GPT OSS 20B Overview

Description:

OpenAI releases the gpt-oss family of open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases. The family consists of the:

gpt-oss-120b — for production, general purpose, high reasoning use-cases that fits into a single H100 GPU (117B parameters with 5.1B active parameters)
gpt-oss-20b — for lower latency, and local or specialized use-cases (21B parameters with 3.6B active parameters).

The gpt-oss-20b is designed as a Mixture-of-Experts (MoE) model, structurally identical to the larger 117B variant, albeit with different hyperparameters. This model leverages SwiGLU activations and incorporates learned attention sinks within its architecture. Functionally, it serves as a robust reasoning model, supporting advanced capabilities such as chain-of-thought processing, adjustable reasoning effort levels, instruction following, and tool use. It operates strictly with text-only modalities for both input and output. A key strategic benefit is its suitability for enterprises and governments, facilitating on-premises or private cloud deployment to ensure enhanced data security and privacy.

Model Highlights:

Configurable reasoning effort: Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.
Full chain-of-thought: Gain complete access to the model's reasoning process, facilitating easier debugging and increased trust in outputs. It's not intended to be shown to end users.
Fine-tunable: Fully customize models to your specific use case through parameter fine-tuning.
Agentic capabilities: Use the models' native capabilities for function calling, web browsing, python code execution, and structured outputs.

This model is ready for commercial/non-commercial use.

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA gpt-oss-20b model card.

License and Terms of Use:

GOVERNING TERMS: This model is released under the NVIDIA Open Model License. The underlying model is licensed under the Apache License, version 2.0.

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Enterprise Support

Get access to knowledge base articles and support cases or submit a ticket.

NVIDIA NIM Documentation

Visit the NIM Container LLM page for release documentation, deployment guides and more.

Deployment Geography:

Global

Use Case:

Intended for use as a reasoning model, offering features like chain-of-thought and adjustable reasoning effort levels. It provides comprehensive support for instruction following and tool use, fostering transparency, customization, and deployment flexibility for developers, researchers, and startups. Crucially, it enables enterprises and governments to deploy on-premises or in private clouds, ensuring stringent data security and privacy requirements are met.

Release Date:

Build.NVIDIA.com - 08/05/2025 via link
\nHugging Face - 08/05/2025 via link

Reference(s):

Model Architecture:

Architecture Type: Transformer
\nNetwork Architecture: Mixture-of-Experts (MoE)
\nTotal Parameters: 20B
\nActive Parameters: 4B
\nVocabulary Size: 201,088 (Utilizes the standard tokenizer used by GPT-4o)

Input:

Input Type(s): Text
\nInput Format(s): String
\nInput Parameters: One Dimensional (1D)
\nOther Properties Related to Input: Uses RoPE with a 128k context length, with attention layers alternating between full context and a sliding 128-token window. Includes a learned attention sink per-head. Employs SwiGLU activations in the MoE layers, and the router performs a Top-K operation (K=4) followed by a Sigmoid function. GEMMs in the MoE include a per-expert bias. Utilizes tiktoken for tokenization. Input Context Length (ISL): 128000

Output:

Output Type(s): Text
\nOutput Format: String
\nOutput Parameters: One Dimensional (1D)
\nOther Properties Related to Output: The model is architected to be compatible with the OpenAI Responses API and supports Structured Output, aligning with key partner expectations for advanced response formatting.

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

Software Integration:

Runtime Engine(s):

NeMo Framework (based on 25.07)

Supported Hardware Microarchitecture Compatibility:

NVIDIA Blackwell: B200
NVIDIA Hopper: H200

Operating System(s): Linux

Model Version(s):

gpt-oss-20b v1.0 (August 5, 2025)

Training, Testing, and Evaluation Datasets:

Training Dataset:

Training Data Collection: Undisclosed
Training Labeling: Undisclosed
Training Properties: The gpt-oss-20b model has approximately 20 billion total parameters, with approximately 4 billion active parameters per inference. The weights for all layers are in BF16, except for the MoE projection weights, which are in MXFP4. The reference implementation, for initial accuracy validation, currently upcasts all weights to BF16. Activations are expected to be in BF16 or FP8.

Testing Dataset:

Testing Data Collection: Undisclosed
Testing Labeling: Undisclosed
Testing Properties: The model's performance is tested against recognized benchmarks such as MMLU (Massive Multitask Language Understanding) and GPQA (General Purpose Question Answering), alongside other benchmarks including LiveCodeBench, AIME 2024, and MATH-500

Evaluation Dataset:

Evaluation Data Collection: Undisclosed
Evaluation Labeling: Undisclosed
Evaluation Benchmark Score:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\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\">

Benchmark	gpt-oss-120b	gpt-oss-20b
AIME 2024 (no tools)	95.8	92.1
AIME 2024 (with tools)	96.6	96.0
AIME 2025 (no tools)	92.5	91.7
AIME 2025 (with tools)	97.9	98.7
GPQA Diamond (no tools)	80.1	71.5
GPQA Diamond (with tools)	80.9	74.2
HLE (no tools)	14.9	10.9
HLE (with tools)	19.0	17.3
MMLU	90.0	85.3
SWE-Bench Verified	62.4	60.7
Tau-Bench Retail	67.8	54.4
Tau-Bench Airline	49.2	38.0
Aider Polyglot	44.4	34.2
MMMLU (Average)	81.3	75.6
HealthBench	57.6	42.5
HealthBench Hard	30.0	10.8
HealthBench Consensus	89.9	82.6
Codeforces (no tools) [elo]	2463	2230
Codeforces (with tools) [elo]	2622	2516

Above scores were measured for the high reasoning level.

Safety Results:

The following evaluations check that the model does not comply with requests for content that is\ndisallowed under OpenAI’s safety policies, including hateful content or illicit advice.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\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\">

Category	gpt-oss-120b	gpt-oss-20b
hate (aggregate)	0.996	0.996
self-harm/intent and selfharm/instructions	0.995	0.984
personal data/semi restrictive	0.967	0.947
sexual/exploitative	1.000	0.980
sexual/minors	1.000	0.971
illicit/non-violent	1.000	0.983
illicit/violent	1.000	1.000
personal data/restricted	0.996	0.978

Inference:

Acceleration Engine: vLLM
\nTest Hardware: NVIDIA Hopper (H200)

Additional Details

The model is released with the native quantization support. Specifically, MXFP4 is used for the linear projection weights in the MoE layer. It is stored the MoE tensor in two parts:

tensor.blocks stores the actual fp4 values. Every two values are packed in one uint8 value.
tensor.scales stores the block scale. The block scaling is done among the last dimension for all MXFP4 tensors.

All other tensors are stored in BF16. It is recommended to use BF16 as the activation precision for the model.

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 security vulnerabilities or NVIDIA AI Concerns here.

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\nOpenAI releases the gpt-oss family of open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases. The family consists of the:\n- `gpt-oss-120b` — for production, general purpose, high reasoning use-cases that fits into a single H100 GPU (117B parameters with 5.1B active parameters)\n- `gpt-oss-20b` — for lower latency, and local or specialized use-cases (21B parameters with 3.6B active parameters).\n\nThe `gpt-oss-20b` is designed as a Mixture-of-Experts (MoE) model, structurally identical to the larger 117B variant, albeit with different hyperparameters. This model leverages SwiGLU activations and incorporates learned attention sinks within its architecture. Functionally, it serves as a robust reasoning model, supporting advanced capabilities such as chain-of-thought processing, adjustable reasoning effort levels, instruction following, and tool use. It operates strictly with text-only modalities for both input and output. A key strategic benefit is its suitability for enterprises and governments, facilitating on-premises or private cloud deployment to ensure enhanced data security and privacy.\n\nModel Highlights: \n- **Configurable reasoning effort:** Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.\n- **Full chain-of-thought:** Gain complete access to the model's reasoning process, facilitating easier debugging and increased trust in outputs. It's not intended to be shown to end users.\n- **Fine-tunable:** Fully customize models to your specific use case through parameter fine-tuning.\n- **Agentic capabilities:** Use the models' native capabilities for function calling, web browsing, python code execution, and structured outputs.\n\nThis model is ready for commercial/non-commercial use.\n\n## Third-Party Community Consideration
\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA [gpt-oss-20b model card](https://huggingface.co/openai/gpt-oss-20b).\n\n### License and Terms of Use:
\n\nGOVERNING TERMS: This model is released under the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). The underlying model is licensed under the [Apache License, version 2.0](https://www.apache.org/licenses/LICENSE-2.0).\n\n\n## Get Help\n\n### Enterprise Support\n\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/).\n\n### NVIDIA NIM Documentation\n\nVisit the [NIM Container LLM](https://docs.nvidia.com/nim/large-language-models/latest/introduction.html) page for release documentation, deployment guides and more.\n\n\n### Deployment Geography:\nGlobal\n\n### Use Case:
\nIntended for use as a reasoning model, offering features like chain-of-thought and adjustable reasoning effort levels. It provides comprehensive support for instruction following and tool use, fostering transparency, customization, and deployment flexibility for developers, researchers, and startups. Crucially, it enables enterprises and governments to deploy on-premises or in private clouds, ensuring stringent data security and privacy requirements are met.\n\n### Release Date:
\nBuild.NVIDIA.com - 08/05/2025 via [link](https://build.nvidia.com/openai/gpt-oss-20b)
\nHugging Face - 08/05/2025 via [link](https://huggingface.co/openai/gpt-oss-20b)
\n\n## Reference(s):\n- [OpenAI Cookbook](https://cookbook.openai.com/)\n- [Open AI Coobkbook -- Serving Model with TensorRT-LLM](https://cookbook.openai.com/articles/gpt-oss/run-nvidia)\n\n\n## Model Architecture:
\n**Architecture Type:** Transformer
\n**Network Architecture:** Mixture-of-Experts (MoE)
\n**Total Parameters:** 20B
\n**Active Parameters:** 4B
\n**Vocabulary Size:** 201,088 (Utilizes the standard tokenizer used by GPT-4o)
\n\n\n## Input:
\n**Input Type(s):** Text
\n**Input Format(s):** String
\n**Input Parameters:** One Dimensional (1D)
\n**Other Properties Related to Input:** Uses RoPE with a 128k context length, with attention layers alternating between full context and a sliding 128-token window. Includes a learned attention sink per-head. Employs SwiGLU activations in the MoE layers, and the router performs a Top-K operation (K=4) followed by a Sigmoid function. GEMMs in the MoE include a per-expert bias. Utilizes tiktoken for tokenization. Input Context Length (ISL): 128000
\n\n## Output:
\n**Output Type(s):** Text
\n**Output Format:** String
\n**Output Parameters:** One Dimensional (1D)
\n**Other Properties Related to Output:** The model is architected to be compatible with the OpenAI Responses API and supports Structured Output, aligning with key partner expectations for advanced response formatting.
\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems [or name equivalent hardware preference]. 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**Runtime Engine(s):**
\n* NeMo Framework (based on 25.07)
\n\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Blackwell: B200
\n* NVIDIA Hopper: H200\n\n\n**Operating System(s):** Linux \n\n## Model Version(s): \n`gpt-oss-20b` v1.0 (August 5, 2025)\n\n\n## Training, Testing, and Evaluation Datasets:
\n### Training Dataset:\n\n* **Training Data Collection:** Undisclosed
\n* **Training Labeling:** Undisclosed
\n* **Training Properties:** The gpt-oss-20b model has approximately 20 billion total parameters, with approximately 4 billion active parameters per inference. The weights for all layers are in BF16, except for the MoE projection weights, which are in MXFP4. The reference implementation, for initial accuracy validation, currently upcasts all weights to BF16. Activations are expected to be in BF16 or FP8.\n\n\n### Testing Dataset:\n* **Testing Data Collection:** Undisclosed
\n* **Testing Labeling:** Undisclosed
\n* **Testing Properties:** The model's performance is tested against recognized benchmarks such as MMLU (Massive Multitask Language Understanding) and GPQA (General Purpose Question Answering), alongside other benchmarks including LiveCodeBench, AIME 2024, and MATH-500 \n\n### Evaluation Dataset:\n\n* **Evaluation Data Collection:** Undisclosed
\n* **Evaluation Labeling:** Undisclosed
\n* **Evaluation Benchmark Score:** \n\n| Benchmark | gpt-oss-120b | gpt-oss-20b |\n|----------|-----------| -----------|\n| AIME 2024 (no tools) | 95.8 | 92.1 |\n| AIME 2024 (with tools) | 96.6 | 96.0 |\n| AIME 2025 (no tools) | 92.5 | 91.7 |\n| AIME 2025 (with tools) | 97.9 | 98.7 |\n| GPQA Diamond (no tools) | 80.1 | 71.5 |\n| GPQA Diamond (with tools) | 80.9 | 74.2 |\n| HLE (no tools) | 14.9 | 10.9 |\n| HLE (with tools) | 19.0 | 17.3 |\n| MMLU | 90.0 | 85.3 |\n| SWE-Bench Verified | 62.4 | 60.7 |\n| Tau-Bench Retail | 67.8 | 54.4 |\n| Tau-Bench Airline | 49.2 | 38.0 |\n| Aider Polyglot | 44.4 | 34.2 |\n| MMMLU (Average) | 81.3 | 75.6 |\n| HealthBench | 57.6 | 42.5 |\n| HealthBench Hard | 30.0 | 10.8 |\n| HealthBench Consensus | 89.9 | 82.6 |\n| Codeforces (no tools) [elo] | 2463 | 2230 |\n| Codeforces (with tools) [elo] | 2622 | 2516 |\n\nAbove scores were measured for the high reasoning level.\n\n### Safety Results:\n\nThe following evaluations check that the model does not comply with requests for content that is\ndisallowed under OpenAI’s safety policies, including hateful content or illicit advice.\n\n| Category | gpt-oss-120b | gpt-oss-20b |\n|----------|-----------| -----------|\n| hate (aggregate) | 0.996 | 0.996 |\n| self-harm/intent and selfharm/instructions | 0.995 | 0.984 |\n| personal data/semi restrictive | 0.967 | 0.947 |\n| sexual/exploitative | 1.000 | 0.980 |\n| sexual/minors | 1.000 | 0.971 |\n| illicit/non-violent | 1.000 | 0.983 |\n| illicit/violent | 1.000 | 1.000 |\n| personal data/restricted | 0.996 | 0.978 |\n\n## Inference:\n**Acceleration Engine:** vLLM
\n**Test Hardware:** NVIDIA Hopper (H200)
\n\n\n## Additional Details\nThe model is released with the native quantization support. Specifically, [MXFP4](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf) is used for the linear projection weights in the MoE layer. It is stored the MoE tensor in two parts:\n\n- `tensor.blocks` stores the actual fp4 values. Every two values are packed in one `uint8` value.\n- `tensor.scales` stores the block scale. The block scaling is done among the last dimension for all MXFP4 tensors.\n\nAll other tensors are stored in BF16. It is recommended to use BF16 as the activation precision for the model.\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 security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","GPT-OSS-20B",["D",1757016914370],"publisher","OpenAI","latestVersion","collectionCounts","imageUrl","/api/image/nim/openai/models/gpt-oss-20b","rawImageUrl","https://assets.ngc.nvidia.com/products/api-catalog/images/gpt-oss-20b.jpg","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[258,259],"displayLabels",[247,248],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_216":256,"_250":256,"_251":257,"_253":254},{"_216":249,"_250":249,"_251":252,"_253":254},"Signed Models","value","filter",{"_255":254,"_250":249},"category","label","field","NSPECT-SW5U-LCYW",{"_255":254,"_250":256},{"_216":265,"_250":265,"_251":266,"_253":263},{"_216":260,"_250":261,"_251":262,"_253":263},"NVIDIA NIM","nimmcro_nvidia_nim",{"_255":264,"_250":261},"feature","nvidia_nim","NVIDIA AI Enterprise Supported",{"_255":254,"_250":265},{"_126":1441,"_128":1442},"ngcApps",[1394],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1375":1376,"_1377":1378,"_1379":1380,"_1381":1382,"_1383":278,"_157":302,"_1384":1385,"_1386":60,"_1387":88,"_1388":88,"_1389":1390,"_1391":88,"_1392":1393},"amplitude",{"_1372":1373,"_975":-7,"_1374":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1366":1367,"_1368":1369,"_1370":1371},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1250":185,"_1251":1252,"_1253":1254,"_1255":92},"announcements",[],"consentState",{"_340":1240,"_1241":88,"_1242":35,"_1243":35,"_1244":1245},"consentConfig",{"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341},"showContactAdmin",1783910893454,[],[],[316,317,318,319,320,321],{"_49":63,"_51":72,"_53":121,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":69,"_53":121,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":68,"_53":121,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":64,"_53":121,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":121,"_55":-5,"_57":-5,"_59":60},{"_49":50,"_51":52,"_53":121,"_55":-5,"_57":-5,"_59":60},"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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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525],[364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380],{"_381":487,"_216":487,"_184":185,"_354":488},{"_381":482,"_216":482,"_184":185,"_354":483},{"_381":478,"_216":478,"_184":185,"_354":479},{"_381":473,"_216":473,"_184":185,"_354":474},{"_381":468,"_216":468,"_184":185,"_354":469},{"_381":456,"_216":456,"_184":185,"_354":457},{"_381":444,"_216":444,"_184":185,"_354":445},{"_381":439,"_216":439,"_184":185,"_354":440},{"_381":428,"_216":428,"_184":185,"_354":429},{"_381":423,"_216":423,"_184":185,"_354":424},{"_381":418,"_216":418,"_184":185,"_354":419},{"_381":413,"_216":413,"_184":185,"_354":414},{"_381":408,"_216":408,"_184":185,"_354":409},{"_381":403,"_216":403,"_184":185,"_354":404},{"_381":398,"_216":398,"_184":185,"_354":399},{"_381":391,"_216":391,"_184":185,"_354":392},{"_381":382,"_216":382,"_184":185,"_354":383},"hostName","amazon-adsystem.com",[384,385],{"_77":390,"_387":382,"_388":35,"_184":389},{"_77":386,"_387":382,"_388":35,"_184":389},"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",[393,394],{"_77":397,"_387":391,"_388":35,"_184":396},{"_77":395,"_387":391,"_388":35,"_184":396},"IDE","This domain is owned by Doubleclick (Google). 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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.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_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.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","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.","AMP_MKTG_91bf70adfe","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,[614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634],[579,580,581,582,583],{"_381":473,"_216":473,"_184":185,"_354":605},{"_381":439,"_216":439,"_184":185,"_354":600},{"_381":593,"_216":593,"_184":185,"_354":594},{"_381":589,"_216":589,"_184":185,"_354":590},{"_381":584,"_216":584,"_184":185,"_354":585},"nvcrm.my.site.com",[586],{"_77":587,"_387":584,"_388":35,"_184":588},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[591],{"_77":592,"_387":589,"_388":35,"_184":422},"edge","github.com",[595,596],{"_77":599,"_387":593,"_388":35,"_184":598},{"_77":597,"_387":593,"_388":35,"_184":598},"logged_in","This domain is owned by Github. 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