompt=False, preserving active Gemma 4 tool and reasoning parsing modules.

Limits and Recommendations for Runtime Budget Control

Restricted token allocations may truncate thought processes mid-sentence. While output clarity is typically maintained, task-specific output evaluation remains necessary.
The client halts execution immediately upon reaching the pre-configured initial generation ceiling, ignoring subsequent line boundaries.
Conduct systematic allocation sweeps using standard evaluation prompts prior to establishing deployment parameters.
Illustration: To maintain a strict 500-token overall threshold, evaluate unconstrained model generation metrics (e.g., averaging 200 tokens). Assigning a dedicated 300-token reasoning parameter alongside a 500-token global ceiling forces thought termination at 300 tokens, reserving 200 tokens for output synthesis.
To encourage brief output (excluding internal reasoning steps), incorporate explicit word- or sentence-count constraints within system prompts.

Asynchronous Tool Calling

Transmit asynchronous functions via the standard OpenAI tools structure.
Retrieve assistant function executions directly from message.tool_calls.
Restrict generation to a single tool invocation per assistant turn.
Assign a persistent task identifier to each decoupled job execution.
Provide subsequent execution outputs as a user role turn prefixed with something like \"TOOL RESULT:\".
Avoid submitting secondary tool turns for requests with an active pending response.
Maintain tracked status sets for unresolved and finalized job identifiers.
Synthesize the final response only after all background processes finish and zero calls remain outstanding.

Example:

Assistant tool-call

{\n  \"role\": \"assistant\",\n  \"tool_calls\": [{\n    \"id\": \"call_1\",\n    \"type\": \"function\",\n    \"function\": {\n      \"name\": \"build_report\",\n      \"arguments\": \"{\\\"_task_id\\\":\\\"task_1\\\",\\\"scope\\\":\\\"weekly\\\"}\"\n    }\n  }]\n}\n

Immediate acknowledgement with pending result

{\n  \"role\": \"tool\",\n  \"tool_call_id\": \"call_1\",\n  \"content\": \"{\\\"status\\\":\\\"pending\\\",\\\"task_id\\\":\\\"task_1\\\",\\\"function\\\":\\\"build_report\\\"}\"\n}\n

Later tool result completion result

{\n  \"role\": \"user\",\n  \"content\": \"ASYNC TOOL UPDATE: {\\\"status\\\":\\\"completed\\\",\\\"task_id\\\":\\\"task_1\\\",\\\"function\\\":\\\"build_report\\\",\\\"response\\\":\\\"Report is ready.\\\"}\"\n}\n

The model processes the finalized payload to resume generation.

Prompting and Usage Guidelines

General guidelines for using Gemma-Nemotron in conversational and agentic applications.

Prompt Structure and Content

Store persistent behavior constraints and operational guidelines in system prompts.
Use explicit headers to organize roles, tasks, tool configs, and formatting safety rules.
Keep the primary objective clear and concise.
Write unambiguous, verifiable instructions and resolve conflicting guidance.
Remove redundant guidance and repetitive few-shot examples.
Use explicit constraints (e.g., \"limit responses to 1-2 sentences\") instead of vague requests (e.g., \"be brief\").
Avoid synthetic or non-model outputs in \"assistant\" turns.
Adhere strictly to standard system/user/assistant/tool turn sequences.

Conveying Environment State in Stateful Applications

Prefix context blocks with explicit structural markers (e.g., \"# CURRENT STATE\").
Clearly specify what part of the context belongs to the model and what part refers to information for other entities like the user.
When state updates end a message, restate the core goal alongside them.

Instructing the Model

Define required behaviors with precise language and mandate necessary tool calls.
Provide fallback protocols for requests exceeding available tool capabilities.
Separate output formatting rules from task directives, and use the official chat template.

Conversational Responses

Set conversational_mode=True for concise, low-formatting outputs suited for audio or spoken interaction.
Set or leave the flag to False to retain standard generation style.

Training, Testing, and Evaluation Datasets

Training

Dataset Overview

Data Modality: Text

Training Data Size: More than 20 Trillion Tokens

Dataset partition: Training [100%], testing [0%], validation [0%]

Time period for training data collection: 2013 to December 2025

Time period for testing data collection: 2013 to December 2025

Time period for validation data collection: 2013 to December 2025

Data Collection Method by dataset: Hybrid: Automated, Manually-Collected, Synthetic

Labeling Method by dataset: Hybrid: Automated, Manually-Labeled, Synthetic

The post-training corpus for NVIDIA-Nemotron-3.5-Bolt-E4B-4GB-BF16 consists of high-quality curated and synthetically-generated data. Primary languages used for post-training include English, French, German, Italian, Japanese, Spanish, and Chinese.

These datasets, such as FinePDFs, EssentialWeb, HotpotQA, SQuAD, and HelpSteer3, do not collectively or exhaustively represent all demographic groups (and proportionally therein). For instance, these datasets do not contain explicit mentions of demographic classes such as age, gender, or ethnicity in 64-99% of samples, depending on the source. In the subset where such terms are present, document-based datasets (FinePDFs and EssentialWeb) contain representational skews, such as references to \"male\" outnumbering those to \"female\", and mentions of \"White\" as the most frequent among ethnic identifiers (comprising 43-44% of ethnicity mentions). To mitigate these imbalances, we recommend considering evaluation techniques such as bias audits, fine-tuning with demographically balanced datasets, and mitigation strategies like counterfactual data augmentation to align with the desired model behavior. This evaluation used a 3,000-sample subset per dataset, identified as the optimal threshold for maximizing embedder accuracy.

During post-training, we generate synthetic data by distilling trajectories, solutions, and translations from strong teacher models and agent systems, often grounded in real tasks or documents and aggressively filtered for quality. For math, code, and science, we start from curated problem sets and use open source permissive models such as GPT-OSS-120B to produce step-by-step reasoning traces, candidate solutions, best-of-n selection traces, and verified CUDA kernels. For long-context and science, we build synthetic QA and reasoning data by retrieving passages from long documents, generating MCQ/OpenQA questions and answers, and paraphrasing them into multiple prompt/response formats to ensure diversity. Across all pipelines we stack automated verification—compilers, numerical checks, language identification—to ensure our data is high quality.

For all domains, we apply a unified data filtering pipeline to ensure that only high-quality, license-compliant, and verifiable samples are used for post-training. We first discard malformed examples using structural checks (e.g., missing tool definitions when tool calls are present). We then aggressively filter reasoning traces exhibiting pathological repetition, such as repeated n-grams within a sliding window or across the entire trajectory, which we found to be a strong indicator of malformed or low-quality reasoning. Finally, based on internal audits of synthetically generated datasets, we observed that some teacher models occasionally produce reasoning traces and final responses that implicitly align with specific political entities or promote nationalistic narratives. To mitigate this, we apply targeted keyword- and regex-based filters and remove all trajectories matching such behavior.

Alongside the model, we release our final pre-training and post-training 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.

Detailed dataset information

Public Datasets

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Dataset	Collection Period
GSM8K	4/23/2025
CC-NEWS	4/23/2025
Common Crawl	4/23/2025
Wikimedia	4/23/2025
Bespoke-Stratos-17k	4/23/2025
tigerbot-kaggle-leetcodesolutions-en-2k	4/23/2025
glaive-function-calling-v2	4/23/2025
APIGen Function-Calling	4/23/2025
LMSYS-Chat-1M	4/23/2025
Open Textbook Library - CC BY-SA & GNU subset and OpenStax - CC BY-SA subset	4/23/2025
Advanced Reasoning Benchmark, tigerbot-kaggle-leetcodesolutions-en-2k, PRM800K, and SciBench	4/23/2025
FineWeb-2	4/23/2025
Court Listener	Legacy Download
peS2o	Legacy Download
OpenWebMath	Legacy Download
BioRxiv	Legacy Download
PMC Open Access Subset	Legacy Download
OpenWebText2	Legacy Download
Stack Exchange Data Dump	Legacy Download
PubMed Abstracts	Legacy Download
NIH ExPorter	Legacy Download
arXiv	Legacy Download
BigScience Workshop Datasets	Legacy Download
Reddit Dataset	Legacy Download
SEC's Electronic Data Gathering, Analysis, and Retrieval (EDGAR)	Legacy Download
Advanced Mathematical Problem Solving	Legacy Download
MathPile	Legacy Download
NuminaMath CoT	Legacy Download
PMC Article	Legacy Download
FLAN	Legacy Download
Advanced Reasoning Benchmark	Legacy Download
SciBench	Legacy Download
WikiTableQuestions	Legacy Download
FinQA	Legacy Download
Riddles	Legacy Download
Problems in Elementary Mathematics for Home Study	Legacy Download
MedMCQA	Legacy Download
Cosmos QA	Legacy Download
MCTest	Legacy Download
AI2's Reasoning Challenge	Legacy Download
OpenBookQA	Legacy Download
MMLU Auxiliary Train	Legacy Download
social-chemestry-101	Legacy Download
Moral Stories	Legacy Download
The Common Pile v0.1	Legacy Download
FineMath	Legacy Download
MegaMath	Legacy Download
MultiverseMathHard	10/2/2025
SWE-Gym	10/2/2025
WorkBench	10/2/2025
WildChat-1M	10/2/2025
OpenCodeReasoning-2	10/2/2025
HelpSteer3	10/2/2025
opc-sft-stage2	10/2/2025
Big-Math-RL-Verified	10/2/2025
MetaMathQA	10/2/2025
simple-arithmetic-problems	10/2/2025
arithmetic	10/2/2025
Skywork-OR1-RL-Data	10/2/2025
FastChat	10/2/2025
News Commentary	10/2/2025
Essential-Web	10/2/2025
finepdfs	10/2/2025
HotpotQA	10/2/2025
SQuAD2.0	10/2/2025
NLTK Words Lists	10/2/2025

Private Non-publicly Accessible Datasets of Third Parties

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Dataset	Model(s) used
Global Regulation	Unknown
TAUS Translation Memory	Unknown
Scale HLE	Unknown
HackerRank Coding	Unknown
RL data for Search	Gemini 3; GPT-5
Mercor SWE-AgentsV1	Undisclosed

Private Non-publicly Accessible Datasets by NVIDIA

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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Dataset	Model(s) used
Simple Minesweeper	Undisclosed
Simple Sudoku	Undisclosed
Multitool Typewriter Hard	Undisclosed
Machine Translation of News Commentary and TAUS Translation Memory	Undisclosed
Machine Translation of STEM -	Qwen2.5-14B-Instruct
Competitive Coding RL data from Nemotron Cascade	Undisclosed
Long context RL	Undisclosed
Single-step SWE RL for patch generation	Undisclosed
OpenHands SWE	Undisclosed

NVIDIA-Sourced Synthetic Datasets (Post-Training)

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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Dataset	Modality	Dataset Size	Seed Dataset	Model(s) used for generation
Synthetic Competitive MATH Proofs from DeepSeek-V4-Pro	Text	Undisclosed	[AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]	[deepseek-ai/DeepSeek-V4-Pro]
Synthetic Hermes Agent Reasoning Traces	Text	Undisclosed	[lambda/hermes-agent-reasoning-traces]	[hermes-agent-generator]
Synthetic Competitive Coding from DeepSeek-V4-Pro	Text	Undisclosed	[NVCompetitiveCodingV1]	[deepseek-ai/DeepSeek-V4-Pro]
Synthetic Competitive Science Reasoning from DeepSeek-V4-Pro	Text	Undisclosed	[AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [EssentialAI/essential-web-v1.0]; [cdquestions.com]; [Pile-FreeLaw]; [Vedantu]; [askfilo]; [doubtnut]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]; [AAPT]; [ChemData 700K]; [oMeBench]; [Flavor Analysis and Recognition Transformer]; [ChemCoTBench]; [Llama Nemotron Dataset]	[deepseek-ai/DeepSeek-V4-Pro]
Synthetic Competitive MATH CoT and TIR from Nemotron 5.5	Text	Undisclosed	[Pile-FreeLaw]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]	[Nemotron 5.5]
Vendor Terminal Bench-like Tasks from Mercor	Text	Undisclosed	[Terminal bench like tasks curated by the vendor]	[Undisclosed - purchased dataset]
Turing Math Data Pack	Text	Undisclosed	[Turing Math Data Pack dataset]	[Undisclosed - purchased dataset]
Synthetic Holdout, Skywork, DAPO, and Turing Math from GPT-5.5	Text	Undisclosed	[DocQA-RL-1.6K]; [DAPO-Math-17k]	[GPT-5.5]
Synthetic Long Context RL from QwenLong L1 and DocQA-RL-1.6K	Text	Undisclosed	[DocQA-RL-1.6K]	Undisclosed
Synthetic Competitive Coding Gym Tasks	Text	Undisclosed	[NVCompetitiveCodingV1.1]	Undisclosed
Synthetic Finance SEC Search Agent from GPT-OSS-120B and Qwen3	Text	Undisclosed	[SEC filings from sec.gov]	[GPT-OSS-120B]; [Qwen3-235B-A22B-Instruct]; [Qwen3-4B-Instruct]
Synthetic Structured Outputs from Qwen3-30B-A3B-Instruct-2507, Qwen3-30B-A3B-Thinking-2507, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	[Nemotron-RL-agent-structured-outputs-v1]	[Qwen3-30B-A3B-Instruct-2507]; [Qwen3-235B-A22B-Instruct-2507]
Synthetic Long Context Equivalence Rule from Qwen3-235B-A22B-Thinking-2507 and DeepSeek-R1	Text	Undisclosed	[Long-context SFT data]	[Qwen/Qwen3-235B-A22B-Thinking-2507]; [Deepseek-ai/DeepSeek-R1]
Synthetic Science RL Data Blend from Qwen2.5-32B	Text	Undisclosed	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[Qwen2.5-32B]
Synthetic Abstention Data from Nemotron Super v3	Text	Undisclosed	[Go abstention Dataset]	[nvidia/nvidia/nemotron-3-super-v3]
Synthetic Chemistry Data from Nemotron Super v3	Text	Undisclosed	[ChemData 700K]	[nvidia/nvidia/nemotron-3-super-v3]
Synthetic Tool Call Schema for RL	Text	469,983	[UltraTool]; [ToolEyes]; [AutoTools]; [API-Bank]; [Nemotron-Personas-USA]; [Salesforce xLAM function-calling]; [Glaive function-calling-v2]; [Agent-Ark/Toucan-1.5M]	[DeepSeek-V3.2]; [GLM-4.6]; [gpt-oss-120b]; [Kimi-K2-Instruct]
Synthetic Freeform Text Formatting from GPT-OSS-120B	Text	Undisclosed	[In-house data]	[GPT OSS 120B - Apache 2.0]
Synthetic Citation Formatting from GPT-OSS-120B	Text	Undisclosed	[In-house data]	[GPT OSS 120B - Apache 2.0]
Droid Harness Pivot Vendor Data	Text	Undisclosed	[Droid Harness Pivot vendor data]	Undisclosed
Synthetic HotpotQA Training Data from Qwen3-235B	Text	Undisclosed	[HotpotQA]	[Qwen3-235B]
Synthetic Natural Language Math Proofs from Nemotron 5.5	Text	Undisclosed	[AMC8, AMC10, and AIME problem sets hosted on Art of Problem Solving]; [Pile-StackExchange]	[Nemotron 5.5]
Synthetic Stack Overflow OpenQ	Text	Undisclosed	[Pile-FreeLaw]	Undisclosed
Chemistry Ether0 Vendor Data	Text	Undisclosed	[Chemistry ether0 vendor data]	Undisclosed
Synthetic Litmus-Bench Chemistry from ChEMBL	Text	Undisclosed	[ChEMBL]; [Nemo Gym RL dataset generated from ChEMBL with RDKit]	Undisclosed
Synthetic ZINC Chemistry from Nemotron Super v3	Text	Undisclosed	[ZINC]	[Nemotron Super v3]
ARC-AGI Gym Environment	Text	Undisclosed	[ARC-AGI-2]	[ARC-AGI-2]
Synthetic Agentic Search Tool-Use from DeepSeek-V3.2	Text	Undisclosed	[Mercor Data]	[DeepSeek-V3.2]
Synthetic Text-To-SQL	Text	96,564	[In-house Text-to-SQL data]	[gpt-oss-120b]
Dialog Memory Vendor Data	Text	Undisclosed	[Patronus external vendor agreement]	Undisclosed
Synthetic Indirect Prompt Injection from Nemotron Super v3 and Qwen3-Next-80B-A3B-Instruct	Text	Undisclosed	[In-house indirect prompt injection data]	[nvidia/nemotron-3-super-v3, qwen/qwen3-next-80b-a3b-instruct]
Synthetic Malicious Code and Agentic Security	Text	Undisclosed	[In-house malicious-code / agentic-security data]	Undisclosed
Synthetic Single-Step SWE Patch Selection	Text	Undisclosed	[SWE-Gym Dataset]; [SWE Bench Verified Benchmark]	[ground truth and task checks]
Synthetic Natural Language Math Final Answers from Nemotron 5.5	Text	Undisclosed	[AMC8, AMC10, and AIME problem sets hosted on Art of Problem Solving]; [Pile-StackExchange]	[nemotron 5.5]
Synthetic Simple Math Prompts for Token Efficiency	Text	Undisclosed	[In-house simple math prompts]	Undisclosed
Synthetic Abstention Data from Nemotron Super v3 (CRAG)	Text	Undisclosed	[CRAG]	[nvidia/nvidia/nemotron-3-super-v3]
Synthetic Agentless SWE	Text	242,536	[SWE-Rebench-V2]; [SWEbench Training Set]; [R2E-Gym/R2E-Gym-Subset]; [SWE-Gym/SWE-Gym]; [SWE-Rebench]	[openai/gpt-oss-120b]
Synthetic Agentless SWE from DeepSeek-R1-0528	Text	209,976	[SWE-Bench-Train]; [SWE-Fixer-Train]; [SWE-reBench]; [SWE-Smith]	[deepseek-ai/DeepSeek-R1-0528]
Synthetic Agentic CUDA Traces from GLM-4.7	Text	2,276	[Internal CUDA task data]	[GLM-4.7]
Synthetic Math Proofs from DeepSeek-V3.2-Speciale	Text	820,772	[Nemotron-Math-Proofs-v1]	[SDG: DeepSeek-V3.2-Speciale]; [Filter: proof validation]
Synthetic Multilingual SFT from DeepSeek-V3	Text	1,245,284	[Nano v3 SFT data]	[DeepSeek-V3]
Synthetic Agentic Code from gpt-oss-120b	Text	109,086	[NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]	[openai/gpt-oss-120b]
Synthetic Agentic CLI and Web Skills from gpt-oss-120b	Text	27,418	[NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1]	[openai/gpt-oss-120b]
Synthetic Agentic Coding from gpt-oss-120b	Text	160,531	[NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1]	[openai/gpt-oss-120b]
Synthetic OpenCode Agentic Tasks from gpt-oss-120b	Text	614,773	[NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1]	[openai/gpt-oss-120b]
Synthetic SWE Unverified	Text	Undisclosed	[NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1]	[gpt-oss-120b]; [Qwen/Qwen3-Coder-480B-A35B-Instruct]; [GLM-4.7-Flash]
Synthetic ARC-AGI Ultra Data	Text	192,016	[ARC-AGI-2]; [arc dataset collection]	[ARC-AGI-2]
Synthetic LiveCodeBench TIR from DeepSeek-R1-0528	Text	1,283,398	[Nemotron-X training datasets]	[DeepSeek-R1-0528]
Synthetic Verilog and SystemVerilog Code from DeepSeek-R1-0528 and GPT-OSS-120B	Text	1,233,247	[Verilog/SystemVerilog seed code]	[SDR: DeepSeek R1 0528 and GPT-OSS-120B]; [Filtering: Claude 4 Sonnet]
Synthetic Aider Python Tasks from DeepSeek-R1-0528	Text	236,099	[Exercism (GitHub Python)]	[Deepseek R1 0528]
Synthetic Chat Reasoning-Off Data from GLM-5	Text	646,738	[lmarena-ai/repochat-arena-preference-4k user prompts]	[Multi-turn conversations generated by GLM-5 with best-of-4 selection via Qwen3-Nemotron-235B-A22B-GenRM]
Synthetic Chat Reasoning-On Data from GLM-5	Text	644,286	[lmarena-ai/repochat-arena-preference-4k user prompts]; [lmarena-ai/arena-expert-5k user prompts]; [lmarena-ai/arena-human-preference-55k user prompts]; [lmarena-ai/arena-human-preference-100k user prompts]; [lmarena-ai/arena-human-preference-140k user prompts]	[Multi-turn conversations generated by GLM-5 with best-of-4 selection via Qwen3-Nemotron-235B-A22B-GenRM]
Synthetic Multilingual Safety from Riva-Translate-4B-Instruct-v1.1	Text	132,067	[Safety SFT Data: Ultra]	[nvidia/Riva-Translate-4B-Instruct-v1.1]
Synthetic Science Reasoning Effort Medium	Text	502,722	[science-reasoning-effort-medium-v0]	Undisclosed
Synthetic Telecom Tool-Use Trajectories from gpt-oss-120b	Text	12,455	[Existing Tau2 telecom trajectories originally generated with DeepSeek V3.2]	[gpt-oss-120b]
Synthetic Terminal Bench Data from OpenReasoningv2	Text	Undisclosed	[OpenCodeReasoningv2]; [OpenMathReasoning]; [nemo-swe-bench-repos]; [SWE-Rebench]; [SWE-Fixer-110K]	[OpenReasoningv2]
Synthetic Tulu Instruction Following from DeepSeek-R1-0528	Text	105,361	[Nemotron-X training datasets]	[DeepSeek-R1-0528]
Synthetic Instruction Following from gpt-oss-120b	Text	151,988	[IFEval]; [IFEvalG]	[gpt-oss-120b]
Synthetic Instruction Following for RL	Text	Undisclosed	[WildChat-1M]; [LMSYS-340B-Eval Dataset]; [LMSYS-Chat-1M Prompts]; [IFEval]; [IFEvalG]	[Qwen/Qwen3-235B-A22B-Thinking-2507]; [gpt-oss-120b]; [Qwen3-235B-A22B-Instruct-2507]
Synthetic Identity Data from Qwen3-Next-80B-A3B-Instruct and Qwen3-235B-A22B-Instruct-2507	Text	25,992	[Hand-written prompts]	[Qwen3-Next-80B-A3B-Instruct]; [Qwen3-235B-A22B-Instruct-2507]
Synthetic Terminus Ultra Agentic Reasoning Blend	Text	96,881	[ARC-AGI-2]; [OpenCodeReasoningv2]; [OpenMathReasoning]; [SWE-Fixer-110K]; [SWE-Rebench]; [SWE-Smith]	[DeepSeek-V3.2]; [Qwen3-235B-A22B-Thinking-2507]; [Ring-1T]; [Kimi-K2.5]; [GLM-4.7-FP8]; [Qwen3-Next-80B-A3B-Thinking]; [gpt-oss-120b]; [Ministral-3-14B-Reasoning-2512]; [LM-4.5-Air-FP8]
Synthetic STEM from Qwen3-235B-A22B-Thinking-2507	Text	1,174,694	[IChO-IPhO-RL-v2]; [Physics-Big Dataset]; Scale HLE; [OpenMathReasoning]; [OpenCodeReasoning]	[Qwen3-235B-A22B-Thinking-2507]
Synthetic STEM from Qwen3-235B-A22B-Instruct-2507 and gpt-oss-120b	Text	Undisclosed	[arXiv]; [National Institutes of Health ExPorter]; [BioRxiv]; [PMC Article]; [USPTO Backgrounds]; [peS2o]; Global Regulation; [CORE]; [PG-19]; [DOAB CC BY & CC BY-SA subset]; [NDLTD]	[Qwen3-235B-A22B-Instruct-2507]; [gpt-oss-120b]
Translation Data from TAUS	Text	1,618,055	[TAUS proprietary dataset]	Undisclosed
Synthetic Art of Problem Solving and Stack Exchange from gpt-oss-120b, Qwen2.5-32B-Instruct, and Goedel-Prover-V2-32B	Text	860,469	[Nemotron-Math-Proofs-v1]	[Goedel-Prover-V2-32B]
Synthetic Art of Problem Solving and Stack Exchange from gpt-oss-120b	Text	1,201,815	[Upstream released math dataset]; [AoPS]; [StackOverflow / StackExchange]	[gpt-oss-120b]
Synthetic Multilingual Science and Code data from DeepSeek-R1, DeepSeek-R1-0528, Qwen2.5-32B-Instruct, and Qwen3-235B-A22B, translated with Qwen2.5-32B-Instruct and Qwen2.5-14B-Instruct	Text	Undisclosed	[Nano-V3 SFT Data (without tool call)]	[Qwen/Qwen2.5-14B-Instruct]; [Qwen/Qwen3-4B-Thinking-2507]
Synthetic Multilingual Science and Code data from DeepSeek-R1, DeepSeek-R1-0528, Qwen2.5-32B-Instruct, and Qwen3-235B-A22B, translated with Qwen2.5-32B-Instruct and Qwen2.5-14B-Instruct (Stack Exchange lineage)	Text	Undisclosed	[Stack Exchange]; [SCP-116K]; [LIMO]; [TACO]; Code Contest; Codeforces	[DeepSeek-R1]; [DeepSeek-R1-0528]; [Qwen2.5-32B-Instruct]; [Qwen3-235B-A22B]
Synthetic Search Graph Walk	Text	6,977	[Wikidata / Wikipedia KnowledgeBase]	[MiniMaxAI/MiniMax-M2]
Synthetic Agentic Diverse Domains	Text	281,537	[Handwritten prompts (synthetic; no external seed data used)]	[SDG model: deepseek-ai/DeepSeek-V3.2, deepseek-ai/DeepSeek-R1-0528, Qwen/Qwen3-235B-A22B-Thinking-2507, Qwen/Qwen3-32B]; [Filtering model: openai/gpt-oss-120b, Qwen/Qwen3-32B, Qwen/Qwen3-235B-A22B-Instruct-2507]
Synthetic Long Context from Qwen3-235B-A22B-Instruct-2507	Text	Undisclosed	[Long-context SFT seed blend (pre-training blend + nano-v1 post-training data)]; [Long-context SFT data: lc_nothink 256k, MRCR 200k, RULER 256k]; [AALCR seed blend: SEC Filings, CC, Wikipedia, FinePDFs, ArXiv, Pile-NIH ExPorter, BioRxiv, PMC Article, USPTO Backgrounds, peS2o, Global Regulations, CORE, Gutenberg (PG-19), DOAB CC-BY, NDLTD, Amps, StackExchange, MathPile, Numinas]	[Qwen/Qwen3-235B-A22B-Thinking-2507]; [deepseek-ai/DeepSeek-R1]; [Qwen3-30B-A3B]
Synthetic Nemotron Math SFT from DeepSeek-V3.2-Speciale	Text	1,900,553	[Nemotron-Math-v2 (AOPS and StackExchange-math problems)]	[DeepSeek-V3.2-Speciale]
Synthetic Nemotron Math TIR from DeepSeek-V3.2	Text	1,789,258	[Nemotron-Math-v2 (AOPS and StackExchange-math problems)]	[DeepSeek-V3.2]
Synthetic NemoCascade OCR Distillation from gpt-oss-120b	Text	682,864	[Nemotron-X training datasets]	[gpt-oss-120b]
Synthetic CUDA 100k	Text	93,086	[KernelBook]; [HuggingFace Transformers]; [FlashInfer]	[gpt-oss-120b]; [DeepSeek-R1-0528]
Synthetic Science MCQ and QA Diversity from GPT-OSS and Kimi-K2	Text	30,358	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Science HLE with Python from GPT-OSS and Kimi-K2	Text	85,184	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Science Search and Python from GPT-OSS and Kimi-K2	Text	6,179	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Science Search from GPT-OSS and Kimi-K2	Text	32,554	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Finance Reasoning from GPT-OSS-120B and Qwen3-235B-A22B-Instruct-2507	Text	326,700	[SEC filings]	[GPT-OSS-120B, Qwen3-235B-A22B-Instruct-2507]
Synthetic Science Diversity MCQ from GPT-OSS and Kimi-K2	Text	532,942	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Science Diversity OpenQ from GPT-OSS and Kimi-K2	Text	131,045	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Science Reasoning No-Tool from GPT-OSS and Kimi-K2	Text	2,085,600	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Text-To-SQL from gpt-oss-120b	Text	Undisclosed	[In-house Text-to-SQL data]	[gpt-oss-120b]
Synthetic Tool Call Schema for RL (extended)	Text	707,967	[UltraTool]; [ToolEyes]; [AutoTools]; [API-Bank]; [Nemotron-Personas-USA]; [Salesforce xLAM function-calling]; [Glaive function-calling-v2]; [Agent-Ark/Toucan-1.5M]	[DeepSeek-V3.2]; [GLM-4.6]; [gpt-oss-120b]; [Kimi-K2-Instruct]
Synthetic Safety from gemma-3-4b-it, Nemotron-Nano-9B-v2, and gpt-oss-120b	Text	44,091	[Safety SFT Data]	[google/gemma-3-4b-it]; [Nemotron-Nano-9B-v2]; [gpt-oss-120b]
Synthetic Safety from DeepSeek-R1-0528, gpt-oss-120b, DeepSeek-R1-Distill-Qwen-7B, and Mixtral-8x7B-v0.1	Text	Undisclosed	[Nemotron Content Safety Dataset V2]; [Gretel Synthetic Safety Alignment Dataset]; [RedTeam-2K]; [Malicious Tasks]; [Nemotron-Personas-USA]	[DeepSeek-R1-0528]; [gpt-oss-120b]; [DeepSeek-R1-Distill-Qwen-7B]; [Qwen3-30B-A3B-Thinking-2507]; [Qwen3-235B-A22B-Instruct-2507]; [Mixtral-8x7B-v0.1]
Synthetic Tool Calling from Qwen3-235B-A22B-Thinking-2507 and Qwen3-Next-80B-A3B-Thinking	Text	Undisclosed	[ToolBench]; [glaive-function-calling-v2]; [APIGen Function-Calling]; [Nemotron-Personas-USA]	[Qwen3-235B-A22B-Thinking-2507]; [Qwen3-Next-80B-A3B-Thinking]
Synthetic Chat from gpt-oss-120b, Mixtral-8x22B-Instruct-v0.1, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	[C4]; [LMSYS-Chat-1M]; [ShareGPT]; [GSM8K]; [PRM800K]; [FinQA]; [WikiTableQuestions]; [Riddles]; [glaive-function-calling-v2]; [SciBench]; [tigerbot-kaggle-leetcodesolutions-en-2k]; [OpenBookQA]; [Advanced Reasoning Benchmark]; Software Heritage; [Khan Academy Math Keywords]; [WildChat-1M]; [Nemotron-Personas-USA]	[gpt-oss-120b]; [Mixtral-8x22B-Instruct-v0.1]; [Qwen3-235B-A22B-Instruct-2507]; [Qwen3-235B-A22B-Thinking-2507]
Synthetic Tool Use Interactive Agent from gpt-oss-120b, DeepSeek-R1-0528, Qwen3-32B, and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	NVIDIA Internal	[gpt-oss-120b]; [DeepSeek-R1-0528]; [Qwen3-32B]; [Qwen3-235B-A22B-Thinking-2507]
Synthetic DocFinQA and SWE-smith from Qwen3-Coder-480B-A35B-Instruct and Kimi-K2-Thinking	Text	Undisclosed	[DocFinQA]; [SWE-smith]	[Qwen3-Coder-480B-A35B-Instruct]; [Kimi-K2-Thinking]
Synthetic SWE-Gym from Qwen3-Coder-480B-A35B-Instruct	Text	Undisclosed	[SWE-Gym]	[Qwen3-Coder-480B-A35B-Instruct]
Synthetic SWE-Gym and R2E-Gym-Subset from Qwen3-Coder-480B-A35B-Instruct	Text	Undisclosed	[SWE-Gym]; [R2E-Gym-Subset]	[Qwen3-Coder-480B-A35B-Instruct]
Synthetic SWE-Gym and R2E-Gym-Subset from DeepSeek-R1-0528	Text	Undisclosed	[SWE-Gym]; [R2E-Gym-Subset]	[DeepSeek-R1-0528]
Synthetic HelpSteer, LMSYS-Chat-1M, and Nemotron-Personas-USA from gpt-oss-120b, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	[HelpSteer2]; [HelpSteer3]; [LMSYS-Chat-1M]; [Nemotron-Personas-USA]	[gpt-oss-120b]; [Qwen3-235B-A22B-Instruct-2507]; [Qwen3-235B-A22B-Thinking-2507]
Synthetic Nemotron-Personas-USA from gpt-oss-120b and Qwen3-8B	Text	Undisclosed	[Nemotron-Personas-USA]	[gpt-oss-120b]; [Qwen3-8B]
Vendor Terminal Bench-like Tasks (Droid)	Text	Undisclosed	[Droid Harness Pivot vendor data]	Undisclosed

Language Distribution in Post-Training

For our post-training recipe, we focused on the following languages in addition to English: French, German, Italian, Japanese, Spanish, and Chinese. Those languages were represented in the form of multilingual reasoning and translation tasks.

Testing Datasets

Data Collection Method by dataset

Hybrid: Automated, Manually-Collected, Synthetic

Labeling Method by dataset

Hybrid: Automated, Manually-Labeled, Synthetic

Properties: This corpus comprises a mix of high-quality standard benchmarks and test suites for modern agentic AI. These benchmarks test model capabilities on tasks such as tool-calling and instruction following.

Evaluation Datasets

Data Collection Method by dataset

Hybrid: Automated, Manually-Collected, Synthetic

Labeling Method by dataset

Hybrid: Automated, Manually-Labeled, Synthetic

Inference

Acceleration Engine: PyTorch
Test Hardware:\n
- NVIDIA Hopper\n
  - 1-8x H100
  - 1-8x H200
  \n
- NVIDIA Blackwell\n
  - GB200
  - GeForce RTX 5090
  \n
\n

Ethical Considerations

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We advise against circumvention of any provided safety guardrails contained in the Model without a substantially similar guardrail appropriate for your use case. For more details: Safety and Explainability Subcards.

For more detailed information on ethical considerations for this model, please see the Model Card++ Bias, Explainability, Safety & Security, and Privacy Subcards.

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\n \n $\"Homepage\"$ \n \n \n $\"Discord\"$ \n \n \n $\"Paper\"$ \n \n

\n\n

\n \n $\"License\"$ \n \n

\n\n## Description\n\n**NVIDIA-Nemotron-3.5-Bolt-E4B-4GB-BF16** is a small language model (SLM) further post-trained from [Gemma-4-E4B-it](https://huggingface.co/google/gemma-4-E4B-it) by NVIDIA. This is the full-precision (BF16) release. Nemotron-3.5-Bolt-E4B-4GB carries forward Gemma’s strong conversational capabilities and further improves multi-turn tool use, instruction following, and input grounding for fast local voice/device assistants, interactive personas, and embedded/Edge interfaces. Designed for low-memory, low-TTFT operation, the model allows agents to respond quickly while preserving headroom for applications or adjacent model pipelines.\n\nThis model is ready for commercial or non‑commercial use.\n\n## Model Summary\n\n| | |\n|:---|:---|\n| **Total Parameters** | 8B (4B effective) |\n| **Architecture** | Gemma-4-E4B |\n| **Precision** | BF16 |\n| **Context Length** | Input: up to 64K tokens; output: up to 64K tokens |\n| **Inputs / Outputs** | Text / text |\n| **Deployment** | vLLM; full-precision customization, post-training, and evaluation |\n| **Reasoning Mode** | Chat-template controls (`enable_thinking=True/False`) and runtime thinking budget |\n| **Conversational Mode** | Chat-template controls (`conversational_mode=True/False`) |\n| **Recommended Sampling** | Temperature 1.0, top_p 0.95 in both thinking modes |\n| **Best For** | Local voice and device assistants, interactive personas, and embedded interfaces |\n| **License** | [OpenMDW-1.1](https://github.com/OpenMDW/OpenMDW/blob/main/1.1/LICENSE.OpenMDW-1.1) |\n\n## Model Overview\n\n**Model Developer:** NVIDIA Corporation\n\n**Model Dates:** December 2025 - May 2026\n\n**Data Freshness:**\n\n* The post-training data has a cutoff date of May 2026.\n\n### What is Nemotron?\n\nNVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents.\n\n## Quick Start\n\nTo get quickly started on a single H100 you can use the following command.\n\nRun it with vLLM! (vLLM v0.31.0: `vllm/vllm-openai:v0.31.0`)\n\n### Without Speculative Decoding\n\n```shell\nexport MODEL=nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB-BF16\n\nvllm serve \"$MODEL\" \\\n --served-model-name \"nemotron-bolt\" \\\n --host 0.0.0.0 \\\n --port 8000 \\\n --dtype bfloat16 \\\n --tensor-parallel-size 1 \\\n --gpu-memory-utilization 0.85 \\\n --kv-cache-dtype auto \\\n --max-model-len 65536 \\\n --max-num-seqs 64 \\\n --max-num-batched-tokens 65536 \\\n --chat-template \"$MODEL/chat_template.jinja\" \\\n --enable-auto-tool-choice \\\n --tool-call-parser gemma4 \\\n --reasoning-parser gemma4 \\\n --limit-mm-per-prompt '{\"image\":0,\"video\":0,\"audio\":0}'\n```\n\n### With DFlash Speculative Decoding\n\nUse the same downloaded BF16 checkpoint with its companion DFlash drafter. Run one serving configuration at a time. This example uses a single concurrent sequence and seven speculative tokens; tune these settings for your workload.\n\n```shell\nexport MODEL=nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB-BF16\nexport DRAFTER_CKPT=nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB-DFlash-BF16\n\nvllm serve \"$MODEL\" \\\n --served-model-name \"nemotron-bolt\" \\\n --host 0.0.0.0 \\\n --port 8000 \\\n --dtype bfloat16 \\\n --tensor-parallel-size 1 \\\n --gpu-memory-utilization 0.85 \\\n --kv-cache-dtype auto \\\n --max-model-len 65536 \\\n --max-num-seqs 1 \\\n --max-num-batched-tokens 65536 \\\n --chat-template \"$MODEL/chat_template.jinja\" \\\n --enable-auto-tool-choice \\\n --tool-call-parser gemma4 \\\n --reasoning-parser gemma4 \\\n --limit-mm-per-prompt '{\"image\":0,\"video\":0,\"audio\":0}' \\\n --speculative-config \"{\n \\\"method\\\": \\\"dflash\\\",\n \\\"model\\\": \\\"$DRAFTER_CKPT\\\",\n \\\"num_speculative_tokens\\\": 7\n }\"\n```\n\n### Sample Request\n\nThe same request works with either serving configuration.\n\n```shell\ncurl -s \"http://:8000/v1/chat/completions\" \\\n -H 'Content-Type: application/json' \\\n -d '{\n \"model\": \"nemotron-bolt\",\n \"messages\": [\n {\"role\": \"user\", \"content\": \"What is 17 multiplied by 23?\"}\n ],\n \"temperature\": 0.0,\n \"max_tokens\": 512,\n \"skip_special_tokens\": false,\n \"chat_template_kwargs\": {\n \"enable_thinking\": true\n }\n }'\n```\n\nFor more details on how to deploy and use the model — see the [Quick Start Guide](#quick-start-guide) below!\n\n## License/Terms of Use\n\n**GOVERNING DOWNLOAD TERMS:** Use of the model is governed by the [OpenMDW License Agreement, version 1.1](https://github.com/OpenMDW/OpenMDW/blob/main/1.1/LICENSE.OpenMDW-1.1).\n\n**ADDITIONAL INFORMATION:** [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).\n\n## Benchmarks\n\n### Reasoning Benchmark Evaluations\n\nWe evaluated our model on the following benchmarks:\n\nAccuracy numbers are measured by NVIDIA under a consistent harness (NeMo Gym / Nemo Evaluator SDK); they may differ from vendors' self-reported numbers.\n\nTODO: Placeholder (Accuracy table)\n\n### Deployment Geography: Global\n\n### Use Case\n\nNVIDIA-Nemotron-3.5-Bolt-E4B-4GB-BF16 is the full-precision release of a general purpose reasoning and chat model, and is intended primarily for **customization and post-training** rather than direct production inference. It is intended to be used by developers who want to: post-train the model on their own data (SFT, RL via [NeMo RL](https://github.com/NVIDIA-NeMo/RL) and [NeMo Gym](https://github.com/NVIDIA-NeMo/Gym), or distillation), adapt it to a domain or task, produce quantized variants (NVFP4, W4A16, GGUF) for their own deployment targets, or run full-precision research and evaluation. English and coding languages are the primary languages, with Spanish, French, German, Italian, and Japanese also supported.\n\n### Release Date\n\nHugging Face 10/23/2026 via https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB-BF16\n\n## Model Architecture\n\n- **Architecture Type:** Gemma 4\n- **Network Architecture:** Gemma-4-E4B\n- **Number of model parameters:** 8B (4B effective parameters)\n\n## Model Design\n\nThe model was further post-trained from Gemma-4-E4B-it.\n\n## Training Methodology\n\nReinforcement Learning\n\n* The model underwent multi-environment reinforcement learning using GRPO (Group Relative Policy Optimization) across instruction following, multi-step tool use, multi-turn conversations, and structured output environments. It utilized an asynchronous RL architecture that decouples training from inference.\n* Software used for reinforcement learning: [NeMo RL](https://github.com/NVIDIA-NeMo/RL), [NeMo Gym](https://github.com/NVIDIA-NeMo/Gym)\n\nNVIDIA-Nemotron-3.5-Bolt-E4B-4GB-BF16 is a result of the above work.\n\n## Input\n\n- **Input Type(s):** Text\n- **Input Format(s):** String\n- **Input Parameters:** One-Dimensional (1D): Sequences\n- **Other Properties Related to Input:** Maximum context length up to 1M tokens. Supported languages include English, Spanish, French, German, Italian, and Japanese.\n\n## Output\n\n- **Output Type(s):** Text\n- **Output Format:** String\n- **Output Parameters:** One-Dimensional (1D): Sequences\n- **Other Properties Related to Output:** Maximum context length up to 128K tokens\n\nOur AI models are designed and/or 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.\n\n## Software Integration\n\n- **Runtime Engine(s):** PyTorch\n- **Supported Hardware Microarchitecture Compatibility:** NVIDIA Ampere - A100; NVIDIA Blackwell; NVIDIA Hopper - H100-80GB\n- **Supported Operating System(s):** Linux\n\nThe 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.\n\n## Model Version(s)\n\n* GA (10/23/2026)\n\n## Quick Start Guide\n\nAll deployment snippets below assume:\n\n```sh\nexport MODEL_CKPT=nvidia/Gemma-Nemotron-4B-BF16\n```\n\n### vLLM\n\n* vLLM: `vllm/vllm-openai:v0.31.0`\n\n#### 1x H100 / A100 / GB200\n\nFor max throughput deployments, use the following configuration, no speculative decoding strategy is best for this serving configuration, and due to memory constraints the Mamba cache `dtype` is set as FP16:\n\n```sh\nvllm serve \"$MODEL\" \\\n --served-model-name \"nemotron-bolt\" \\\n --host 0.0.0.0 \\\n --port 8000 \\\n --dtype bfloat16 \\\n --tensor-parallel-size 1 \\\n --gpu-memory-utilization 0.85 \\\n --kv-cache-dtype auto \\\n --max-model-len 65536 \\\n --max-num-seqs 64 \\\n --max-num-batched-tokens 65536 \\\n --chat-template \"$MODEL/chat_template.jinja\" \\\n --enable-auto-tool-choice \\\n --tool-call-parser gemma4 \\\n --reasoning-parser gemma4 \\\n --limit-mm-per-prompt '{\"image\":0,\"video\":0,\"audio\":0}'\n```\n\n### API Client\n\nThe examples below use the OpenAI-compatible client and work with the serving backend above. Recommended sampling settings are **Temperature 1.0** and **Top\\_P 0.95** in both thinking enabled and disabled modes.\n\n```py\nfrom openai import OpenAI\nclient = OpenAI(base_url=\"http://localhost:8000/v1\", api_key=\"EMPTY\")\nMODEL = \"nemotron-bolt\"\n```\n\nGemma-Nemotron exposes reasoning control through chat-template kwargs: thinking enabled (the default), thinking disabled for direct answers, and a runtime thinking budget.\n\n#### Reasoning On\n\n```py\nresponse = client.chat.completions.create(\n model=MODEL,\n messages=[{\"role\": \"user\", \"content\": \"Write a haiku about GPUs\"}],\n max_tokens=16000,\n temperature=1.0,\n top_p=0.95,\n extra_body={\n \"skip_special_tokens\": False,\n \"chat_template_kwargs\": {\"enable_thinking\": True},\n }\n)\nprint(response.choices[0].message.content)\n```\n\n#### Reasoning Off\n\n```py\nresponse = client.chat.completions.create(\n model=MODEL,\n messages=[{\"role\": \"user\", \"content\": \"What is the capital of Japan?\"}],\n max_tokens=16000,\n temperature=1.0,\n top_p=0.95,\n extra_body={\n \"skip_special_tokens\": False,\n \"chat_template_kwargs\": {\"enable_thinking\": False},\n }\n)\nprint(response.choices[0].message.content)\n```\n\n#### Streaming\n\n```py\nstream = client.chat.completions.create(\n model=MODEL,\n messages=[{\"role\": \"user\", \"content\": \"Explain speculative decoding in two sentences\"}],\n max_tokens=16000,\n temperature=1.0,\n top_p=0.95,\n stream=True,\n extra_body={\"skip_special_tokens\": False},\n)\nfor chunk in stream:\n print(chunk.choices[0].delta.content or \"\", end=\"\", flush=True)\n```\n\n#### Tool Calling\n\nFor vLLM, add the following to any serve command above:\n\n```sh\n--enable-auto-tool-choice \\\n--tool-call-parser gemma4 \\\n--reasoning-parser gemma4 \\\n```\n\nSample request with tool-calling enabled:\n\n```py\ntools = [{\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_weather\",\n \"description\": \"Get the current weather for a city\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\"city\": {\"type\": \"string\"}},\n \"required\": [\"city\"],\n },\n },\n}]\n\nresponse = client.chat.completions.create(\n model=MODEL,\n messages=[{\"role\": \"user\", \"content\": \"What's the weather in Santa Clara?\"}],\n tools=tools,\n max_tokens=16000,\n temperature=1.0,\n top_p=0.95,\n extra_body={\n \"skip_special_tokens\": False,\n \"chat_template_kwargs\": {\"enable_thinking\": True},\n },\n)\nprint(response.choices[0].message.tool_calls)\n```\n\n#### Conversational Mode\n\nGemma-Nemotron supports a conversational mode. If enabled, Gemma-Nemotron responds concisely and without as much textual formatting. This mode is suitable for conversational and spoken interaction-based applications. Conversational mode can be toggled as follows:\n\n```py\nresponse = client.chat.completions.create(\n model=MODEL,\n messages=[{\"role\": \"user\", \"content\": \"What is the capital of Japan?\"}],\n max_tokens=16000,\n temperature=1.0,\n top_p=0.95,\n extra_body={\n \"skip_special_tokens\": False,\n \"chat_template_kwargs\": {\"conversational_mode\": True},\n }\n)\nprint(response.choices[0].message.content)\n```\n\nConversational mode can be combined with thinking enabled and disabled mode, as well as tool-calling.\n\n#### Runtime Thinking Budget Control\n\n

Client implementation and usage

\n\nIn thinking enabled mode, you can control the thinking budget that Gemma-Nemotron uses with a two-step inference client. The client stops Gemma-Nemotron’s reasoning process at a defined token budget and gets a final response from the model. The budget control client can be implemented as following:\n\n```py\nimport copy\nimport requests\n\nclass GemmaNemotronThinkingBudgetClient:\n def __init__(self, base_url):\n self.base_url = base_url.rstrip(\"/\")\n\n def _post(self, payload):\n response = requests.post(\n f\"{self.base_url}/chat/completions\",\n json=payload,\n timeout=300,\n )\n response.raise_for_status()\n return response.json()\n\n def chat_completion(\n self,\n model,\n messages,\n max_thinking_budget=512,\n max_tokens=1024,\n tools=None,\n temperature=1.0,\n top_p=0.95,\n seed=None,\n ):\n if max_tokens <= max_thinking_budget:\n raise ValueError(\n \"max_tokens must be larger than max_thinking_budget\"\n )\n\n original_messages = copy.deepcopy(messages)\n sampling = {\n \"temperature\": temperature,\n \"top_p\": top_p,\n }\n if seed is not None:\n sampling[\"seed\"] = seed\n\n first_payload = {\n \"model\": model,\n \"skip_special_tokens\": False,\n \"messages\": original_messages,\n \"max_tokens\": max_thinking_budget,\n \"chat_template_kwargs\": {\"enable_thinking\": True},\n **sampling,\n }\n if tools is not None:\n first_payload[\"tools\"] = tools\n first_payload[\"tool_choice\"] = \"auto\"\n\n first = self._post(first_payload)\n choice = first[\"choices\"][0]\n message = choice[\"message\"]\n reasoning = (\n message.get(\"reasoning_content\")\n or message.get(\"reasoning\")\n or \"\"\n )\n partial_answer = message.get(\"content\") or \"\"\n tool_calls = message.get(\"tool_calls\") or []\n\n if choice.get(\"finish_reason\") == \"stop\" and (\n partial_answer or tool_calls\n ):\n return {\n \"reasoning_content\": reasoning,\n \"content\": partial_answer,\n \"tool_calls\": tool_calls,\n \"finish_reason\": choice.get(\"finish_reason\"),\n \"budget_forced\": False,\n }\n\n used_tokens = first.get(\"usage\", {}).get(\n \"completion_tokens\",\n max_thinking_budget,\n )\n remaining_tokens = max_tokens - used_tokens\n if remaining_tokens <= 0:\n raise ValueError(\"Increase max_tokens or lower the budget\")\n\n continuation_messages = copy.deepcopy(original_messages)\n continuation_messages.append(\n {\n \"role\": \"assistant\",\n \"reasoning_content\": reasoning,\n \"content\": partial_answer,\n }\n )\n\n second_payload = {\n \"model\": model,\n \"skip_special_tokens\": False,\n \"messages\": continuation_messages,\n \"max_tokens\": remaining_tokens,\n \"add_generation_prompt\": False,\n \"continue_final_message\": True,\n \"chat_template_kwargs\": {\n \"enable_thinking\": True,\n \"preserve_thinking\": True,\n },\n **sampling,\n }\n if tools is not None:\n second_payload[\"tools\"] = tools\n second_payload[\"tool_choice\"] = \"auto\"\n\n second = self._post(second_payload)\n second_choice = second[\"choices\"][0]\n second_message = second_choice[\"message\"]\n return {\n \"reasoning_content\": reasoning,\n \"content\": partial_answer + (\n second_message.get(\"content\") or \"\"\n ),\n \"tool_calls\": second_message.get(\"tool_calls\") or [],\n \"finish_reason\": second_choice.get(\"finish_reason\"),\n \"budget_forced\": True,\n }\n```\n\nYou can then use the client as:\n\n```py\nclient = GemmaNemotronThinkingBudgetClient(\n base_url=\"http://localhost:8000/v1\", api_key=\"EMPTY\"\n)\nMODEL = \"nemotron-bolt\"\n\nresult = client.chat_completion(\n model=MODEL,\n messages=[\n {\"role\": \"system\", \"content\": \"\"},\n {\"role\": \"user\", \"content\": \"What is 2+2?\"},\n ],\n max_thinking_budget=32,\n max_tokens=512,\n temperature=1.0,\n top_p=0.95,\n seed=17,\n)\n\nprint(result[\"content\"])\n# Tool-Calling Example\n\ncalculate_tip = {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"calculate_tip\",\n \"description\": \"Calculate the tip for a bill.\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"bill_total\": {\"type\": \"number\"},\n \"tip_percentage\": {\"type\": \"number\"},\n },\n \"required\": [\"bill_total\", \"tip_percentage\"],\n \"additionalProperties\": False,\n },\n },\n}\n\ntool_result = client.chat_completion(\n model=MODEL,\n messages=[\n {\"role\": \"system\", \"content\": \"\"},\n {\n \"role\": \"user\",\n \"content\": (\n \"My bill is $100. Use calculate_tip to calculate an 18% tip.\"\n ),\n },\n ],\n tools=[calculate_tip],\n max_thinking_budget=32,\n max_tokens=512,\n temperature=1.0,\n top_p=0.95,\n seed=23,\n)\n\nif not tool_result[\"tool_calls\"]:\n raise RuntimeError(\"The model did not return a parsed tool call.\")\n\ntool_call = tool_result[\"tool_calls\"][0]\nprint(tool_call[\"function\"][\"name\"])\nprint(tool_call[\"function\"][\"arguments\"])\n\n# Example output:\n# calculate_tip\n# {\"bill_total\":100,\"tip_percentage\":18}\n```\n\n##### How Runtime Budget Control Works\n\n* Phase 1 executes an initial request to `/v1/chat/completions`, configuring `enable_thinking=True` within `chat_template_kwargs` alongside the designated token limit via `max_thinking_budget`. \n* Should execution terminate standardly and return complete output, the system yields this output without triggering secondary inference. \n* If execution halts upon reaching the designated ceiling, extract `reasoning_content` directly from the message object. \n* Construct and append an assistant role turn incorporating the extracted thought traces along with any generated output snippets. \n* Re-pass the function schemas while assigning `preserve_thinking=True` inside the template parameters. \n* Assign the secondary `max_tokens` allocation to match the remaining generation threshold. \n* Phase 2 dispatches to `/v1/chat/completions` using `continue_final_message=True` and `add_generation_prompt=False`, preserving active Gemma 4 tool and reasoning parsing modules.\n\n##### Limits and Recommendations for Runtime Budget Control\n\n* Restricted token allocations may truncate thought processes mid-sentence. While output clarity is typically maintained, task-specific output evaluation remains necessary. \n* The client halts execution immediately upon reaching the pre-configured initial generation ceiling, ignoring subsequent line boundaries. \n* Conduct systematic allocation sweeps using standard evaluation prompts prior to establishing deployment parameters. \n* Illustration: To maintain a strict 500-token overall threshold, evaluate unconstrained model generation metrics (e.g., averaging 200 tokens). Assigning a dedicated 300-token reasoning parameter alongside a 500-token global ceiling forces thought termination at 300 tokens, reserving 200 tokens for output synthesis. \n* To encourage brief output (excluding internal reasoning steps), incorporate explicit word- or sentence-count constraints within system prompts.\n\n

\n\n#### Asynchronous Tool Calling\n\n* Transmit asynchronous functions via the standard OpenAI tools structure. \n* Retrieve assistant function executions directly from `message.tool_calls`. \n* Restrict generation to a single tool invocation per assistant turn. \n* Assign a persistent task identifier to each decoupled job execution. \n* Provide subsequent execution outputs as a user role turn prefixed with something like `\"TOOL RESULT:\"`. \n* Avoid submitting secondary tool turns for requests with an active pending response. \n* Maintain tracked status sets for unresolved and finalized job identifiers. \n* Synthesize the final response only after all background processes finish and zero calls remain outstanding.\n\nExample:\n\nAssistant tool-call\n\n```json\n{\n \"role\": \"assistant\",\n \"tool_calls\": [{\n \"id\": \"call_1\",\n \"type\": \"function\",\n \"function\": {\n \"name\": \"build_report\",\n \"arguments\": \"{\\\"_task_id\\\":\\\"task_1\\\",\\\"scope\\\":\\\"weekly\\\"}\"\n }\n }]\n}\n```\n\nImmediate acknowledgement with pending result\n\n```json\n{\n \"role\": \"tool\",\n \"tool_call_id\": \"call_1\",\n \"content\": \"{\\\"status\\\":\\\"pending\\\",\\\"task_id\\\":\\\"task_1\\\",\\\"function\\\":\\\"build_report\\\"}\"\n}\n```\n\nLater tool result completion result\n\n```json\n{\n \"role\": \"user\",\n \"content\": \"ASYNC TOOL UPDATE: {\\\"status\\\":\\\"completed\\\",\\\"task_id\\\":\\\"task_1\\\",\\\"function\\\":\\\"build_report\\\",\\\"response\\\":\\\"Report is ready.\\\"}\"\n}\n```\n\nThe model processes the finalized payload to resume generation.\n\n## Prompting and Usage Guidelines\n\nGeneral guidelines for using Gemma-Nemotron in conversational and agentic applications.\n\n### Prompt Structure and Content\n\n* Store persistent behavior constraints and operational guidelines in system prompts. \n* Use explicit headers to organize roles, tasks, tool configs, and formatting safety rules. \n* Keep the primary objective clear and concise. \n* Write unambiguous, verifiable instructions and resolve conflicting guidance. \n* Remove redundant guidance and repetitive few-shot examples. \n* Use explicit constraints (e.g., `\"limit responses to 1-2 sentences\"`) instead of vague requests (e.g., `\"be brief\"`). \n* Avoid synthetic or non-model outputs in `\"assistant\"` turns. \n* Adhere strictly to standard `system/user/assistant/tool` turn sequences.\n\n### Conveying Environment State in Stateful Applications\n\n* Prefix context blocks with explicit structural markers (e.g., `\"# CURRENT STATE\"`). \n* Clearly specify what part of the context belongs to the model and what part refers to information for other entities like the user. \n* When state updates end a message, restate the core goal alongside them.\n\n### Instructing the Model\n\n* Define required behaviors with precise language and mandate necessary tool calls. \n* Provide fallback protocols for requests exceeding available tool capabilities. \n* Separate output formatting rules from task directives, and use the official chat template.\n\n### Conversational Responses\n\n* Set `conversational_mode=True` for concise, low-formatting outputs suited for audio or spoken interaction. \n* Set or leave the flag to `False` to retain standard generation style.\n\n## Training, Testing, and Evaluation Datasets\n\n### Training\n\n#### Dataset Overview\n\n**Data Modality:** Text\n\n**Training Data Size:** More than 20 Trillion Tokens\n\n**Dataset partition:** *Training [100%], testing [0%], validation [0%]*\n\n**Time period for training data collection:** 2013 to December 2025\n\n**Time period for testing data collection:** 2013 to December 2025\n\n**Time period for validation data collection:** 2013 to December 2025\n\n**Data Collection Method by dataset:** Hybrid: Automated, Manually-Collected, Synthetic\n\n**Labeling Method by dataset:** Hybrid: Automated, Manually-Labeled, Synthetic\n\nThe post-training corpus for NVIDIA-Nemotron-3.5-Bolt-E4B-4GB-BF16 consists of high-quality curated and synthetically-generated data. Primary languages used for post-training include English, French, German, Italian, Japanese, Spanish, and Chinese.\n\nThese datasets, such as FinePDFs, EssentialWeb, HotpotQA, SQuAD, and HelpSteer3, do not collectively or exhaustively represent all demographic groups (and proportionally therein). For instance, these datasets do not contain explicit mentions of demographic classes such as age, gender, or ethnicity in 64-99% of samples, depending on the source. In the subset where such terms are present, document-based datasets (FinePDFs and EssentialWeb) contain representational skews, such as references to \"male\" outnumbering those to \"female\", and mentions of \"White\" as the most frequent among ethnic identifiers (comprising 43-44% of ethnicity mentions). To mitigate these imbalances, we recommend considering evaluation techniques such as bias audits, fine-tuning with demographically balanced datasets, and mitigation strategies like counterfactual data augmentation to align with the desired model behavior. This evaluation used a 3,000-sample subset per dataset, identified as the optimal threshold for maximizing embedder accuracy.\n\nDuring post-training, we generate synthetic data by distilling trajectories, solutions, and translations from strong teacher models and agent systems, often grounded in real tasks or documents and aggressively filtered for quality. For math, code, and science, we start from curated problem sets and use open source permissive models such as GPT-OSS-120B to produce step-by-step reasoning traces, candidate solutions, best-of-n selection traces, and verified CUDA kernels. For long-context and science, we build synthetic QA and reasoning data by retrieving passages from long documents, generating MCQ/OpenQA questions and answers, and paraphrasing them into multiple prompt/response formats to ensure diversity. Across all pipelines we stack automated verification—compilers, numerical checks, language identification—to ensure our data is high quality.\n\nFor all domains, we apply a unified data filtering pipeline to ensure that only high-quality, license-compliant, and verifiable samples are used for post-training. We first discard malformed examples using structural checks (e.g., missing tool definitions when tool calls are present). We then aggressively filter reasoning traces exhibiting pathological repetition, such as repeated n-grams within a sliding window or across the entire trajectory, which we found to be a strong indicator of malformed or low-quality reasoning. Finally, based on internal audits of synthetically generated datasets, we observed that some teacher models occasionally produce reasoning traces and final responses that implicitly align with specific political entities or promote nationalistic narratives. To mitigate this, we apply targeted keyword- and regex-based filters and remove all trajectories matching such behavior.\n\nAlongside the model, we release our final pre-training and post-training 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.\n\n

Detailed dataset information

\n\n#### Public Datasets\n\n| Dataset | Collection Period |\n| :---- | :---- |\n| [GSM8K](https://github.com/openai/grade-school-math) | 4/23/2025 |\n| [CC-NEWS](https://commoncrawl.org/blog/news-dataset-available) | 4/23/2025 |\n| [Common Crawl](https://commoncrawl.org/) | 4/23/2025 |\n| [Wikimedia](https://dumps.wikimedia.org/) | 4/23/2025 |\n| [Bespoke-Stratos-17k](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k) | 4/23/2025 |\n| [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k) | 4/23/2025 |\n| [glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) | 4/23/2025 |\n| [APIGen Function-Calling](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) | 4/23/2025 |\n| [LMSYS-Chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) | 4/23/2025 |\n| [Open Textbook Library - CC BY-SA & GNU subset](https://open.umn.edu/opentextbooks/textbooks/) and [OpenStax - CC BY-SA subset](https://openstax.org/) | 4/23/2025 |\n| [Advanced Reasoning Benchmark](https://github.com/TheDuckAI/arb), [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k), [PRM800K](https://github.com/openai/prm800k), and [SciBench](https://github.com/mandyyyyii/scibench) | 4/23/2025 |\n| [FineWeb-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2) | 4/23/2025 |\n| [Court Listener](https://www.courtlistener.com/help/api/bulk-data/) | Legacy Download |\n| [peS2o](https://huggingface.co/datasets/allenai/peS2o) | Legacy Download |\n| [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math) | Legacy Download |\n| [BioRxiv](https://www.biorxiv.org/tdm) | Legacy Download |\n| [PMC Open Access Subset](https://pmc.ncbi.nlm.nih.gov/tools/openftlist/) | Legacy Download |\n| [OpenWebText2](https://openwebtext2.readthedocs.io/en/latest/) | Legacy Download |\n| [Stack Exchange Data Dump](https://archive.org/details/stackexchange) | Legacy Download |\n| [PubMed Abstracts](https://github.com/thoppe/The-Pile-PubMed) | Legacy Download |\n| [NIH ExPorter](https://reporter.nih.gov/exporter) | Legacy Download |\n| [arXiv](https://info.arxiv.org/help/bulk_data/index.html) | Legacy Download |\n| [BigScience Workshop Datasets](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#datasets) | Legacy Download |\n| Reddit Dataset | Legacy Download |\n| [SEC's Electronic Data Gathering, Analysis, and Retrieval (EDGAR)](https://www.sec.gov/search-filings) | Legacy Download |\n| [Advanced Mathematical Problem Solving](https://github.com/hendrycks/math?tab=readme-ov-file) | Legacy Download |\n| [MathPile](https://github.com/GAIR-NLP/MathPile/) | Legacy Download |\n| [NuminaMath CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT) | Legacy Download |\n| [PMC Article](https://pmc.ncbi.nlm.nih.gov/tools/textmining/) | Legacy Download |\n| [FLAN](https://github.com/google-research/FLAN) | Legacy Download |\n| [Advanced Reasoning Benchmark](https://github.com/TheDuckAI/arb) | Legacy Download |\n| [SciBench](https://github.com/mandyyyyii/scibench) | Legacy Download |\n| [WikiTableQuestions](https://huggingface.co/datasets/stanfordnlp/wikitablequestions) | Legacy Download |\n| [FinQA](https://finqasite.github.io/) | Legacy Download |\n| [Riddles](https://github.com/crawsome/riddles) | Legacy Download |\n| [Problems in Elementary Mathematics for Home Study](https://archive.org/details/AntonovVygodskyNikitinSankinProblemsInElementaryMathematicsForHomeStudyMir1982) | Legacy Download |\n| [MedMCQA](https://huggingface.co/datasets/openlifescienceai/medmcqa) | Legacy Download |\n| [Cosmos QA](https://huggingface.co/datasets/allenai/cosmos_qa) | Legacy Download |\n| [MCTest](https://huggingface.co/datasets/sagnikrayc/mctest) | Legacy Download |\n| [AI2's Reasoning Challenge](https://huggingface.co/datasets/allenai/ai2_arc) | Legacy Download |\n| [OpenBookQA](https://github.com/allenai/OpenBookQA) | Legacy Download |\n| [MMLU Auxiliary Train](https://huggingface.co/datasets/cais/mmlu/viewer/all/auxiliary_train) | Legacy Download |\n| [social-chemestry-101](https://huggingface.co/datasets/tasksource/social-chemestry-101) | Legacy Download |\n| [Moral Stories](https://huggingface.co/datasets/demelin/moral_stories) | Legacy Download |\n| [The Common Pile v0.1](https://huggingface.co/common-pile) | Legacy Download |\n| [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath) | Legacy Download |\n| [MegaMath](https://huggingface.co/datasets/LLM360/MegaMath) | Legacy Download |\n| [MultiverseMathHard](https://huggingface.co/datasets/Nexusflow/MultiverseMathHard) | 10/2/2025 |\n| [SWE-Gym](https://huggingface.co/datasets/SWE-Gym/SWE-Gym) | 10/2/2025 |\n| [WorkBench](https://github.com/olly-styles/WorkBench/tree/main/data/raw) | 10/2/2025 |\n| [WildChat-1M](https://huggingface.co/datasets/allenai/WildChat-1M) | 10/2/2025 |\n| [OpenCodeReasoning-2](https://huggingface.co/datasets/nvidia/OpenCodeReasoning-2) | 10/2/2025 |\n| [HelpSteer3](https://huggingface.co/datasets/nvidia/HelpSteer3) | 10/2/2025 |\n| [opc-sft-stage2](https://huggingface.co/datasets/OpenCoder-LLM/opc-sft-stage2) | 10/2/2025 |\n| [Big-Math-RL-Verified](https://huggingface.co/datasets/SynthLabsAI/Big-Math-RL-Verified) | 10/2/2025 |\n| [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA) | 10/2/2025 |\n| [simple-arithmetic-problems](https://huggingface.co/datasets/garrethlee/simple-arithmetic-problems) | 10/2/2025 |\n| [arithmetic](https://huggingface.co/datasets/EleutherAI/arithmetic) | 10/2/2025 |\n| [Skywork-OR1-RL-Data](https://huggingface.co/datasets/Skywork/Skywork-OR1-RL-Data) | 10/2/2025 |\n| FastChat | 10/2/2025 |\n| News Commentary | 10/2/2025 |\n| [Essential-Web](https://huggingface.co/datasets/EssentialAI/essential-web-v1.0) | 10/2/2025 |\n| [finepdfs](https://huggingface.co/datasets/HuggingFaceFW/finepdfs) | 10/2/2025 |\n| [HotpotQA](https://huggingface.co/datasets/hotpotqa/hotpot_qa) | 10/2/2025 |\n| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | 10/2/2025 |\n| [NLTK Words Lists](https://www.nltk.org/nltk_data/) | 10/2/2025 |\n\n#### Private Non-publicly Accessible Datasets of Third Parties\n\n| Dataset | Model(s) used |\n|---------|---------------|\n| Global Regulation | Unknown |\n| TAUS Translation Memory | Unknown |\n| Scale HLE | Unknown |\n| HackerRank Coding | Unknown |\n| RL data for Search | Gemini 3; GPT-5 |\n| Mercor SWE-AgentsV1 | Undisclosed |\n\n#### Private Non-publicly Accessible Datasets by NVIDIA\n\n| Dataset | Model(s) used |\n|---------|---------------|\n| Simple Minesweeper | Undisclosed |\n| Simple Sudoku | Undisclosed |\n| Multitool Typewriter Hard | Undisclosed |\n| Machine Translation of News Commentary and TAUS Translation Memory | Undisclosed |\n| Machine Translation of STEM - | [Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) |\n| Competitive Coding RL data from Nemotron Cascade | Undisclosed |\n| Long context RL | Undisclosed |\n| Single-step SWE RL for patch generation | Undisclosed |\n| OpenHands SWE | Undisclosed |\n\n#### NVIDIA-Sourced Synthetic Datasets (Post-Training)\n\n| Dataset | Modality | Dataset Size | Seed Dataset | Model(s) used for generation |\n| :---- | :---- | :---- | :---- | :---- |\n| Synthetic Competitive MATH Proofs from DeepSeek-V4-Pro | Text | Undisclosed | [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions] | [deepseek-ai/DeepSeek-V4-Pro] |\n| Synthetic Hermes Agent Reasoning Traces | Text | Undisclosed | [lambda/hermes-agent-reasoning-traces] | [hermes-agent-generator] |\n| Synthetic Competitive Coding from DeepSeek-V4-Pro | Text | Undisclosed | [NVCompetitiveCodingV1] | [deepseek-ai/DeepSeek-V4-Pro] |\n| Synthetic Competitive Science Reasoning from DeepSeek-V4-Pro | Text | Undisclosed | [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [EssentialAI/essential-web-v1.0]; [cdquestions.com]; [Pile-FreeLaw]; [Vedantu]; [askfilo]; [doubtnut]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]; [AAPT]; [ChemData 700K]; [oMeBench]; [Flavor Analysis and Recognition Transformer]; [ChemCoTBench]; [Llama Nemotron Dataset] | [deepseek-ai/DeepSeek-V4-Pro] |\n| Synthetic Competitive MATH CoT and TIR from Nemotron 5.5 | Text | Undisclosed | [Pile-FreeLaw]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions] | [Nemotron 5.5] |\n| Vendor Terminal Bench-like Tasks from Mercor | Text | Undisclosed | [Terminal bench like tasks curated by the vendor] | [Undisclosed - purchased dataset] |\n| Turing Math Data Pack | Text | Undisclosed | [Turing Math Data Pack dataset] | [Undisclosed - purchased dataset] |\n| Synthetic Holdout, Skywork, DAPO, and Turing Math from GPT-5.5 | Text | Undisclosed | [DocQA-RL-1.6K]; [DAPO-Math-17k] | [GPT-5.5] |\n| Synthetic Long Context RL from QwenLong L1 and DocQA-RL-1.6K | Text | Undisclosed | [DocQA-RL-1.6K] | Undisclosed |\n| Synthetic Competitive Coding Gym Tasks | Text | Undisclosed | [NVCompetitiveCodingV1.1] | Undisclosed |\n| Synthetic Finance SEC Search Agent from GPT-OSS-120B and Qwen3 | Text | Undisclosed | [SEC filings from sec.gov] | [GPT-OSS-120B]; [Qwen3-235B-A22B-Instruct]; [Qwen3-4B-Instruct] |\n| Synthetic Structured Outputs from Qwen3-30B-A3B-Instruct-2507, Qwen3-30B-A3B-Thinking-2507, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | [Nemotron-RL-agent-structured-outputs-v1] | [Qwen3-30B-A3B-Instruct-2507]; [Qwen3-235B-A22B-Instruct-2507] |\n| Synthetic Long Context Equivalence Rule from Qwen3-235B-A22B-Thinking-2507 and DeepSeek-R1 | Text | Undisclosed | [Long-context SFT data] | [Qwen/Qwen3-235B-A22B-Thinking-2507]; [Deepseek-ai/DeepSeek-R1] |\n| Synthetic Science RL Data Blend from Qwen2.5-32B | Text | Undisclosed | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [Qwen2.5-32B] |\n| Synthetic Abstention Data from Nemotron Super v3 | Text | Undisclosed | [Go abstention Dataset] | [nvidia/nvidia/nemotron-3-super-v3] |\n| Synthetic Chemistry Data from Nemotron Super v3 | Text | Undisclosed | [ChemData 700K] | [nvidia/nvidia/nemotron-3-super-v3] |\n| Synthetic Tool Call Schema for RL | Text | 469,983 | [UltraTool]; [ToolEyes]; [AutoTools]; [API-Bank]; [Nemotron-Personas-USA]; [Salesforce xLAM function-calling]; [Glaive function-calling-v2]; [Agent-Ark/Toucan-1.5M] | [DeepSeek-V3.2]; [GLM-4.6]; [gpt-oss-120b]; [Kimi-K2-Instruct] |\n| Synthetic Freeform Text Formatting from GPT-OSS-120B | Text | Undisclosed | [In-house data] | [GPT OSS 120B - Apache 2.0] |\n| Synthetic Citation Formatting from GPT-OSS-120B | Text | Undisclosed | [In-house data] | [GPT OSS 120B - Apache 2.0] |\n| Droid Harness Pivot Vendor Data | Text | Undisclosed | [Droid Harness Pivot vendor data] | Undisclosed |\n| Synthetic HotpotQA Training Data from Qwen3-235B | Text | Undisclosed | [HotpotQA] | [Qwen3-235B] |\n| Synthetic Natural Language Math Proofs from Nemotron 5.5 | Text | Undisclosed | [AMC8, AMC10, and AIME problem sets hosted on Art of Problem Solving]; [Pile-StackExchange] | [Nemotron 5.5] |\n| Synthetic Stack Overflow OpenQ | Text | Undisclosed | [Pile-FreeLaw] | Undisclosed |\n| Chemistry Ether0 Vendor Data | Text | Undisclosed | [Chemistry ether0 vendor data] | Undisclosed |\n| Synthetic Litmus-Bench Chemistry from ChEMBL | Text | Undisclosed | [ChEMBL]; [Nemo Gym RL dataset generated from ChEMBL with RDKit] | Undisclosed |\n| Synthetic ZINC Chemistry from Nemotron Super v3 | Text | Undisclosed | [ZINC] | [Nemotron Super v3] |\n| ARC-AGI Gym Environment | Text | Undisclosed | [ARC-AGI-2] | [ARC-AGI-2] |\n| Synthetic Agentic Search Tool-Use from DeepSeek-V3.2 | Text | Undisclosed | [Mercor Data] | [DeepSeek-V3.2] |\n| Synthetic Text-To-SQL | Text | 96,564 | [In-house Text-to-SQL data] | [gpt-oss-120b] |\n| Dialog Memory Vendor Data | Text | Undisclosed | [Patronus external vendor agreement] | Undisclosed |\n| Synthetic Indirect Prompt Injection from Nemotron Super v3 and Qwen3-Next-80B-A3B-Instruct | Text | Undisclosed | [In-house indirect prompt injection data] | [nvidia/nemotron-3-super-v3, qwen/qwen3-next-80b-a3b-instruct] |\n| Synthetic Malicious Code and Agentic Security | Text | Undisclosed | [In-house malicious-code / agentic-security data] | Undisclosed |\n| Synthetic Single-Step SWE Patch Selection | Text | Undisclosed | [SWE-Gym Dataset]; [SWE Bench Verified Benchmark] | [ground truth and task checks] |\n| Synthetic Natural Language Math Final Answers from Nemotron 5.5 | Text | Undisclosed | [AMC8, AMC10, and AIME problem sets hosted on Art of Problem Solving]; [Pile-StackExchange] | [nemotron 5.5] |\n| Synthetic Simple Math Prompts for Token Efficiency | Text | Undisclosed | [In-house simple math prompts] | Undisclosed |\n| Synthetic Abstention Data from Nemotron Super v3 (CRAG) | Text | Undisclosed | [CRAG] | [nvidia/nvidia/nemotron-3-super-v3] |\n| Synthetic Agentless SWE | Text | 242,536 | [SWE-Rebench-V2]; [SWEbench Training Set]; [R2E-Gym/R2E-Gym-Subset]; [SWE-Gym/SWE-Gym]; [SWE-Rebench] | [openai/gpt-oss-120b] |\n| Synthetic Agentless SWE from DeepSeek-R1-0528 | Text | 209,976 | [SWE-Bench-Train]; [SWE-Fixer-Train]; [SWE-reBench]; [SWE-Smith] | [deepseek-ai/DeepSeek-R1-0528] |\n| Synthetic Agentic CUDA Traces from GLM-4.7 | Text | 2,276 | [Internal CUDA task data] | [GLM-4.7] |\n| Synthetic Math Proofs from DeepSeek-V3.2-Speciale | Text | 820,772 | [Nemotron-Math-Proofs-v1] | [SDG: DeepSeek-V3.2-Speciale]; [Filter: proof validation] |\n| Synthetic Multilingual SFT from DeepSeek-V3 | Text | 1,245,284 | [Nano v3 SFT data] | [DeepSeek-V3] |\n| Synthetic Agentic Code from gpt-oss-120b | Text | 109,086 | [NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1] | [openai/gpt-oss-120b] |\n| Synthetic Agentic CLI and Web Skills from gpt-oss-120b | Text | 27,418 | [NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1] | [openai/gpt-oss-120b] |\n| Synthetic Agentic Coding from gpt-oss-120b | Text | 160,531 | [NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1] | [openai/gpt-oss-120b] |\n| Synthetic OpenCode Agentic Tasks from gpt-oss-120b | Text | 614,773 | [NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1] | [openai/gpt-oss-120b] |\n| Synthetic SWE Unverified | Text | Undisclosed | [NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1] | [gpt-oss-120b]; [Qwen/Qwen3-Coder-480B-A35B-Instruct]; [GLM-4.7-Flash] |\n| Synthetic ARC-AGI Ultra Data | Text | 192,016 | [ARC-AGI-2]; [arc dataset collection] | [ARC-AGI-2] |\n| Synthetic LiveCodeBench TIR from DeepSeek-R1-0528 | Text | 1,283,398 | [Nemotron-X training datasets] | [DeepSeek-R1-0528] |\n| Synthetic Verilog and SystemVerilog Code from DeepSeek-R1-0528 and GPT-OSS-120B | Text | 1,233,247 | [Verilog/SystemVerilog seed code] | [SDR: DeepSeek R1 0528 and GPT-OSS-120B]; [Filtering: Claude 4 Sonnet] |\n| Synthetic Aider Python Tasks from DeepSeek-R1-0528 | Text | 236,099 | [Exercism (GitHub Python)] | [Deepseek R1 0528] |\n| Synthetic Chat Reasoning-Off Data from GLM-5 | Text | 646,738 | [lmarena-ai/repochat-arena-preference-4k user prompts] | [Multi-turn conversations generated by GLM-5 with best-of-4 selection via Qwen3-Nemotron-235B-A22B-GenRM] |\n| Synthetic Chat Reasoning-On Data from GLM-5 | Text | 644,286 | [lmarena-ai/repochat-arena-preference-4k user prompts]; [lmarena-ai/arena-expert-5k user prompts]; [lmarena-ai/arena-human-preference-55k user prompts]; [lmarena-ai/arena-human-preference-100k user prompts]; [lmarena-ai/arena-human-preference-140k user prompts] | [Multi-turn conversations generated by GLM-5 with best-of-4 selection via Qwen3-Nemotron-235B-A22B-GenRM] |\n| Synthetic Multilingual Safety from Riva-Translate-4B-Instruct-v1.1 | Text | 132,067 | [Safety SFT Data: Ultra] | [nvidia/Riva-Translate-4B-Instruct-v1.1] |\n| Synthetic Science Reasoning Effort Medium | Text | 502,722 | [science-reasoning-effort-medium-v0] | Undisclosed |\n| Synthetic Telecom Tool-Use Trajectories from gpt-oss-120b | Text | 12,455 | [Existing Tau2 telecom trajectories originally generated with DeepSeek V3.2] | [gpt-oss-120b] |\n| Synthetic Terminal Bench Data from OpenReasoningv2 | Text | Undisclosed | [OpenCodeReasoningv2]; [OpenMathReasoning]; [nemo-swe-bench-repos]; [SWE-Rebench]; [SWE-Fixer-110K] | [OpenReasoningv2] |\n| Synthetic Tulu Instruction Following from DeepSeek-R1-0528 | Text | 105,361 | [Nemotron-X training datasets] | [DeepSeek-R1-0528] |\n| Synthetic Instruction Following from gpt-oss-120b | Text | 151,988 | [IFEval]; [IFEvalG] | [gpt-oss-120b] |\n| Synthetic Instruction Following for RL | Text | Undisclosed | [WildChat-1M]; [LMSYS-340B-Eval Dataset]; [LMSYS-Chat-1M Prompts]; [IFEval]; [IFEvalG] | [Qwen/Qwen3-235B-A22B-Thinking-2507]; [gpt-oss-120b]; [Qwen3-235B-A22B-Instruct-2507] |\n| Synthetic Identity Data from Qwen3-Next-80B-A3B-Instruct and Qwen3-235B-A22B-Instruct-2507 | Text | 25,992 | [Hand-written prompts] | [Qwen3-Next-80B-A3B-Instruct]; [Qwen3-235B-A22B-Instruct-2507] |\n| Synthetic Terminus Ultra Agentic Reasoning Blend | Text | 96,881 | [ARC-AGI-2]; [OpenCodeReasoningv2]; [OpenMathReasoning]; [SWE-Fixer-110K]; [SWE-Rebench]; [SWE-Smith] | [DeepSeek-V3.2]; [Qwen3-235B-A22B-Thinking-2507]; [Ring-1T]; [Kimi-K2.5]; [GLM-4.7-FP8]; [Qwen3-Next-80B-A3B-Thinking]; [gpt-oss-120b]; [Ministral-3-14B-Reasoning-2512]; [LM-4.5-Air-FP8] |\n| Synthetic STEM from Qwen3-235B-A22B-Thinking-2507 | Text | 1,174,694 | [IChO-IPhO-RL-v2]; [Physics-Big Dataset]; Scale HLE; [OpenMathReasoning]; [OpenCodeReasoning] | [Qwen3-235B-A22B-Thinking-2507] |\n| Synthetic STEM from Qwen3-235B-A22B-Instruct-2507 and gpt-oss-120b | Text | Undisclosed | [arXiv]; [National Institutes of Health ExPorter]; [BioRxiv]; [PMC Article]; [USPTO Backgrounds]; [peS2o]; Global Regulation; [CORE]; [PG-19]; [DOAB CC BY & CC BY-SA subset]; [NDLTD] | [Qwen3-235B-A22B-Instruct-2507]; [gpt-oss-120b] |\n| Translation Data from TAUS | Text | 1,618,055 | [TAUS proprietary dataset] | Undisclosed |\n| Synthetic Art of Problem Solving and Stack Exchange from gpt-oss-120b, Qwen2.5-32B-Instruct, and Goedel-Prover-V2-32B | Text | 860,469 | [Nemotron-Math-Proofs-v1] | [Goedel-Prover-V2-32B] |\n| Synthetic Art of Problem Solving and Stack Exchange from gpt-oss-120b | Text | 1,201,815 | [Upstream released math dataset]; [AoPS]; [StackOverflow / StackExchange] | [gpt-oss-120b] |\n| Synthetic Multilingual Science and Code data from DeepSeek-R1, DeepSeek-R1-0528, Qwen2.5-32B-Instruct, and Qwen3-235B-A22B, translated with Qwen2.5-32B-Instruct and Qwen2.5-14B-Instruct | Text | Undisclosed | [Nano-V3 SFT Data (without tool call)] | [Qwen/Qwen2.5-14B-Instruct]; [Qwen/Qwen3-4B-Thinking-2507] |\n| Synthetic Multilingual Science and Code data from DeepSeek-R1, DeepSeek-R1-0528, Qwen2.5-32B-Instruct, and Qwen3-235B-A22B, translated with Qwen2.5-32B-Instruct and Qwen2.5-14B-Instruct (Stack Exchange lineage) | Text | Undisclosed | [Stack Exchange]; [SCP-116K]; [LIMO]; [TACO]; Code Contest; Codeforces | [DeepSeek-R1]; [DeepSeek-R1-0528]; [Qwen2.5-32B-Instruct]; [Qwen3-235B-A22B] |\n| Synthetic Search Graph Walk | Text | 6,977 | [Wikidata / Wikipedia KnowledgeBase] | [MiniMaxAI/MiniMax-M2] |\n| Synthetic Agentic Diverse Domains | Text | 281,537 | [Handwritten prompts (synthetic; no external seed data used)] | [SDG model: deepseek-ai/DeepSeek-V3.2, deepseek-ai/DeepSeek-R1-0528, Qwen/Qwen3-235B-A22B-Thinking-2507, Qwen/Qwen3-32B]; [Filtering model: openai/gpt-oss-120b, Qwen/Qwen3-32B, Qwen/Qwen3-235B-A22B-Instruct-2507] |\n| Synthetic Long Context from Qwen3-235B-A22B-Instruct-2507 | Text | Undisclosed | [Long-context SFT seed blend (pre-training blend + nano-v1 post-training data)]; [Long-context SFT data: lc_nothink 256k, MRCR 200k, RULER 256k]; [AALCR seed blend: SEC Filings, CC, Wikipedia, FinePDFs, ArXiv, Pile-NIH ExPorter, BioRxiv, PMC Article, USPTO Backgrounds, peS2o, Global Regulations, CORE, Gutenberg (PG-19), DOAB CC-BY, NDLTD, Amps, StackExchange, MathPile, Numinas] | [Qwen/Qwen3-235B-A22B-Thinking-2507]; [deepseek-ai/DeepSeek-R1]; [Qwen3-30B-A3B] |\n| Synthetic Nemotron Math SFT from DeepSeek-V3.2-Speciale | Text | 1,900,553 | [Nemotron-Math-v2 (AOPS and StackExchange-math problems)] | [DeepSeek-V3.2-Speciale] |\n| Synthetic Nemotron Math TIR from DeepSeek-V3.2 | Text | 1,789,258 | [Nemotron-Math-v2 (AOPS and StackExchange-math problems)] | [DeepSeek-V3.2] |\n| Synthetic NemoCascade OCR Distillation from gpt-oss-120b | Text | 682,864 | [Nemotron-X training datasets] | [gpt-oss-120b] |\n| Synthetic CUDA 100k | Text | 93,086 | [KernelBook]; [HuggingFace Transformers]; [FlashInfer] | [gpt-oss-120b]; [DeepSeek-R1-0528] |\n| Synthetic Science MCQ and QA Diversity from GPT-OSS and Kimi-K2 | Text | 30,358 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] |\n| Synthetic Science HLE with Python from GPT-OSS and Kimi-K2 | Text | 85,184 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] |\n| Synthetic Science Search and Python from GPT-OSS and Kimi-K2 | Text | 6,179 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] |\n| Synthetic Science Search from GPT-OSS and Kimi-K2 | Text | 32,554 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] |\n| Synthetic Finance Reasoning from GPT-OSS-120B and Qwen3-235B-A22B-Instruct-2507 | Text | 326,700 | [SEC filings] | [GPT-OSS-120B, Qwen3-235B-A22B-Instruct-2507] |\n| Synthetic Science Diversity MCQ from GPT-OSS and Kimi-K2 | Text | 532,942 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] |\n| Synthetic Science Diversity OpenQ from GPT-OSS and Kimi-K2 | Text | 131,045 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] |\n| Synthetic Science Reasoning No-Tool from GPT-OSS and Kimi-K2 | Text | 2,085,600 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] |\n| Synthetic Text-To-SQL from gpt-oss-120b | Text | Undisclosed | [In-house Text-to-SQL data] | [gpt-oss-120b] |\n| Synthetic Tool Call Schema for RL (extended) | Text | 707,967 | [UltraTool]; [ToolEyes]; [AutoTools]; [API-Bank]; [Nemotron-Personas-USA]; [Salesforce xLAM function-calling]; [Glaive function-calling-v2]; [Agent-Ark/Toucan-1.5M] | [DeepSeek-V3.2]; [GLM-4.6]; [gpt-oss-120b]; [Kimi-K2-Instruct] |\n| Synthetic Safety from gemma-3-4b-it, Nemotron-Nano-9B-v2, and gpt-oss-120b | Text | 44,091 | [Safety SFT Data] | [google/gemma-3-4b-it]; [Nemotron-Nano-9B-v2]; [gpt-oss-120b] |\n| Synthetic Safety from DeepSeek-R1-0528, gpt-oss-120b, DeepSeek-R1-Distill-Qwen-7B, and Mixtral-8x7B-v0.1 | Text | Undisclosed | [Nemotron Content Safety Dataset V2]; [Gretel Synthetic Safety Alignment Dataset]; [RedTeam-2K]; [Malicious Tasks]; [Nemotron-Personas-USA] | [DeepSeek-R1-0528]; [gpt-oss-120b]; [DeepSeek-R1-Distill-Qwen-7B]; [Qwen3-30B-A3B-Thinking-2507]; [Qwen3-235B-A22B-Instruct-2507]; [Mixtral-8x7B-v0.1] |\n| Synthetic Tool Calling from Qwen3-235B-A22B-Thinking-2507 and Qwen3-Next-80B-A3B-Thinking | Text | Undisclosed | [ToolBench]; [glaive-function-calling-v2]; [APIGen Function-Calling]; [Nemotron-Personas-USA] | [Qwen3-235B-A22B-Thinking-2507]; [Qwen3-Next-80B-A3B-Thinking] |\n| Synthetic Chat from gpt-oss-120b, Mixtral-8x22B-Instruct-v0.1, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | [C4]; [LMSYS-Chat-1M]; [ShareGPT]; [GSM8K]; [PRM800K]; [FinQA]; [WikiTableQuestions]; [Riddles]; [glaive-function-calling-v2]; [SciBench]; [tigerbot-kaggle-leetcodesolutions-en-2k]; [OpenBookQA]; [Advanced Reasoning Benchmark]; Software Heritage; [Khan Academy Math Keywords]; [WildChat-1M]; [Nemotron-Personas-USA] | [gpt-oss-120b]; [Mixtral-8x22B-Instruct-v0.1]; [Qwen3-235B-A22B-Instruct-2507]; [Qwen3-235B-A22B-Thinking-2507] |\n| Synthetic Tool Use Interactive Agent from gpt-oss-120b, DeepSeek-R1-0528, Qwen3-32B, and Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | NVIDIA Internal | [gpt-oss-120b]; [DeepSeek-R1-0528]; [Qwen3-32B]; [Qwen3-235B-A22B-Thinking-2507] |\n| Synthetic DocFinQA and SWE-smith from Qwen3-Coder-480B-A35B-Instruct and Kimi-K2-Thinking | Text | Undisclosed | [DocFinQA]; [SWE-smith] | [Qwen3-Coder-480B-A35B-Instruct]; [Kimi-K2-Thinking] |\n| Synthetic SWE-Gym from Qwen3-Coder-480B-A35B-Instruct | Text | Undisclosed | [SWE-Gym] | [Qwen3-Coder-480B-A35B-Instruct] |\n| Synthetic SWE-Gym and R2E-Gym-Subset from Qwen3-Coder-480B-A35B-Instruct | Text | Undisclosed | [SWE-Gym]; [R2E-Gym-Subset] | [Qwen3-Coder-480B-A35B-Instruct] |\n| Synthetic SWE-Gym and R2E-Gym-Subset from DeepSeek-R1-0528 | Text | Undisclosed | [SWE-Gym]; [R2E-Gym-Subset] | [DeepSeek-R1-0528] |\n| Synthetic HelpSteer, LMSYS-Chat-1M, and Nemotron-Personas-USA from gpt-oss-120b, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | [HelpSteer2]; [HelpSteer3]; [LMSYS-Chat-1M]; [Nemotron-Personas-USA] | [gpt-oss-120b]; [Qwen3-235B-A22B-Instruct-2507]; [Qwen3-235B-A22B-Thinking-2507] |\n| Synthetic Nemotron-Personas-USA from gpt-oss-120b and Qwen3-8B | Text | Undisclosed | [Nemotron-Personas-USA] | [gpt-oss-120b]; [Qwen3-8B] |\n| Vendor Terminal Bench-like Tasks (Droid) | Text | Undisclosed | [Droid Harness Pivot vendor data] | Undisclosed |\n\n#### Language Distribution in Post-Training\n\nFor our post-training recipe, we focused on the following languages in addition to English: French, German, Italian, Japanese, Spanish, and Chinese. Those languages were represented in the form of multilingual reasoning and translation tasks.\n\n

\n\n## Testing Datasets\n\n**Data Collection Method by dataset**
\n* Hybrid: Automated, Manually-Collected, Synthetic\n\n**Labeling Method by dataset**
\n* Hybrid: Automated, Manually-Labeled, Synthetic\n\n**Properties:** This corpus comprises a mix of high-quality standard benchmarks and test suites for modern agentic AI. These benchmarks test model capabilities on tasks such as tool-calling and instruction following.\n\n## Evaluation Datasets\n\n**Data Collection Method by dataset**
\n* Hybrid: Automated, Manually-Collected, Synthetic\n\n**Labeling Method by dataset**
\n* Hybrid: Automated, Manually-Labeled, Synthetic\n\n**Properties:** This corpus comprises a mix of high-quality standard benchmarks and test suites for modern agentic AI. These benchmarks test model capabilities on tasks such as tool-calling and instruction following.\n\n## Inference\n\n* **Acceleration Engine:** PyTorch\n* **Test Hardware:**\n * NVIDIA Hopper\n - 1-8x H100\n - 1-8x H200\n * NVIDIA Blackwell\n - GB200\n - GeForce RTX 5090\n\n## Ethical Considerations\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nWe advise against circumvention of any provided safety guardrails contained in the Model without a substantially similar guardrail appropriate for your use case. For more details: [Safety](./safety.md) and [Explainability](./explainability.md) Subcards.\n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ [Bias](./bias.md), [Explainability](./explainability.md), [Safety & Security](./safety.md), and [Privacy](./privacy.md) Subcards.\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n## **Get Help**\n\n### NVIDIA Developer Community Forum\nGet access to community knowledge base articles and support cases (https://forums.developer.nvidia.com/)\n\n### Enterprise Support\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).","displayName","NVIDIA-Nemotron-3.5-Bolt-E4B-4GB-BF16",["D",1792689200882],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nim/nvidia/models/nemotron-3.5-bolt-e4b-4gb","rawImageUrl","","canGuestDownload","latestVersionSizeInBytes","licenseTerms",{"_274":275,"_276":16,"_277":278,"_279":20,"_280":281},"isMultinodeEnabled","isMultiArchitecture","bias","| Field | Response |\n| :---- | :---- |\n| Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None |\n| Bias Metric (If Measured): | [BBQ Accuracy Scores in Ambiguous Contexts](https://github.com/nyu-mll/BBQ/) |\n| Which characteristic (feature) show(s) the greatest difference in performance?: | The model shows high variance in the characteristics when it is used with a high temperature. |\n| Measures taken to mitigate against unwanted bias: | Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) employed to calibrate the model’s reasoning capabilities to maintain logical consistency and appropriate complexity when interacting with or interpreting data from diverse age demographics. |\n| If using internal data, description of methods implemented in data acquisition or processing, if any, to address the prevalence of identifiable biases in the training, testing, and validation data: | The training datasets contain a large amount of synthetic data generated by LLMs. We manually curated prompts. |\n| Tools used to assess statistical imbalances and highlight patterns that may introduce bias into AI models: | [BBQ](https://github.com/nyu-mll/BBQ/) |\n| Tools used to assess statistical imbalances and highlight patterns that may introduce bias into AI models: | These datasets, such as web-scraped finance reasoning data derived from SEC EDGAR filings, science and math problem datasets, OpenResearcher/source-document datasets, Common Crawl, CC-News, Wikimedia, and long-context document datasets, do not collectively or exhaustively represent all demographic groups (and proportionally therein). For instance, these datasets do not contain explicit mentions of demographic classes such as age, gender, or ethnicity in approximately 97% to 99.9% of finance reasoning samples and in over 85% of samples across the broader assessed datasets. In the subset where such terms are present, these datasets contain notable representational skews. For example, ethnicity mentions are often dominated by Middle Eastern contexts (found in finance documents) or \"White,\" \"Two or more,\" and \"Black or African American\" as the most frequent ethnic identifiers, while references categorized as male-only significantly outnumber those categorized as female-only. Furthermore, gender is explicitly mentioned in approximately 12% of samples across the broader dataset assessment, yet in only 0.9% of finance-specific samples. Dataset-level results vary by source type, with long-context/source-document datasets containing higher explicit demographic mention rates compared to certain web-scraped sources. To mitigate these imbalances, we recommend considering evaluation techniques such as bias audits, fine-tuning with demographically balanced datasets, and mitigation strategies such as counterfactual data augmentation to align with the desired model behavior. This evaluation used a 3,000-sample subset per dataset, identified as the optimal threshold for maximizing embedder accuracy. |\n| Unwanted Bias Testing: | Constrained to English-language inputs. Multi-lingual parity is not currently claimed or guaranteed. |","explainability","| Field | Response |\n| :---- | :---- |\n| Intended Task/Domain: | Text generation, reasoning, and chat |\n| Model Type: | Text-to-text Gemma-4 |\n| Intended Users: | Generative AI creators working with conversational AI models and image content. |\n| Output: | Text |\n| Tools used to evaluate datasets to identify synthetic data and ensure data authenticity. | We used a Gemma-3 4B-based filtering model fine-tuned on [Nemotron Content Safety Dataset v2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) to ensure the quality of synthetic data. |\n| Describe how the model works: | Generates text by predicting the next word or token based on the context provided in the input sequence using multiple self-attention layers. |\n| List the groups (or group characteristics) for which this was tested to produce comparable outcomes: | Age, Disability Status, Gender Identity, Nationality, Physical Appearance, Ethnicity, Socioeconomic Status, Sexual Orientation, Religion |\n| Technical Limitations & Mitigation: | This model performs particularly well in instruction following regimes, as such may be strongly influenced by untrusted inputs and should be paired with appropriate guardrails and data filtering to better align use-case behaviors when exposed to such data. |\n| Verified to have met prescribed NVIDIA quality standards: | Yes |\n| Performance Metrics: | Accuracy, Throughput, and User-side throughput |\n| Potential Known Risks: | The model was optimized explicitly for instruction following and as such is more susceptible to prompt injection and jailbreaking in various forms as a result of its instruction tuning. This means that the model should be paired with additional rails or system filtering to limit exposure to instructions from malicious sources -- either directly or indirectly by retrieval (e.g. via visiting a website) -- as they may yield outputs that can lead to harmful, system-level outcomes up to and including remote code execution in agentic systems when effective security controls including guardrails are not in place. The model may generate answers that may be inaccurate, omit key information, include irrelevant or redundant text, or produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. |\n| Licensing: | Use of this model is governed by the [OpenMDW License Agreement, version 1.1](https://github.com/OpenMDW/OpenMDW/blob/main/1.1/LICENSE.OpenMDW-1.1). This model has been post-trained on the Google Gemma 4 model, and use of the Google Gemma 4 model is governed by [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) |","privacy","| Field | Response |\n| :---- | :---- |\n| Generatable or reverse engineerable personal data\\*? | No |\n| Personal data used to create this model? | No |\n| Was consent obtained for any personal data\\* used? | Not Applicable |\n| (For GPAI Models): A description of any methods implemented in data acquisition or processing, if any, to address the prevalence of personal data in the training data, where relevant and applicable. | We used only prompts that do not contain any personal data for synthetic data generation. |\n| How often is dataset reviewed? | Before Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable |\n| If personal data was collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable |\n| If personal data was collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable |\n| If personal data was collected for the development of this AI model, was it minimized to only what was required? | Not Applicable |\n| Was data from user interactions with the AI model (e.g. user input and prompts) used to train the model? | \\[Yes/No\\] |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data. |\n| Applicable Privacy Policy | [https://www.nvidia.com/en-us/about-nvidia/privacy-policy/](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) |","safetyAndSecurity","| Field | Response |\n| :---- | :---- |\n| Model Application Field(s): | Chat, Instruction Following, Chatbot Development, Code Generation, Reasoning, Customer Service |\n| Describe the life critical impact (if present). | Not Applicable |\n| Description of methods implemented in data acquisition or processing, if any, to address other types of potentially harmful data in the training, testing, and validation data: | We used a guard model for content safety to exclude potentially harmful data from training. |\n| Description of any methods implemented in data acquisition or processing, if any, to address illegal or harmful content in the training data, including, but not limited to, child sexual abuse material (CSAM) and non-consensual intimate imagery (NCII) | We used a Gemma-3 4B-based guard model trained on [Nemotron Content Safety Dataset v2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) for content safety to exclude potentially illegal or harmful content from the training. |\n| Use Case Restrictions: | Use of this model is governed by the [OpenMDW License Agreement, version 1.1](https://github.com/OpenMDW/OpenMDW/blob/main/1.1/LICENSE.OpenMDW-1.1). This model has been post-trained on the Google Gemma 4 model, and use of the Google Gemma 4 model is governed by [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)|\n| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |\n| This AI model was developed based on our policies to ensure responsible data handling and risk mitigation. The datasets used for training have been scanned for harmful content and illegal content, consistent with our policies including scanning for Child Sexual Abuse Material (CSAM). Ongoing review and monitoring mechanisms are in place based on our policies and to maintain data integrity. | True. We use [Nemotron Content Safety Dataset V2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) and an internal safety dataset specialized for minority sexuality for content safety evaluation to ensure the safety of this model. |","featureLabels",[268],"displayLabels",[256,257,258],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_218":266,"_260":266,"_261":267,"_74":80},{"_218":264,"_260":264,"_261":265,"_74":80},{"_218":259,"_260":259,"_261":262,"_74":80},"NSPECT-B8OB-2WO9","value","filter",{"_263":80,"_260":259},"field","ingested",{"_263":80,"_260":264},"False",{"_263":80,"_260":266},{"_218":269,"_260":270,"_261":271,"_74":272},"NVIDIA NIM","nimmcro_nvidia_nim",{"_263":273,"_260":270},"feature","nvidia_nim","governingTerms","

GOVERNING DOWNLOAD TERMS: Use of the model is governed by the OpenMDW License Agreement, version 1.1.

ADDITIONAL INFORMATION: Apache License, Version 2.0.

","licenseId","licenseVersion","v1.0","needsAcceptance","configuredLicenseTerms",{"_282":283,"_284":285},"hosting",{"_286":287,"_67":291,"_65":292},"download",{"_286":287,"_67":288,"_65":66},"artifactType","model",[289,290],{"_72":82,"_74":83,"_76":84,"_78":85,"_80":86},{"_72":73,"_74":75,"_76":77,"_78":79,"_80":81},[293,294],"

GOVERNING HOSTING TERMS: Use of the model is governed by the OpenMDW License Agreement, version 1.1.

ADDITIONAL INFORMATION: Apache License, Version 2.0.

",{"_72":82,"_74":83,"_76":84,"_78":85,"_80":86},{"_72":73,"_74":75,"_76":77,"_78":79,"_80":81},{"_107":1426,"_109":1427},"ngcApps",[1379],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1360":1361,"_1362":1363,"_1364":1365,"_1366":1367,"_1368":306,"_139":332,"_1369":1370,"_1371":50,"_1372":18,"_1373":18,"_1374":1375,"_1376":18,"_1377":1378},"amplitude",{"_1357":1358,"_967":-7,"_1359":20},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1351":1352,"_1353":1354,"_1355":1356},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1793032943820,"1.28.1","defaultSearchSuggestions",{"_1237":229,"_1238":1239,"_1240":1241,"_1242":183},"announcements",[],"consentState",{"_369":1227,"_1228":20,"_1229":20,"_1230":18,"_1231":1232},"consentConfig",{"_351":352,"_353":354,"_355":356,"_357":358,"_359":360,"_361":362,"_363":364,"_365":366,"_367":368,"_369":370},"showContactAdmin",1793032943821,[],[],[346,347,348,349,350],{"_39":54,"_41":63,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":61,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":55,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":60,"_45":-5,"_47":-5,"_49":50},{"_39":40,"_41":42,"_43":44,"_45":46,"_47":53,"_49":50},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. 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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","omniverse.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. 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