[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10,"_11":12},"actionData","errors","root",{"_213":214,"_215":216,"_217":218,"_219":220,"_221":222,"_223":20,"_224":225,"_226":227,"_228":20,"_229":20,"_230":20,"_231":20,"_232":233,"_234":-5,"_235":236,"_237":238,"_239":240,"_241":242,"_243":244,"_245":246,"_247":-5,"_248":20,"_249":-5,"_250":251,"_252":253,"_80":254,"_255":256,"_257":258,"_259":260,"_261":262,"_263":20,"_15":16,"_17":264,"_19":20,"_21":265,"_23":266,"_25":267},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/route",{"_66":67,"_13":68,"_69":70,"_71":72,"_73":20,"_74":75,"_76":20,"_77":20,"_78":79,"_80":81,"_82":-5,"_83":84,"_85":-5,"_86":87,"_88":89,"_90":20,"_91":92,"_15":16,"_17":93,"_19":20,"_21":94,"_23":95,"_25":96},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/model-card/route",{"_15":16,"_17":59,"_19":20,"_21":60,"_23":61,"_25":62},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/model-card/explainability/route",{"_13":14,"_15":16,"_17":18,"_19":20,"_21":22,"_23":24,"_25":26},"html","\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Field	Response
Participation considerations from adversely impacted groups protected classes in model design and testing:	None
Measures taken to mitigate against unwanted bias:	None
Bias Metric (If Measured):	BBQ Accuracy Scores in Ambiguous Contexts

","traceId","b707fcfcdd20ff7b6784841b8ed85068","traceStartTime",1788883265320,"isStaleResponse",false,"notifications",[],"toasts",[],"apiCalls",[27,28,29,30,31,32],{"_33":48,"_35":57,"_37":50,"_39":51,"_41":58,"_43":44},{"_33":48,"_35":55,"_37":50,"_39":51,"_41":56,"_43":44},{"_33":48,"_35":53,"_37":50,"_39":51,"_41":54,"_43":44},{"_33":48,"_35":49,"_37":50,"_39":51,"_41":52,"_43":44},{"_33":45,"_35":46,"_37":38,"_39":40,"_41":47,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44},"apiTag","subscription-api","path","/v2/users/me/subscriptions","status","error","httpStatus",401,"durationMs",3,"retryCount",0,"oms-api","/v2/users/me",5,"models-api","/v2/models/org/nim/team/nvidia/nemotron-labs-voicechat/collections?page-number=0&page-size=1","success",200,61,"/v2/models/nim/nvidia/nemotron-labs-voicechat/versions/1.0.0",55,"/v2/models/nim/nvidia/nemotron-labs-voicechat/versions?page-size=100&page-number=0&sort-order=CREATED_DATE_DESC",80,"/v2/models/nim/nvidia/nemotron-labs-voicechat?resolve-labels=true&remove-unresolved-labels=true",237,1788883265064,[],[],[63,64],{"_33":45,"_35":46,"_37":65,"_39":-5,"_41":-5,"_43":44},{"_33":34,"_35":36,"_37":65,"_39":-5,"_41":-5,"_43":44},"pending","resource",{"_139":144,"_145":146,"_147":148,"_149":150,"_103":104,"_105":106,"_151":152,"_110":153,"_154":155,"_116":156,"_157":158,"_159":81,"_160":-7,"_161":-7,"_162":163,"_164":165,"_166":108,"_167":119,"_168":-5,"_169":-7,"_170":-7,"_171":111,"_172":173,"_174":175,"_176":177,"_178":179,"_180":181,"_182":183,"_184":185,"_186":187,"_188":20},"

Model Overview

Description:

NVIDIA NemotronLabs VoiceChat is a 11B end-to-end, real-time speech full duplex (FD) model for conversational AI that jointly performs streaming speech understanding and speech generation [1]. Unlike traditional cascaded stacks (ASR → LLM → TTS), this model achieves full duplex, real-time, seamless voice interaction in one unified architecture, eliminating the need for multiple models or API handoffs, thus reducing end-to-end latency.\nIt sets new benchmarks by bringing open, robust, and highly natural conversation capabilities. Moreover, NVIDIA NemotronLabs VoiceChat is the first open full-duplex model to support tool calling while maintaining a natural conversation flow during tool execution. For each tool a specific “on-hold” message can be defined that will be spoken by the agent as soon as the LLM generates the text that will trigger the tool call and response.

The model operates on audio signals, which are encoded using a fast conformer module. The resulting audio tokens are inputted into a Nemotron Nano V2 9B LLM backbone to predict text tokens, which are fed to a TTS decoder [2] to predict audio codes for generating the agent's speech. A separate output channel is used to predict tool calling scripts.\nNemotronLabs VoiceChat offers an unprecedented trade-off between intelligence and latency in the space of open-source voice agents, as highlighted by our benchmarking results below.

\n
This model is ready for research purposes only.
\n

Highlights

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

tr:nth-child(odd)]:bg-surface-sunken\">

Parameters	Turn-taking latency	VoiceBench (open FD)	Tool calling	Architecture
11B	~450 ms	#2	1st open FD	1 unified model

License/Terms of Use

GOVERNING TERMS: The use of this model is governed by the OpenMDW License Agreement, version 1.1.

Use Case:

NemotronLabs VoiceChat is targeted for researchers, developers, and professionals in the field of natural language processing (NLP) and speech technology for purposes such as automatic speech recognition (ASR), text-to-speech synthesis (TTS), and voice assistant development.

Deployment Geography

Global

Release Date:

July 16, 2026.

Model Architecture

Architecture Type: Hybrid Mamba/Transformer · Parameters: 11B

Network Architecture:

NemotronLabs VoiceChat uses:

Fast Conformer Speech Encoder from Nemotron-Speech-Streaming-En-0.6b
NVIDIA Nemotron Nano v2 LLM backbone
NVIDIA TTS decoder and codec [2]
Separate output channel for tool-calling scripts

Input

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

tr:nth-child(odd)]:bg-surface-sunken\">

Property	Value
Input Type(s)	Text (prompt), Audio (user speech)
Input Format	String, WAV/WebAudio
Sample rate	16 kHz

Output

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

tr:nth-child(odd)]:bg-surface-sunken\">

Property	Value
Output Type(s)	Text (agent), Audio (agent speech), Text (user transcription)
Output Format	String, WAV/WebAudio
Sample rate	22.05 kHz

How NemotronLabs VoiceChat Compares to Other Open FD Models

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

tr:nth-child(odd)]:bg-surface-sunken\">

Model	Params	Tool calling	Big Bench Audio
NemotronLabs VoiceChat	11B	✔	37.0%
PersonaPlex 7B	7B	✘	19.1%
Moshi	7B	✘	4.4%
Freeze-Omni	7B	✘	33.4%

Software Integration

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.

Runtime Engine: vLLM

Supported Hardware Microarchitecture Compatibility:

NVIDIA A100
NVIDIA H100
NVIDIA B200
NVIDIA RTX-6000

Preferred/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)

v1.0

Quickstart

A tutorial is under preparation and will be shared shortly. Visit back soon!

Training, Testing, and Evaluation Datasets:

Training Dataset:

Data Modality: Audio (speech) and Text
\nAudio Training Data Size: ~550k hours

VoiceChat has been trained on a blend of different datasets comprising both real audio datasets and synthetic speech datasets generated using various TTS systems on text training corpora, including:

Nemotron 5.5 pre-training and SFT text data
Brainy-mantis text data
Greteal AI v1 and v2 text data
Ultrachat text data
Blackwell studio recordings real speech data
Fisher real speech data
LibriVox
LibriTTS
HiFi-TTS
Riva Speakers: Internal Dataset
Publicly available internet scale data
PromptTTS
VCTK
Voxmovies
JL-Corpus
Nemotron Nano v3 function calling data
Persona Plex training datasets

Data Collection Method by dataset: Hybrid: Human, Synthetic, Automated.
\nLabeling Method by dataset: Automated.

Testing/Evaluation Dataset:

VoiceBench

VoiceBench is a benchmark dataset developed to evaluate large language model (LLM)-based voice assistants, focusing on real-world spoken interactions rather than just text or clean speech recognition. It combines audio and text data. The dataset includes multiple subsets covering tasks like open-ended questions, multiple-choice QA, instruction following, and adversarial cases — sourced from both real human speech and synthetic text-to-speech examples.

Link: VoiceBench
\nData Collection Method by dataset: Hybrid: Human, Synthetic, Automated.
\nLabeling Method by dataset: Automated.
\nProperties: VoiceChat is #2 amongst all open full-duplex models on VoiceBench.

Benchmark Scores:

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

tr:nth-child(odd)]:bg-surface-sunken\">

Metric	Value
Text-output average accuracy	55.1

Artificial Analysis Big Bench Audio

Big Bench Audio is a benchmark dataset used to evaluate the reasoning capabilities of audio-language and native speech-to-speech models. It adapts 1,000 questions from Big Bench Hard into audio format, testing whether models can understand spoken prompts and solve reasoning tasks directly from audio rather than relying only on text input. The benchmark is used by Artificial Analysis to compare speech models on audio reasoning performance across challenging question-answering tasks.

Link: Artificial Analysis Big Bench Audio
\nData Collection Method by dataset: Hybrid: Synthetic, Automated.
\nLabeling Method by dataset: Automated.
\nProperties: TODO

Benchmark Scores:

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

tr:nth-child(odd)]:bg-surface-sunken\">

Metric	Value
Text-output average accuracy	37.0%

Full-Duplex-Bench 1.0

Full-Duplex-Bench 1.0 is a benchmark designed to evaluate the interactive capabilities of full-duplex spoken dialogue models like VoiceChat. It focuses on measuring natural, human-like conversational behaviors such as pause handling, backchanneling, smooth turn-taking, and user interruption management, using automatic metrics to provide consistent, reproducible assessments of model performance.

Link: Full-Duplex-Bench 1.0
\nData Collection Method by dataset: Hybrid: Human, Synthetic, Automated.
\nLabeling Method by dataset: Automated.
\nProperties: NemotronLabs VoiceChat is #2 amongst all open models on FullDuplexBench 1.0.

Benchmark Scores:

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

tr:nth-child(odd)]:bg-surface-sunken\">

Metric	Value
Pause Handling(Synthetic): TOR↓	0.153
Pause Handling(Candor): TOR↓	0.255
Smooth Turn Taking: TOR↑	0.82
Smooth Turn Taking: Latency↓	448 ms
User Interruption: TOR↑	1
User Interruption: Latency↓	480 ms
User Interruption: GPT-4o↑	4.33

AU Harness BFCL-v3 (tool calling)

This is the subset of the AU Harness benchmark focused on the evaluation of tool calling within audio conversation. It was created by systematically converting the original BFCL-v3 textual instructions into spoken counterparts.

Link: AU Harness
\nData Collection Method by dataset: Hybrid: Human, Synthetic, Automated.
\nProperties: NemotronLabs VoiceChat is the first open full-duplex model to support tool calling while maintaining a natural conversation flow during tool execution.

Benchmark Scores:

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

tr:nth-child(odd)]:bg-surface-sunken\">

Metric	Value
Simple	58.5%
Multiple	62.5%
Parallel	42.5%
Parallel Multiple	27.5%
Irrelevance	89.6%
Average	56.1%

Full-Duplex-Bench v3 (tool calling)

Full-Duplex-Bench-v3 (FDB-v3) is a benchmark for evaluating spoken language models under naturalistic speech conditions and multi-step tool use.

Link: Full-Duplex-Bench v3
\nData Collection Method by dataset: Human.
\nProperties: NemotronLabs VoiceChat is competitive with frontier models in terms of tool selection accuracy on this benchmark.

Benchmark Scores:

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

tr:nth-child(odd)]:bg-surface-sunken\">

Metric	Value
Tool Selection	82.5%
Argument accuracy	44.2%
Pass@1	33%

Inference:

Acceleration Engine: vLLM
\nTest Hardware: NVIDIA H100

Known Limitations

NemotronLabs VoiceChat is trained with no more than 2-min long audio context windows. Conversational context beyond this window may not be retained reliably.

NemotronLabs VoiceChat has been optimized to achieve a trade-off between general knowledge and natural conversation, hence it may not be as performant as the LLM backbone it is derived from, namely NVIDIA Nemotron Nano v2, in terms of knowledge, instruction-following capabilities and safety.

The model can make reasoning errors or provide incorrect/incomplete information NemotronLabs VoiceChat was not explicitly trained for reasoning or alignment. Its performance on tasks requiring multi-step reasoning, arithmetic, or safety-aligned behavior may therefore be limited.

Further, the model may not yet be able to systematically handle user backchannelling.

As far as tool calling is concerned, we recommend a maximum of 5 tools per session; more tools may degrade performance. Also, the model cannot yet reliably call multiple tools simultaneously. Long tool responses may cause delays before the agent speaks (mitigate with on-hold messages). The user cannot interrupt the agent during tool calling execution. Further, in mixed conversations (general chat + tool requests), the model may answer from its own knowledge instead of calling the appropriate tool.

Finally, the model is not suitable for noisy or highly reverberant environments, especially where background speech may occur.

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, 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.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

References:

[1] SALM-Duplex: Efficient and Direct Duplex Modeling for Speech-to-Speech Language Model
\n[2] Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models
\n[3] PersonaPlex: Voice and role control for full duplex conversational speech models

Get Help

NVIDIA Developer Community Forum

Get access to community knowledge base articles and support cases (https://forums.developer.nvidia.com/)

","resourceVersions",[137,138],"resourceVersionsTotal",1,"hasMoreVersions","versionMetadata",{"_109":81,"_110":111,"_37":112,"_113":-5,"_114":115,"_116":117,"_118":119,"_120":121,"_122":108,"_123":-5,"_124":20,"_125":-5,"_126":20,"_127":-5,"_128":20,"_129":-5,"_130":20,"_131":-5,"_132":20,"_133":-5,"_134":20,"_135":136},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nim/nvidia/models/nemotron-labs-voicechat/-","version","1.0.0","imageReference","cliDownloadCommand","ngc registry model download-version nim/nvidia/nemotron-labs-voicechat:1.0.0","helmFetchCommand","viewAllVersionsPath","/orgs/nim/nvidia/models/nemotron-labs-voicechat/1.0.0/version-history","accessCheckResult",{"_107":108},"hasRelatedCollections","context",{"_103":104,"_105":106},1788883265317,[],[],[97,98,99,100,101,102],{"_33":48,"_35":57,"_37":50,"_39":51,"_41":58,"_43":44},{"_33":48,"_35":55,"_37":50,"_39":51,"_41":56,"_43":44},{"_33":48,"_35":53,"_37":50,"_39":51,"_41":54,"_43":44},{"_33":48,"_35":49,"_37":50,"_39":51,"_41":52,"_43":44},{"_33":45,"_35":46,"_37":38,"_39":40,"_41":47,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44},"orgName","nim","teamName","nvidia","allowed",true,"versionId","description","","UPLOAD_COMPLETE","releaseType","createdDate",["D",1785865553595],"updatedDate",["D",1785865578823],"totalSizeInBytes",46913192085,"totalFileCount",23,"isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_139":142,"_140":143,"_113":-5,"_122":108,"_124":20,"_135":141,"_126":20},{"_139":81,"_140":81,"_113":-5,"_122":108,"_124":20,"_135":141,"_126":20},"id","name",[],"-","Latest (1.0.0)","nim/nvidia/nemotron-labs-voicechat","key","nim/nvidia/models/nemotron-labs-voicechat","resourceType","models","resourceId","nemotron-labs-voicechat","shortDescription","Real-time Speech to Speech full duplex model for conversational AI","# Model Overview\n\n## Description:\nNVIDIA NemotronLabs VoiceChat is a **11B end-to-end, real-time speech full duplex (FD)** model for conversational AI that jointly performs streaming speech understanding and speech generation [1]. Unlike traditional cascaded stacks (ASR → LLM → TTS), this model achieves full duplex, real-time, seamless voice interaction in one unified architecture, eliminating the need for multiple models or API handoffs, thus reducing end-to-end latency. \nIt sets new benchmarks by bringing open, robust, and highly natural conversation capabilities. Moreover, NVIDIA NemotronLabs VoiceChat is **the first open full-duplex model to support tool calling** while maintaining a natural conversation flow during tool execution. For each tool a specific “on-hold” message can be defined that will be spoken by the agent as soon as the LLM generates the text that will trigger the tool call and response. \n\nThe model operates on audio signals, which are encoded using a fast conformer module. The resulting audio tokens are inputted into a Nemotron Nano V2 9B LLM backbone to predict text tokens, which are fed to a TTS decoder [2] to predict audio codes for generating the agent's speech. A separate output channel is used to predict tool calling scripts. \nNemotronLabs VoiceChat offers an unprecedented trade-off between intelligence and latency in the space of open-source voice agents, as highlighted by our benchmarking results [below](#testingevaluation-dataset).\n\n> This model is ready for research purposes only.\n\n### Highlights\n\n| Parameters | Turn-taking latency | VoiceBench (open FD) | Tool calling | Architecture |\n|---|---|---|---|---|\n| 11B | ~450 ms | #2 | 1st open FD | 1 unified model |\n\n### License/Terms of Use\nGOVERNING TERMS: The 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).\n\n### Use Case:
\nNemotronLabs VoiceChat is targeted for researchers, developers, and professionals in the field of natural language processing (NLP) and speech technology for purposes such as automatic speech recognition (ASR), text-to-speech synthesis (TTS), and voice assistant development.\n\n### Deployment Geography\nGlobal\n\n### Release Date:
\nJuly 16, 2026.\n\n\n## Model Architecture\n**Architecture Type:** Hybrid Mamba/Transformer · **Parameters:** 11B\n\n**Network Architecture:**
\n\nNemotronLabs VoiceChat uses:\n* Fast Conformer Speech Encoder from [Nemotron-Speech-Streaming-En-0.6b](https://huggingface.co/nvidia/nemotron-speech-streaming-en-0.6b)\n* [NVIDIA Nemotron Nano v2 LLM backbone](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2)\n* NVIDIA TTS decoder and codec [2]\n* Separate output channel for tool-calling scripts\n\n### Input\n\n| Property | Value |\n|---|---|\n| Input Type(s) | Text (prompt), Audio (user speech) |\n| Input Format | String, WAV/WebAudio |\n| Sample rate | 16 kHz |\n\n### Output\n\n| Property | Value |\n|---|---|\n| Output Type(s) | Text (agent), Audio (agent speech), Text (user transcription) |\n| Output Format | String, WAV/WebAudio |\n| Sample rate | 22.05 kHz |\n\n## How NemotronLabs VoiceChat Compares to Other Open FD Models\n\n\n| Model | Params | Tool calling | Big Bench Audio |\n|---|---|---|---|\n| **NemotronLabs VoiceChat** | 11B | ✔ | 37.0% | \n| PersonaPlex 7B | 7B | ✘ | 19.1% |\n| Moshi | 7B | ✘ | 4.4% |\n| Freeze-Omni | 7B | ✘ | 33.4% |\n\n\n## Software Integration\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**Runtime Engine:** vLLM
\n\n**Supported Hardware Microarchitecture Compatibility:**
\n\n* NVIDIA A100\n* NVIDIA H100\n* NVIDIA B200\n* NVIDIA RTX-6000\n\n**Preferred/Supported Operating System(s):**\n* 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* v1.0\n\n\n## Quickstart\n\nA tutorial is under preparation and will be shared shortly. Visit back soon!\n\n\n## Training, Testing, and Evaluation Datasets:\n\n### Training Dataset:\n\n**Data Modality:** Audio (speech) and Text
\n**Audio Training Data Size:** ~550k hours
\n\nVoiceChat has been trained on a blend of different datasets comprising both real audio datasets and synthetic speech datasets generated using various TTS systems on text training corpora, including:\n\n- Nemotron 5.5 pre-training and SFT text data\n- Brainy-mantis text data\n- Greteal AI v1 and v2 text data\n- [Ultrachat text data](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)\n- Blackwell studio recordings real speech data\n- [Fisher real speech data](https://catalog.ldc.upenn.edu/LDC2004S13)\n- [LibriVox](https://librivox.org/)\n- [LibriTTS](https://www.openslr.org/60/)\n- [HiFi-TTS](https://www.openslr.org/109/)\n- Riva Speakers: Internal Dataset\n- Publicly available internet scale data\n- [PromptTTS](https://speechresearch.github.io/prompttts/)\n- [VCTK](https://datashare.ed.ac.uk/handle/10283/3443)\n- [Voxmovies](https://mmai.io/datasets/voxmovies/)\n- [JL-Corpus](https://huggingface.co/datasets/CLAPv2/JL-Corpus)\n- Nemotron Nano v3 function calling data\n- Persona Plex training datasets\n\n**Data Collection Method by dataset:** Hybrid: Human, Synthetic, Automated.
\n**Labeling Method by dataset:** Automated.
\n\n### Testing/Evaluation Dataset:\n\n#### VoiceBench\nVoiceBench is a benchmark dataset developed to evaluate large language model (LLM)-based voice assistants, focusing on real-world spoken interactions rather than just text or clean speech recognition. It combines audio and text data. The dataset includes multiple subsets covering tasks like open-ended questions, multiple-choice QA, instruction following, and adversarial cases — sourced from both real human speech and synthetic text-to-speech examples.\n\n**Link:** [VoiceBench](https://github.com/MatthewCYM/VoiceBench)
\n**Data Collection Method by dataset:** Hybrid: Human, Synthetic, Automated.
\n**Labeling Method by dataset:** Automated.
\n**Properties:** VoiceChat is #2 amongst all open full-duplex models on [VoiceBench](https://arxiv.org/abs/2410.17196).
\n\n\n**Benchmark Scores:** \n\n| Metric | Value |\n|------------------------------------------|-------|\n| Text-output average accuracy | 55.1 |\n\n\n#### Artificial Analysis Big Bench Audio\nBig Bench Audio is a benchmark dataset used to evaluate the reasoning capabilities of audio-language and native speech-to-speech models. It adapts 1,000 questions from Big Bench Hard into audio format, testing whether models can understand spoken prompts and solve reasoning tasks directly from audio rather than relying only on text input. The benchmark is used by Artificial Analysis to compare speech models on audio reasoning performance across challenging question-answering tasks.\n\n**Link:** [Artificial Analysis Big Bench Audio](https://artificialanalysis.ai/speech-to-speech?utm_source=chatgpt.com)
\n**Data Collection Method by dataset:** Hybrid: Synthetic, Automated.
\n**Labeling Method by dataset:** Automated.
\n**Properties:** TODO
\n\n\n**Benchmark Scores:** \n\n| Metric | Value |\n|------------------------------------------|-------|\n| Text-output average accuracy | 37.0% |\n\n\n#### Full-Duplex-Bench 1.0\nFull-Duplex-Bench 1.0 is a benchmark designed to evaluate the interactive capabilities of full-duplex spoken dialogue models like VoiceChat. It focuses on measuring natural, human-like conversational behaviors such as pause handling, backchanneling, smooth turn-taking, and user interruption management, using automatic metrics to provide consistent, reproducible assessments of model performance.\n\n\n**Link:** [Full-Duplex-Bench 1.0](https://arxiv.org/abs/2503.04721)
\n**Data Collection Method by dataset:** Hybrid: Human, Synthetic, Automated.
\n**Labeling Method by dataset:** Automated.
\n**Properties:** NemotronLabs VoiceChat is #2 amongst all open models on [FullDuplexBench 1.0](https://arxiv.org/abs/2503.04721).
\n\n\n**Benchmark Scores:**\n\n| Metric | Value |\n|------------------------------------------|-------|\n| Pause Handling(Synthetic): TOR↓ | 0.153 |\n| Pause Handling(Candor): TOR↓ | 0.255 |\n| Smooth Turn Taking: TOR↑ | 0.82 |\n| Smooth Turn Taking: Latency↓ | 448 ms |\n| User Interruption: TOR↑ | 1 |\n| User Interruption: Latency↓ | 480 ms |\n| User Interruption: GPT-4o↑ | 4.33 |\n\n\n#### AU Harness BFCL-v3 (tool calling)\nThis is the subset of the [AU Harness benchmark](https://github.com/ServiceNow/AU-Harness) focused on the evaluation of tool calling within audio conversation. It was created by systematically converting the original [BFCL-v3](https://openreview.net/pdf?id=2GmDdhBdDk) textual instructions into spoken counterparts. \n\n\n**Link:** [AU Harness](https://github.com/ServiceNow/AU-Harness)
\n**Data Collection Method by dataset:** Hybrid: Human, Synthetic, Automated.
\n**Properties:** NemotronLabs VoiceChat is the first open full-duplex model to support tool calling while maintaining a natural conversation flow during tool execution.
\n\n\n**Benchmark Scores:** \n\n| Metric | Value |\n|--------------------|-------|\n| Simple | 58.5% |\n| Multiple | 62.5% |\n| Parallel | 42.5% |\n| Parallel Multiple | 27.5% |\n| Irrelevance | 89.6% |\n| Average | 56.1% |\n\n\n#### Full-Duplex-Bench v3 (tool calling)\n[Full-Duplex-Bench-v3 (FDB-v3)](https://arxiv.org/abs/2604.04847) is a benchmark for evaluating spoken language models under naturalistic speech conditions and multi-step tool use. \n\n**Link:** [Full-Duplex-Bench v3](https://daniellin94144.github.io/FDB-v3-demo/)
\n**Data Collection Method by dataset:** Human.
\n**Properties:** NemotronLabs VoiceChat is competitive with frontier models in terms of tool selection accuracy on this benchmark.
\n\n\n**Benchmark Scores:** \n\n| Metric | Value |\n|--------------------|-------|\n| Tool Selection | 82.5% |\n| Argument accuracy | 44.2% |\n| Pass@1 | 33% |\n\n\n# Inference:\n**Acceleration Engine:** vLLM
\n**Test Hardware:** NVIDIA H100
\n\n## Known Limitations\n\nNemotronLabs VoiceChat is trained with no more than 2-min long audio context windows. Conversational context beyond this window may not be retained reliably.\n\nNemotronLabs VoiceChat has been optimized to achieve a trade-off between general knowledge and natural conversation, hence it may not be as performant as the LLM backbone it is derived from, namely NVIDIA Nemotron Nano v2, in terms of knowledge, instruction-following capabilities and safety.\n\nThe model can make reasoning errors or provide incorrect/incomplete information NemotronLabs VoiceChat was not explicitly trained for reasoning or alignment. Its performance on tasks requiring multi-step reasoning, arithmetic, or safety-aligned behavior may therefore be limited.\n\nFurther, the model may not yet be able to systematically handle user backchannelling.\n\nAs far as tool calling is concerned, we recommend a maximum of 5 tools per session; more tools may degrade performance. Also, the model cannot yet reliably call multiple tools simultaneously. Long tool responses may cause delays before the agent speaks (mitigate with on-hold messages). The user cannot interrupt the agent during tool calling execution. Further, in mixed conversations (general chat + tool requests), the model may answer from its own knowledge instead of calling the appropriate tool.\n\nFinally, the model is not suitable for noisy or highly reverberant environments, especially where background speech may occur.\n\n## Ethical Considerations\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. When downloaded or used in accordance with our terms of service, 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\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n\n## References:\n[1] [SALM-Duplex: Efficient and Direct Duplex Modeling for Speech-to-Speech Language Model](https://arxiv.org/abs/2505.15670)
\n[2] [Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models](https://arxiv.org/abs/2507.08128)
\n[3] [PersonaPlex: Voice and role control for full duplex conversational speech models](https://research.nvidia.com/labs/adlr/files/personaplex/personaplex_preprint.pdf)
\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/)","displayName","NemotronLabs Voicechat",["D",1785865578827],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nim/nvidia/models/nemotron-labs-voicechat","rawImageUrl","https://assets.ngc.nvidia.com/products/api-catalog/images/nemotron-voice-agent.jpg","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------\nParticipation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None\nMeasures taken to mitigate against unwanted bias: | None\nBias Metric (If Measured): | [BBQ Accuracy Scores in Ambiguous Contexts](https://github.com/nyu-mll/BBQ/)","privacy","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Task/Domain: | Speech To Speech Conversational Chat Agent \nModel Type: | Speech Encoder (Fast Conformer), Nemotron Nano v2 LLM backbone (hybrid Mamba/Transformer), EAR TTS Speech Decoder (ConvNet, Transformer)\nIntended Users: | NemotronLabs VoiceChat is targeted for researchers, developers, and professionals in the field of natural language processing (NLP) and speech technology for purposes such as automatic speech recognition (ASR), text-to-speech synthesis (TTS), and voice assistant development.\nOutput: | Speech, Text\nDescribe how the model works: | Nemotron NemotronLabs VoiceChat is a real-time speech-to-speech conversational model that jointly performs streaming speech understanding and speech generation. The model operates on continuous audio encoded using a fast conformer module before the resulting audio tokens are handed to a Nemotron nano V2 9b LLM backbone to predict text tokens which are fed to an EAR-TTS decoder [2] generating the agent's speech.\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable\nTechnical Limitations & Mitigation: | NemotronLabs VoiceChat is trained with no more than 2-min long audio context windows. Conversational context beyond this window may not be retained reliably.
NemotronLabs VoiceChat has been optimized to achieve a trade-off between general knowledge and natural conversation, hence it may not be as performant as the LLM backbone it is derived from, namely NVIDIA Nemotron Nano v2, in terms of knowledge, instruction-following capabilities and safety.
The model can make reasoning errors or provide incorrect/incomplete information NemotronLabs VoiceChat was not explicitly trained for reasoning or alignment. Its performance on tasks requiring multi-step reasoning, arithmetic, or safety-aligned behavior may therefore be limited.
Further, the model may not yet be able to systematically handle user backchannelling.
As far as tool calling is concerned, we recommend a maximum of 5 tools per session; more tools may degrade performance. Also, the model cannot yet reliably call multiple tools simultaneously. Long tool responses may cause delays before the agent speaks (mitigate with on-hold messages). The user cannot interrupt the agent during tool calling execution. Further, in mixed conversations (general chat + tool requests), the model may answer from its own knowledge instead of calling the appropriate tool.
Finally, the model is not suitable for noisy or highly reverberant environments, especially where background speech may occur.\nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Knowledge performance metrics include accuracy (%), GPT-scores (1-5) and refusal rate (%).
Conversational dynamics metrics include takeover rate (TOR); latency (seconds) for interruptions, pause handling, and turn-taking; frequency and Jensen–Shannon divergence (JSD) for backchannels; question-answering accuracy (score); and speaker similarity (SSIM).\nPotential Known Risks: | The model may present errors in speech understanding and/or pronunciation. Additionally, the training data consists exclusively of English speech. The model cannot handle non-English languages. NemotronLabs VoiceChat was trained primarily on assistant-style and general conversational interactions. Its behavior may degrade when applied outside these domains. For security and privacy reasons, NemotronLabs VoiceChat does not support generating voice prompts from real user voice recordings.\nLicensing: | GOVERNING TERMS: The 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).","safetyAndSecurity","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personal data? | No\nPersonal data used to create this model? | Yes - Voice\nWas consent obtained for any personal data used? | Yes\nHow often is dataset reviewed? | During dataset creation, model training, evaluation and before release\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Yes\nIf personal data was collected for the development of the model, was it collected directly by NVIDIA? | No\nIf personal data was collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | NA\nIf personal data was collected for the development of this AI model, was it minimized to only what was required? | Yes\nWas data from user interactions with the AI model (e.g. user input and prompts) used to train the model? | No\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | NA\nApplicable Privacy Policy | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/","featureLabels",[207],"displayLabels",[189,190,191,192],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_154":204,"_194":204,"_195":205,"_197":198},{"_154":202,"_194":202,"_195":203,"_197":198},{"_154":200,"_194":200,"_195":201,"_197":198},{"_154":193,"_194":193,"_195":196,"_197":198},"NSPECT-HHHJ-E8N1","value","filter",{"_199":198,"_194":193},"category","label","field","False",{"_199":198,"_194":200},"Experimental",{"_199":198,"_194":202},"English",{"_199":206,"_194":204},"language",{"_154":208,"_194":209,"_195":210,"_197":211},"NVIDIA NIM","nimmcro_nvidia_nim",{"_199":212,"_194":209},"feature","nvidia_nim","appConfig",{"_1193":1194,"_247":1195},"ngcApps",[1146],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1127":1128,"_1129":1130,"_1131":1132,"_1133":1134,"_1135":225,"_80":254,"_1136":1137,"_1138":44,"_1139":108,"_1140":108,"_1141":1142,"_1143":108,"_1144":1145},"amplitude",{"_1124":1125,"_766":-7,"_1126":20},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1118":1119,"_1120":1121,"_1122":1123},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","https://api.ngc.nvidia.com","ngcHelmOrigin","https://helm.ngc.nvidia.com","origin","https://catalog.ngc.nvidia.com","user","usesMocks","theme","year",2026,"now",1788883265082,"1.15.0","defaultSearchSuggestions",{"_1006":111,"_1007":1008,"_1009":1010,"_1011":1012},"announcements",[],"consentState",{"_292":996,"_997":20,"_998":20,"_999":108,"_1000":1001},"consentConfig",{"_274":275,"_276":277,"_278":279,"_280":281,"_282":283,"_284":285,"_286":287,"_288":289,"_290":291,"_292":293},"showContactAdmin",1788883265083,[],[],[268,269,270,271,272,273],{"_33":48,"_35":57,"_37":65,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":55,"_37":65,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":53,"_37":65,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":49,"_37":65,"_39":-5,"_41":-5,"_43":44},{"_33":45,"_35":46,"_37":38,"_39":40,"_41":47,"_43":44},{"_33":34,"_35":36,"_37":65,"_39":-5,"_41":-5,"_43":44},"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.

Under certain privacy laws, you have the right to direct us not to \"sell\" or \"share\" your personal information for targeted advertising. To opt-out of the \"sale\" and \"sharing\" of personal information through cookies, you must opt-out of optional cookies using the toggles below. To opt out of the \"sale\" and \"sharing\" of data collected by other means (e.g., online forms) you must also update your data sharing preferences through the NVIDIA Preference Center.

Click on the different category headings below to find out more and change the settings according to your preference. You cannot opt out of Required Cookies as they are deployed to ensure the proper functioning of our website (such as prompting the cookie banner and remembering your settings, etc.). By clicking \"Save and Accept\" or \"Decline All\" at the bottom, you consent to the use of cookies and other tools as described in our Cookie Policy in accordance with your settings and accept our Terms of Service (which contains important waivers). For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_988":989,"_990":991,"_992":993,"_994":995},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[294,295,296,297,298],{"_299":678,"_140":679,"_110":680,"_303":108,"_304":72,"_306":681,"_308":682},{"_299":581,"_140":582,"_110":583,"_303":20,"_304":42,"_306":584,"_308":585},{"_299":488,"_140":489,"_110":490,"_303":20,"_304":491,"_306":492,"_308":493},{"_299":310,"_140":311,"_110":312,"_303":20,"_304":47,"_306":313,"_308":314},{"_299":300,"_140":301,"_110":302,"_303":20,"_304":305,"_306":307,"_308":309},"optanonGroupId","C0005","Social Media Cookies","These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. 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. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",[430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451],[315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331],{"_332":425,"_154":425,"_110":111,"_306":426},{"_332":421,"_154":421,"_110":111,"_306":422},{"_332":416,"_154":416,"_110":111,"_306":417},{"_332":412,"_154":412,"_110":111,"_306":413},{"_332":407,"_154":407,"_110":111,"_306":408},{"_332":402,"_154":402,"_110":111,"_306":403},{"_332":394,"_154":394,"_110":111,"_306":395},{"_332":386,"_154":386,"_110":111,"_306":387},{"_332":381,"_154":381,"_110":111,"_306":382},{"_332":376,"_154":376,"_110":111,"_306":377},{"_332":371,"_154":371,"_110":111,"_306":372},{"_332":366,"_154":366,"_110":111,"_306":367},{"_332":361,"_154":361,"_110":111,"_306":362},{"_332":354,"_154":354,"_110":111,"_306":355},{"_332":349,"_154":349,"_110":111,"_306":350},{"_332":342,"_154":342,"_110":111,"_306":343},{"_332":333,"_154":333,"_110":111,"_306":334},"hostName","amazon-adsystem.com",[335,336],{"_140":341,"_338":333,"_339":20,"_110":340},{"_140":337,"_338":333,"_339":20,"_110":340},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[344,345],{"_140":348,"_338":342,"_339":20,"_110":347},{"_140":346,"_338":342,"_339":20,"_110":347},"IDE","This domain is owned by Doubleclick (Google). The main business activity is: Doubleclick is Googles real time bidding advertising exchange","test_cookie","bing.com",[351],{"_140":352,"_338":349,"_339":20,"_110":353},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[356,357],{"_140":360,"_338":354,"_339":20,"_110":359},{"_140":358,"_338":354,"_339":20,"_110":359},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_*","If you know anything about this host and its uses please add your contributions here. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","tiktok.com",[363],{"_140":364,"_338":361,"_339":20,"_110":365},"_ttp","TikTok is a social media platform owned by ByteDance, known for short-form video sharing. The service is used to create and share videos, and it utilizes cookies for various purposes including advertising and analytics.","t.co",[368],{"_140":369,"_338":366,"_339":20,"_110":370},"muc_ads","This domain is owned by Twitter. It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[373],{"_140":374,"_338":371,"_339":20,"_110":375},"sp","Testing","google.com",[378],{"_140":379,"_338":376,"_339":20,"_110":380},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","linkedin.com",[383],{"_140":384,"_338":381,"_339":20,"_110":385},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[388,389],{"_140":392,"_338":386,"_339":20,"_110":393},{"_140":390,"_338":386,"_339":20,"_110":391},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_*","This domain is owned by X (formerly Twitter). The main business activity is: Social Networking Services. Where X (formerly Twitter) acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","youtube.com",[396,397],{"_140":400,"_338":394,"_339":20,"_110":401},{"_140":398,"_338":394,"_339":20,"_110":399},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-*","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","demdex.net",[404],{"_140":405,"_338":402,"_339":20,"_110":406},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[409],{"_140":410,"_338":407,"_339":20,"_110":411},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[414],{"_140":415,"_338":412,"_339":20,"_110":375},"R","6sc.co",[418],{"_140":419,"_338":416,"_339":20,"_110":420},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. It is primarily used for tracking and targeting in marketing automation and advertising.","hushly.com",[423],{"_140":424,"_338":421,"_339":20,"_110":375},"_hly_gvid","t.contentsquare.net",[427],{"_140":428,"_338":425,"_339":108,"_110":429},"_cs_*","This is a session cookie. This is a pattern type cookie where a random 13-digit number is appended to the prefix _cs_.",{"_140":486,"_338":453,"_339":20,"_110":487},{"_140":484,"_338":453,"_339":20,"_110":485},{"_140":482,"_338":453,"_339":20,"_110":483},{"_140":364,"_338":453,"_339":20,"_110":481},{"_140":458,"_338":453,"_339":108,"_110":460},{"_140":480,"_338":453,"_339":20,"_110":111},{"_140":479,"_338":456,"_339":20,"_110":111},{"_140":477,"_338":453,"_339":20,"_110":478},{"_140":475,"_338":453,"_339":20,"_110":476},{"_140":474,"_338":453,"_339":20,"_110":111},{"_140":472,"_338":453,"_339":20,"_110":473},{"_140":458,"_338":471,"_339":108,"_110":460},{"_140":469,"_338":456,"_339":20,"_110":470},{"_140":469,"_338":459,"_339":20,"_110":470},{"_140":428,"_338":453,"_339":20,"_110":429},{"_140":467,"_338":453,"_339":20,"_110":468},{"_140":374,"_338":465,"_339":20,"_110":466},{"_140":463,"_338":453,"_339":20,"_110":464},{"_140":461,"_338":453,"_339":20,"_110":462},{"_140":458,"_338":459,"_339":108,"_110":460},{"_140":455,"_338":456,"_339":20,"_110":457},{"_140":452,"_338":453,"_339":108,"_110":454},"AMCVS_xxxxxAdobeOrg","nvidia.com","This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","AMP_*","build.nvidia.com","Supports Amplitude product analytics.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547],[494,495,496,497,498],{"_332":407,"_154":407,"_110":111,"_306":518},{"_332":381,"_154":381,"_110":111,"_306":513},{"_332":508,"_154":508,"_110":111,"_306":509},{"_332":503,"_154":503,"_110":111,"_306":504},{"_332":499,"_154":499,"_110":111,"_306":500},"nvidia.partners",[501],{"_140":502,"_338":499,"_339":20,"_110":375},"edge","app.hushly.com",[505],{"_140":506,"_338":503,"_339":20,"_110":507},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[510],{"_140":511,"_338":508,"_339":20,"_110":512},"_octo","This domain is owned by Github. The main business activity is: Source code repository and collaboration platform",[514,515],{"_140":517,"_338":381,"_339":20,"_110":385},{"_140":516,"_338":381,"_339":20,"_110":385},"lidc","li_gc",[519,520],{"_140":522,"_338":407,"_339":20,"_110":411},{"_140":521,"_338":407,"_339":20,"_110":411},"imid_*","imid",{"_140":548,"_338":456,"_339":20,"_110":550},{"_140":579,"_338":453,"_339":20,"_110":580},{"_140":548,"_338":578,"_339":20,"_110":550},{"_140":576,"_338":453,"_339":108,"_110":577},{"_140":574,"_338":453,"_339":20,"_110":575},{"_140":573,"_338":556,"_339":108,"_110":111},{"_140":548,"_338":552,"_339":20,"_110":550},{"_140":571,"_338":453,"_339":20,"_110":572},{"_140":548,"_338":459,"_339":20,"_110":550},{"_140":559,"_338":456,"_339":20,"_110":111},{"_140":548,"_338":570,"_339":20,"_110":550},{"_140":568,"_338":453,"_339":20,"_110":569},{"_140":565,"_338":566,"_339":20,"_110":567},{"_140":563,"_338":564,"_339":20,"_110":111},{"_140":548,"_338":560,"_339":20,"_110":550},{"_140":562,"_338":453,"_339":20,"_110":111},{"_140":548,"_338":561,"_339":20,"_110":550},{"_140":559,"_338":560,"_339":20,"_110":111},{"_140":558,"_338":556,"_339":108,"_110":111},{"_140":548,"_338":557,"_339":20,"_110":550},{"_140":548,"_338":556,"_339":20,"_110":550},{"_140":548,"_338":555,"_339":20,"_110":550},{"_140":554,"_338":453,"_339":108,"_110":550},{"_140":551,"_338":552,"_339":108,"_110":553},{"_140":548,"_338":549,"_339":20,"_110":550},"_gd_*","info.nvidia.com","This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv_country_code","developer.nvidia.com","This cookie is likely used to store the country code of the visitor to provide localized content and possibly for legal compliance with regional data privacy laws.","_gdxxxxxxxxxxxxx","nvidianews.nvidia.com","docs.api.nvidia.com","research.nvidia.com","readme_library","wcmmode","ai-podcast.nvidia.com","resources.nvidia.com","geo_locator","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","crowdsource.nvidia.com","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","readme_language","vid","This cookie name 'vid' is used for tracking the search request made by the user on the 'Find a practitioner' page.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","blogs.nvidia.com","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634],[586,587,588,589],{"_332":601,"_154":601,"_110":111,"_306":602},{"_332":596,"_154":596,"_110":111,"_306":597},{"_332":394,"_154":394,"_110":111,"_306":593},{"_332":508,"_154":508,"_110":111,"_306":590},[591],{"_140":592,"_338":508,"_339":108,"_110":512},"_gh_sess",[594],{"_140":595,"_338":394,"_339":108,"_110":401},"YSC","bizible.com",[598],{"_140":599,"_338":596,"_339":20,"_110":600},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[603],{"_140":604,"_338":601,"_339":20,"_110":605},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_140":676,"_338":453,"_339":20,"_110":677},{"_140":661,"_338":675,"_339":108,"_110":662},{"_140":644,"_338":456,"_339":20,"_110":569},{"_140":644,"_338":557,"_339":20,"_110":569},{"_140":673,"_338":453,"_339":20,"_110":674},{"_140":661,"_338":459,"_339":108,"_110":662},{"_140":671,"_338":453,"_339":20,"_110":672},{"_140":661,"_338":456,"_339":108,"_110":662},{"_140":669,"_338":556,"_339":20,"_110":670},{"_140":661,"_338":578,"_339":108,"_110":662},{"_140":641,"_338":456,"_339":20,"_110":643},{"_140":667,"_338":453,"_339":108,"_110":668},{"_140":665,"_338":453,"_339":20,"_110":666},{"_140":663,"_338":453,"_339":108,"_110":664},{"_140":661,"_338":552,"_339":108,"_110":662},{"_140":659,"_338":453,"_339":20,"_110":660},{"_140":657,"_338":453,"_339":20,"_110":658},{"_140":655,"_338":453,"_339":108,"_110":656},{"_140":653,"_338":561,"_339":20,"_110":654},{"_140":641,"_338":552,"_339":20,"_110":643},{"_140":651,"_338":453,"_339":20,"_110":652},{"_140":649,"_338":453,"_339":108,"_110":650},{"_140":647,"_338":453,"_339":20,"_110":648},{"_140":645,"_338":453,"_339":20,"_110":646},{"_140":644,"_338":453,"_339":20,"_110":569},{"_140":641,"_338":642,"_339":20,"_110":643},{"_140":639,"_338":453,"_339":20,"_110":640},{"_140":637,"_338":453,"_339":108,"_110":638},{"_140":635,"_338":453,"_339":20,"_110":636},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_session_id","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.","_hjHasCachedUserAttributes","This cookie name is associated with Hotjar which is used to know whether the data set in _hjUserAttributes Local Storage item is up to date or not.","s_ppv","This cookie is associated with Adobe Analytics site tracking and is used to measure a user's scroll activity to see how much of a page they view before moving to another page.","_gid","This cookie name is associated with Google Universal Analytics. This appears to be a new cookie and as of Spring 2017 no information is available from Google. It appears to store and update a unique value for each page visited.","_hjTLDTest","When the Hotjar script executes we try to determine the most generic cookie path we should use, instead of the page hostname. This is done so that cookies can be shared across subdomains (where applicable). To determine this, we try to store the _hjTLDTest cookie for different URL substring alternatives until it fails. After this check, the cookie is removed.","ekfls-2025-03-27","This cookie name is associated with readme.io which is used to help maintain data for the API reference support page.","s_getNewRepeat","This cookie is used to determine whether a visitor is a new visitor or a repeat visitor to the site, and is used for analytical purposes.","_hly_sid","This cookie is likely associated with website analytics, as the prefix '_hly_' suggests a connection to Holistics, a business intelligence and analytics platform.","docs.nvidia.com","_hjSession_xxxxxx","A cookie that holds the current session data. This ensues that subsequent requests within the session window will be attributed to the same Hotjar session.","C0001","Required Cookies","These cookies enable core functionality such as security, network management, and accessibility. These cookies are required for the site to function and cannot be turned off.",[823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895],[683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707],{"_332":820,"_154":820,"_110":111,"_306":821},{"_332":814,"_154":814,"_110":111,"_306":815},{"_332":811,"_154":811,"_110":111,"_306":812},{"_332":799,"_154":799,"_110":111,"_306":800},{"_332":796,"_154":796,"_110":111,"_306":797},{"_332":793,"_154":793,"_110":111,"_306":794},{"_332":788,"_154":788,"_110":111,"_306":789},{"_332":394,"_154":394,"_110":111,"_306":785},{"_332":782,"_154":782,"_110":111,"_306":783},{"_332":386,"_154":386,"_110":111,"_306":780},{"_332":381,"_154":381,"_110":111,"_306":778},{"_332":773,"_154":773,"_110":111,"_306":774},{"_332":768,"_154":768,"_110":111,"_306":769},{"_332":763,"_154":763,"_110":111,"_306":764},{"_332":760,"_154":760,"_110":111,"_306":761},{"_332":508,"_154":508,"_110":111,"_306":757},{"_332":366,"_154":366,"_110":111,"_306":754},{"_332":503,"_154":503,"_110":111,"_306":747},{"_332":499,"_154":499,"_110":111,"_306":741},{"_332":735,"_154":735,"_110":111,"_306":736},{"_332":730,"_154":730,"_110":111,"_306":731},{"_332":725,"_154":725,"_110":111,"_306":726},{"_332":721,"_154":721,"_110":111,"_306":722},{"_332":716,"_154":716,"_110":111,"_306":717},{"_332":708,"_154":708,"_110":111,"_306":709},"polyfill.io",[710,711],{"_140":714,"_338":708,"_339":20,"_110":715},{"_140":712,"_338":708,"_339":20,"_110":713},"cf_ob_info","This cookie is associated with sites using CloudFlare. It is used to improve page load times and to override any security restrictions based on the visitors IP address. It does not contain any user identification information.","cf_use_ob","This cookie is associated with sites using CloudFlare. It is used to improve page load times and to override any security restrictions based on the visitors IP address. It does not contain any user identification information. ","www.recaptcha.net",[718],{"_140":719,"_338":716,"_339":20,"_110":720},"_GRECAPTCHA","This domain is used by Google reCAPTCHA, which provides spam protection services to websites by detecting if a user is a human or a bot.","api2.hcaptcha.com",[723],{"_140":724,"_338":721,"_339":20,"_110":375},"__cflb","instagram.com",[727],{"_140":728,"_338":725,"_339":20,"_110":729},"csrftoken","Instagram is a social networking service owned by Facebook, Inc. It's designed for sharing photos and videos from a smartphone. The service also uses cookies for various purposes, including analytics, personalization, and advertising.","readme.io",[732],{"_140":733,"_338":730,"_339":20,"_110":734},"__cf_bm","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.","nvidiacorp.us-5.evergage.com",[737,738],{"_140":740,"_338":735,"_339":20,"_110":375},{"_140":739,"_338":735,"_339":20,"_110":375},"AWSALBTG","AWSALBTGCORS",[742,743],{"_140":745,"_338":499,"_339":20,"_110":746},{"_140":744,"_338":499,"_339":20,"_110":375},"ak_bmsc","bm_mi","This domain is likely associated with NVIDIA's partner program, which could involve marketing, sales, and support services for partners.",[748,749,750],{"_140":753,"_338":503,"_339":20,"_110":375},{"_140":752,"_338":503,"_339":108,"_110":375},{"_140":751,"_338":503,"_339":20,"_110":375},"AWSALBCORS","JSESSIONID","AWSALB",[755],{"_140":733,"_338":366,"_339":20,"_110":756},"This is a CloudFoundry cookie",[758],{"_140":759,"_338":508,"_339":20,"_110":512},"logged_in","hcaptcha.com",[762],{"_140":733,"_338":760,"_339":20,"_110":756},"tracking.intentsify.io",[765],{"_140":766,"_338":763,"_339":20,"_110":767},"userId","Used to track usage of the embedded video player.","static.reo.dev",[770],{"_140":771,"_338":768,"_339":20,"_110":772},"cookietest","Common cookie name could have a number of different origins. Where this is first party and a session cookie, its most likely to do with checking to see if the browser is set to block or allow cookies.","www.datadoghq-browser-agent.com",[775],{"_140":776,"_338":773,"_339":20,"_110":777},"dd_cookie_test_","dd_cookie_test",[779],{"_140":733,"_338":381,"_339":20,"_110":756},[781],{"_140":733,"_338":386,"_339":20,"_110":756},"supabase.co",[784],{"_140":733,"_338":782,"_339":20,"_110":756},[786],{"_140":787,"_338":394,"_339":20,"_110":401},"VISITOR_PRIVACY_METADATA","dosa-prd.azurewebsites.net",[790],{"_140":791,"_338":788,"_339":20,"_110":792},"access_token","This cookie is used to obtain the permissions for users of the portal to allow for API access in order to retrieve information required to perform portal functions.","w.hcaptcha.com",[795],{"_140":733,"_338":793,"_339":20,"_110":756},"cdn.readme.io",[798],{"_140":733,"_338":796,"_339":20,"_110":756},"queue-it.net",[801,802,803,804,805],{"_140":810,"_338":799,"_339":20,"_110":111},{"_140":809,"_338":799,"_339":20,"_110":111},{"_140":808,"_338":799,"_339":20,"_110":111},{"_140":807,"_338":799,"_339":20,"_110":111},{"_140":806,"_338":799,"_339":20,"_110":111},"Queue-it-{queueId}","Queue-it-{dmonvidia }________{eventId}","Queue-it-token","queueitsoftblock_{queueId}","Queue-it-{nvidia}________{eventId}","x.com",[813],{"_140":733,"_338":811,"_339":20,"_110":734},"api.hcaptcha.com",[816,817],{"_140":819,"_338":814,"_339":20,"_110":375},{"_140":724,"_338":814,"_339":20,"_110":818},"This domain is associated with hCaptcha, a service that provides CAPTCHA and reCAPTCHA alternatives for user verification and bot protection.","hmt_id","vimeo.com",[822],{"_140":733,"_338":820,"_339":20,"_110":756},{"_140":956,"_338":453,"_339":20,"_110":987},{"_140":985,"_338":549,"_339":108,"_110":986},{"_140":978,"_338":947,"_339":108,"_110":979},{"_140":984,"_338":642,"_339":108,"_110":111},{"_140":982,"_338":453,"_339":20,"_110":983},{"_140":740,"_338":968,"_339":20,"_110":907},{"_140":915,"_338":977,"_339":108,"_110":917},{"_140":739,"_338":906,"_339":20,"_110":907},{"_140":918,"_338":977,"_339":20,"_110":919},{"_140":980,"_338":453,"_339":108,"_110":981},{"_140":978,"_338":954,"_339":108,"_110":979},{"_140":912,"_338":977,"_339":20,"_110":914},{"_140":975,"_338":453,"_339":20,"_110":976},{"_140":739,"_338":898,"_339":20,"_110":907},{"_140":903,"_338":974,"_339":20,"_110":905},{"_140":751,"_338":904,"_339":20,"_110":920},{"_140":753,"_338":904,"_339":20,"_110":971},{"_140":918,"_338":913,"_339":20,"_110":919},{"_140":972,"_338":552,"_339":20,"_110":973},{"_140":753,"_338":578,"_339":20,"_110":971},{"_140":969,"_338":453,"_339":20,"_110":970},{"_140":745,"_338":578,"_339":20,"_110":964},{"_140":941,"_338":557,"_339":108,"_110":111},{"_140":739,"_338":968,"_339":20,"_110":907},{"_140":966,"_338":906,"_339":108,"_110":967},{"_140":930,"_338":453,"_339":20,"_110":921},{"_140":903,"_338":453,"_339":20,"_110":905},{"_140":965,"_338":465,"_339":20,"_110":111},{"_140":745,"_338":453,"_339":20,"_110":964},{"_140":753,"_338":898,"_339":20,"_110":963},{"_140":961,"_338":561,"_339":108,"_110":962},{"_140":959,"_338":453,"_339":108,"_110":960},{"_140":958,"_338":453,"_339":20,"_110":111},{"_140":956,"_338":904,"_339":20,"_110":957},{"_140":955,"_338":465,"_339":20,"_110":111},{"_140":946,"_338":954,"_339":108,"_110":948},{"_140":952,"_338":557,"_339":20,"_110":953},{"_140":950,"_338":642,"_339":20,"_110":951},{"_140":949,"_338":459,"_339":20,"_110":111},{"_140":946,"_338":947,"_339":108,"_110":948},{"_140":944,"_338":913,"_339":108,"_110":945},{"_140":942,"_338":453,"_339":20,"_110":943},{"_140":941,"_338":456,"_339":108,"_110":111},{"_140":912,"_338":916,"_339":20,"_110":914},{"_140":744,"_338":931,"_339":20,"_110":921},{"_140":939,"_338":456,"_339":20,"_110":940},{"_140":937,"_338":453,"_339":20,"_110":938},{"_140":935,"_338":453,"_339":20,"_110":936},{"_140":934,"_338":453,"_339":20,"_110":111},{"_140":776,"_338":456,"_339":20,"_110":933},{"_140":930,"_338":578,"_339":20,"_110":921},{"_140":733,"_338":549,"_339":20,"_110":932},{"_140":930,"_338":931,"_339":20,"_110":921},{"_140":929,"_338":465,"_339":20,"_110":111},{"_140":928,"_338":453,"_339":20,"_110":901},{"_140":926,"_338":913,"_339":20,"_110":927},{"_140":739,"_338":924,"_339":20,"_110":925},{"_140":923,"_338":465,"_339":20,"_110":111},{"_140":922,"_338":453,"_339":20,"_110":111},{"_140":915,"_338":913,"_339":108,"_110":917},{"_140":744,"_338":578,"_339":20,"_110":921},{"_140":751,"_338":578,"_339":20,"_110":920},{"_140":918,"_338":916,"_339":20,"_110":919},{"_140":915,"_338":916,"_339":108,"_110":917},{"_140":912,"_338":913,"_339":20,"_110":914},{"_140":910,"_338":906,"_339":108,"_110":911},{"_140":908,"_338":564,"_339":20,"_110":909},{"_140":740,"_338":906,"_339":20,"_110":907},{"_140":903,"_338":904,"_339":20,"_110":905},{"_140":733,"_338":556,"_339":20,"_110":902},{"_140":900,"_338":453,"_339":20,"_110":901},{"_140":751,"_338":898,"_339":20,"_110":899},{"_140":896,"_338":453,"_339":20,"_110":897},"LoginHints","Keeps 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.","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.","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.","LSKey-c$CookieConsentPolicy","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","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.","CookieConsentPolicy","Used to apply end-user cookie consent preferences set by our client-side utility.","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.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","nv_co","__uxq412__ses.88c1","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.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.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","OptanonAlertBoxClosed","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.","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","bm_ss","This cookie is used to help protect websites from automated bot traffic.","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.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","docs.ngc.nvidia.com","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. Usually used to maintain an anonymised user session by the server.","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","c_code","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","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 one year lifespan and contains no personal information.","default","

NVIDIA uses cookies to improve your experience on our web site. We and our third-party partners also use cookies and other tools to collect and record information you provide as well as information about your interactions with our websites for performance improvement, analytics, and to assist in marketing efforts. By clicking \"Accept All\", you consent to our use of cookies and other tools as described in our Cookie Policy. You can manage your cookie settings by clicking on \"Manage Settings.\" By continuing to use this site or by clicking one of the buttons below, you agree to our Terms of Service (which contains important waivers). Please see our Privacy Policy for more information on our privacy practices.

","gpc","

We have detected the Global Privacy Control (GPC) signal and recorded your rejection of all optional cookies on this site for this browser. You can manage your cookie settings by clicking on \"Manage Settings\". Please see our Cookie Policy for more information. To opt out of non-cookie personal information \"sales\" / \"sharing\" for targeted advertising purposes, please visit the NVIDIA Preference Center. Please see our Privacy Policy for more information on our privacy practices.

","gpcLoggedIn","

We have detected the Global Privacy Control Signal (GPC) and have opted you out of all optional cookies on this browser. You can manage your cookie settings by clicking on \"Manage Settings\". Please see our Cookie Policy for more information. We have also opted you out of \"sharing\"/\"sales\" of personal information outside of cookies. You can manage these settings in the NVIDIA NVIDIA Preference Center. Please see our Privacy Policy for more information.

","gpcLoggedInOverridden","

We have detected the Global Privacy Control Signal (GPC) and have opted you out of all optional cookies on this browser. You can manage your cookie settings by clicking on \"Manage Settings\". Please see our Cookie Policy for more information. We have also opted you out of \"sharing\"/\"sales\" of personal information outside of cookies which overrides at least one of your previous settings. You can manage them in the NVIDIA Preference Center. Please see our Privacy Policy for more information.

",{"_678":72,"_581":44,"_488":44,"_310":44,"_300":44},"hasConsented","gpcApplied","showBanner","geolocation",{"_1002":1003,"_1004":1005},"country","US","state","OR","query","categories",[1025,1026,1027,1028,1029],"optionUrls",[1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024],"total",12,"/orgs/nim/deepseek-ai/containers/deepseek-r1/-","/orgs/nim/nvidia/containers/llama-3.1-nemotron-70b-instruct/-","/orgs/nvidia/-/containers/pytorch/-","/orgs/nvidia/omniverse/collections/kit/-","/orgs/nvidia/-/collections/deepstream_sdk/-","/orgs/nvidia/omniverse/collections/kit-appstreaming-collection/-","/orgs/nvidia/research/models/stylegan3/-","/orgs/nvidia/tao/models/peoplenet/-","/orgs/nvidia/tao/models/trafficcamnet/-","/orgs/nvidia/riva/resources/riva_quickstart/-","/orgs/nvidia/-/helm-charts/gpu-operator/-","/orgs/nvidia/-/helm-charts/k8s-nim-operator/-",{"_147":1103,"_1031":1104,"_1033":44},{"_147":1068,"_1031":1069,"_1033":42},{"_147":148,"_1031":1052,"_1033":1053},{"_147":1044,"_1031":1045,"_1033":1046},{"_147":1030,"_1031":1032,"_1033":1034},"helm-charts","items",[1035,1036],"startIndex",10,{"_147":1030,"_154":1041,"_103":106,"_105":-7,"_149":1042,"_157":158,"_186":1043,"_1040":1023},{"_147":1030,"_154":1037,"_103":106,"_105":-7,"_149":1038,"_157":158,"_186":1039,"_1040":1024},"NVIDIA NIM Operator","k8s-nim-operator",[],"url","GPU Operator","gpu-operator",[],"resources",[1047],9,{"_147":1044,"_154":1048,"_103":106,"_105":1049,"_149":1050,"_157":158,"_186":1051,"_1040":1022},"Riva Skills Quick Start","riva","riva_quickstart",[],[1054,1055,1056],6,{"_147":148,"_154":1064,"_103":106,"_105":1065,"_149":1066,"_157":158,"_186":1067,"_1040":1019},{"_147":148,"_154":1061,"_103":106,"_105":1058,"_149":1062,"_157":158,"_186":1063,"_1040":1020},{"_147":148,"_154":1057,"_103":106,"_105":1058,"_149":1059,"_157":158,"_186":1060,"_1040":1021},"TrafficCamNet","tao","trafficcamnet",[],"PeopleNet","peoplenet",[],"StyleGAN3 pretrained models","research","stylegan3",[],"collections",[1070,1071,1072],{"_147":1068,"_154":1100,"_103":106,"_105":1074,"_149":1101,"_157":158,"_186":1102,"_1040":1016},{"_147":1068,"_154":1097,"_103":106,"_105":-7,"_149":1098,"_157":158,"_186":1099,"_1040":1017},{"_147":1068,"_154":1073,"_103":106,"_105":1074,"_149":1075,"_157":158,"_186":1076,"_1040":1018},"Omniverse Kit App Streaming","omniverse","kit-appstreaming-collection",[1077,1078,1079],{"_140":1091,"_154":1092,"_151":1093,"_162":1094,"_1084":1095},{"_140":1074,"_154":1087,"_151":1088,"_162":1083,"_1084":1089},{"_140":1080,"_154":1081,"_151":1082,"_162":1083,"_1084":1085},"omniverse-dev","NVIDIA Developer Program","Join the Developer Program for access to free tools, support, and tech resources.","https://store-images.s-microsoft.com/image/apps.33936.6f1bcf42-55d0-45d1-b4b6-f08c68a50bbe.e91ca707-db64-49d6-9761-4781590a9685.7ba64fc5-7c44-4864-a5e6-7bde05126935","supportedFeatures",[1086],"UNIFIED_CATALOG","NVIDIA Omniverse","NVIDIA Omniverse is a platform of APIs, services, and software development kits (SDKs) that enable developers to build generative AI-enabled tools and applications.",[1086,1090],"SHAREABLE_TO_USER_GROUPS","nv-ai-enterprise","NVIDIA AI Enterprise","Accelerate your AI agent development","https://assets.ngc.nvidia.com/products/catalog/images/product-NVAIE.jpg",[1086,1090,1096],"NGC_CONSOLE","DeepStream SDK","deepstream_sdk",[],"Omniverse Kit (FB)","kit",[1077,1078,1079],"containers",[1105,1106,1107],{"_147":1103,"_154":1114,"_103":104,"_105":1115,"_149":1116,"_157":158,"_186":1117,"_1040":1013},{"_147":1103,"_154":1111,"_103":104,"_105":106,"_149":1112,"_157":158,"_186":1113,"_1040":1014},{"_147":1103,"_154":1108,"_103":106,"_105":-7,"_149":1109,"_157":158,"_186":1110,"_1040":1015},"PyTorch","pytorch",[],"Llama-3.1-Nemotron-70B-Instruct","llama-3.1-nemotron-70b-instruct",[],"DeepSeek-R1","deepseek-ai","deepseek-r1",[],"ownerOrgs",[],"memberOrgs",[],"externalOrgs",[],"apiKey","2b44f86fea50e90ffd2db29ee2028419","consentGranted","applicationId","4c857e4f-5ffa-45fa-b75e-af773c22f0ad","clientToken","pubb6927db33dae60ba5031a186cde0242a","site","datadoghq.com","service","catalog-ui","env","sessionSampleRate",100,"sessionReplaySampleRate","compressIntakeRequests","storeContextsAcrossPages","defaultPrivacyLevel","mask-user-input","trackViewsManually","trackingConsent","not-granted",{"_1147":1148,"_1149":246,"_1150":20,"_1151":1152,"_1031":1153,"_1154":1155,"_1156":1148,"_1157":20,"_1158":20,"_1159":20,"_1160":1161},"appName","ngc-catalog","appOrigin","hideOnPrivateOrg","icon","ObjectsModules2",[1162,1163,1164,1165,1166,1167,1168],"menuIcon","editor-swatches","productName","requiresAllScopes","requiresLogin","requiresOrgLevelScopesOnly","title","Catalog",{"_1160":1190,"_35":1191,"_1154":1192},{"_1183":1184,"_1160":1185,"_35":1186,"_1156":1091},{"_1160":1181,"_35":1182,"_1154":1155},{"_1160":1178,"_35":1179,"_1154":1180},{"_1160":1175,"_35":1176,"_1154":1177},{"_1160":1172,"_35":1173,"_1154":1174},{"_1160":1169,"_35":1170,"_1154":1171},"Resources","/resources","misc-library","Models","/models","misc-neural-network","Helm Charts","/helm-charts","misc-ship-wheel","Containers","/containers","3d-cube","Collections","/collections","access",[1187],"Enterprise Catalog","/enterprise",[1188,1189],"NVAIE","READ","Explore Catalog","/","common-home","urls",{"_1197":1198,"_1199":242,"_1200":244,"_1201":1202,"_1203":1204,"_1205":1206},{"_1196":20},"loggedIn","build","https://build.nvidia.com","ngcApi","ngcHelm","ngcRegistry","https://registry.ngc.nvidia.com","orgUi","https://org.ngc.nvidia.com","ngcUi","https://ngc.nvidia.com"]