Model Overview

Description

ReaSyn is a model for predicting the synthesis pathway, reaction steps from reactants to finadatal product(s), for a target product molecule. When the target molecule cannot be synthesized directly using known reaction steps, ReaSyn will generate the pathways for the most structurally similar, synthesizable analog of the target molecule.The model uses an encoder-decoder transformer architecture and a chain-of-reaction notation, where a full synthetic pathway is represented as a text sequence. This approach allows the model to achieve SOTA performance in tasks like synthesis planning and incorporating synthesizability into goal-directed molecular property optimization.

This model is ready for commercial use.

License/Terms of Use

GOVERNING TERMS: Use of this model is governed by the NVIDIA Open Model License. ReaSyn source code is licensed under Apache 2.0.

Deployment Geography: Global

Use Case:
\nReaSyn is a model for predicting the synthetic pathway, reaction steps from reactants to final product(s), for a target product molecule. The model can be used in the pharmaceutical and chemical industries and in academic research to identify how to synthesize a molecule, help chemists in planning a first time synthesis of a molecule, the optimization of an existing synthesis pathway, or the filtering of candidate molecules based on ease of synthesis.

Release Date:
\nGithub 09/23/2025 via https://github.com/NVIDIA-Digital-Bio/ReaSyn
\nNGC 09/23/2025

References

Research paper: “Rethinking Molecule Synthesizability with Chain-of-Reaction”

Model Architecture

Architecture Type: Encoder-decoder\nNetwork Architecture: Encoder-decoder Transformer\nReaSyn utilizes an Encoder-decoder Transformer architecture which takes a molecular SMILES as input and outputs its synthetic pathway. Encoder contains 6 layers and decoder contains 10 layers. Both encoder and decoder have a hidden size of 768, 16 attention heads, and a feed-forward dimension of 4096.

The total number of parameters in ReaSyn is 166M.

Input

Input Types: Text
\nInput Formats: SMILES string
\nInput Parameters: One-Dimensional (1D)
\nOther Properties Related to Input: Maximum input length is 256 tokens.

Output

Output Types: Text
\nOutput Formats: Chain-of-Reaction sequence (molecular synthetic pathway)
\nOutput Parameters: One-Dimensional (1D)
\nOther Properties Related to Output: Maximum output length is 768 tokens.

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

Software Integration

Runtime Engine: Torch
\nSupported Hardware Microarchitecture Compatibility: NVIDIA Ampere
\nPreferred Operating System: Linux, Windows

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 Versions

ReaSyn v1

Training and Evaluation Datasets

Training Datasets

SynFormer Reaction Templates
\nLink: https://github.com/wenhao-gao/synformer/blob/main/data/rxn_templates/comprehensive.txt
\nData Modality: Text
\nText Training Data Size: 1 Billion to 10 Trillion Tokens
\nData Collection Method by dataset: Human
\nLabeling Method by dataset: Automated
\nProperties: 115 molecular reaction templates in the SMARTS format

Building Blocks in Enamine US Stock retrieved in October 2023
\nLink: https://enamine.net/building-blocks/building-blocks-catalog
\nData Modality: Text
\nText Training Data Size: 1 Billion to 10 Trillion Tokens
\nData Collection Method by dataset: Human
\nLabeling Method by dataset: N/A
\nProperties: 115 molecular reaction templates in the SMARTS format

Evaluation Dataset

Enamine REAL Test Set
\nLink: https://github.com/wenhao-gao/synformer/blob/main/data/enamine_smiles_1k.txt
\nhttps://enamine.net/compound-collections/real-compounds/real-database
\nData Collection Method by dataset: Human
\nLabeling Method by dataset: N/A
\nProperties: Randomly selected 1k test molecules from Enamine REAL to evaluate synthesizable molecule reconstruction.

ChEMBL Test Set
\nLink: https://github.com/wenhao-gao/synformer/blob/main/data/chembl_filtered_1k.txt
\nhttps://www.ebi.ac.uk/chembl
\nData Collection Method by dataset: Human
\nLabeling Method by dataset: N/A
\nProperties: Randomly selected 1k test molecules from ChEMBL to evaluate synthesizable molecule reconstruction.

ZINC250k Test Set
\nLink: https://www.kaggle.com/datasets/basu369victor/zinc250k
\nData Collection Method by dataset: Synthetic
\nLabeling Method by dataset: N/A
\nProperties: Randomly selected 1k test molecules from ZINC250k to evaluate synthesizable molecule reconstruction.

Inference

Engine: Torch
\nTest Hardware: Ampere / NVIDIA A100

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.

Users are responsible for ensuring the physical properties of model-generated molecules are appropriately evaluated and comply with applicable safety regulations and ethical standards.

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

Please report security vulnerabilities or NVIDIA AI Concerns here.

Bias

Participation considerations from adversely impacted groups (protected classes) in model design and testing: None
\nMeasures taken to mitigate against unwanted bias: None

Explainability

Intended Application(s) & Domain(s): Molecular drug discovery and design
\nModel Type: Molecular synthesis pathway generation
\nIntended Users: Developers in the academic or pharmaceutical industries who want to predict synthesis pathways for molecules and who build artificial intelligence applications to perform property guided molecule optimization and novel molecule generation.
\nOutput: Text
\nDescribe how the model works: ReaSyn uses a Transformer encoder-decoder architecture and requires a \"target molecule\" as its input (SMILES format). The model then generates a synthetic pathway for a synthesizable molecule that that molecule, or, if needed, an analog of the input molecule.
\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: Not Applicable
\nTechnical Limitations: The model's reasoning steps, while incorporating reactants, reaction types, and intermediate products, do not account for other important reaction information like environmental conditions or yields. This could be a limitation in real-world drug discovery scenarios.
\nVerified to have met prescribed quality standards?: Yes
\nPerformance Metrics: Reconstruction rate, Similarity, Diversity (Pathway), Diversity (BB).
\nPotential Known Risks: The framework, while effective for generating drug candidates, also has the possibility of generating synthetic pathways for toxic drugs. This requires an additional scheme to be adopted to filter out harmful molecules during the generation search.
\nLicensing & Terms of Use: The use of this model is governed by the NVIDIA Open Model License Agreement.

Privacy

Generatable or reverse engineerable personal data?: No
\nPersonal data used to create this model?: No
\nHow often is the dataset reviewed?: Before Release
\nIs there provenance for all datasets used in training?: Yes
\nDoes data labeling (annotation, metadata) comply with privacy laws?: Yes
\nApplicable Privacy Policy: NVIDIA Privacy Policy

Safety

Model Application(s): Synthesis Pathway Prediction in drug, chemical, and materials design.
\nDescribe life critical impact (if present): Experimental results: Additional in silico and experimental tests are recommended before using the predicted synthesis paths in downstream applications.
\nUse Case Restrictions: Abide by NVIDIA Open Model License Agreement.
\nModel and Dataset Restrictions: The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.

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\nReaSyn is a model for predicting the synthetic pathway, reaction steps from reactants to final product(s), for a target product molecule. The model can be used in the pharmaceutical and chemical industries and in academic research to identify how to synthesize a molecule, help chemists in planning a first time synthesis of a molecule, the optimization of an existing synthesis pathway, or the filtering of candidate molecules based on ease of synthesis.
\n\nRelease Date:
\nGithub 09/23/2025 via https://github.com/NVIDIA-Digital-Bio/ReaSyn
\nNGC 09/23/2025
\n\n### References\nResearch paper: “Rethinking Molecule Synthesizability with Chain-of-Reaction”\n\n### Model Architecture\n\nArchitecture Type: Encoder-decoder\nNetwork Architecture: Encoder-decoder Transformer\nReaSyn utilizes an Encoder-decoder Transformer architecture which takes a molecular SMILES as input and outputs its synthetic pathway. Encoder contains 6 layers and decoder contains 10 layers. Both encoder and decoder have a hidden size of 768, 16 attention heads, and a feed-forward dimension of 4096.\n\nThe total number of parameters in ReaSyn is 166M.\n\n### Input\n\nInput Types: Text
\nInput Formats: SMILES string
\nInput Parameters: One-Dimensional (1D)
\nOther Properties Related to Input: Maximum input length is 256 tokens.\n\n### Output\n\nOutput Types: Text
\nOutput Formats: Chain-of-Reaction sequence (molecular synthetic pathway)
\nOutput Parameters: One-Dimensional (1D)
\nOther Properties Related to Output: Maximum output length is 768 tokens.\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n\n### Software Integration\n\nRuntime Engine: Torch
\nSupported Hardware Microarchitecture Compatibility: NVIDIA Ampere
\nPreferred Operating System: Linux, Windows\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 Versions\n\nReaSyn v1\n\n\n## Training and Evaluation Datasets\n\n### Training Datasets\n\nSynFormer Reaction Templates
\nLink: https://github.com/wenhao-gao/synformer/blob/main/data/rxn_templates/comprehensive.txt
\nData Modality: Text
\nText Training Data Size: 1 Billion to 10 Trillion Tokens
\nData Collection Method by dataset: Human
\nLabeling Method by dataset: Automated
\nProperties: 115 molecular reaction templates in the SMARTS format\n\nBuilding Blocks in Enamine US Stock retrieved in October 2023
\nLink: https://enamine.net/building-blocks/building-blocks-catalog
\nData Modality: Text
\nText Training Data Size: 1 Billion to 10 Trillion Tokens
\nData Collection Method by dataset: Human
\nLabeling Method by dataset: N/A
\nProperties: 115 molecular reaction templates in the SMARTS format\n\n### Evaluation Dataset\n\nEnamine REAL Test Set
\nLink: https://github.com/wenhao-gao/synformer/blob/main/data/enamine_smiles_1k.txt
\nhttps://enamine.net/compound-collections/real-compounds/real-database
\nData Collection Method by dataset: Human
\nLabeling Method by dataset: N/A
\nProperties: Randomly selected 1k test molecules from Enamine REAL to evaluate synthesizable molecule reconstruction.
\n\nChEMBL Test Set
\nLink: https://github.com/wenhao-gao/synformer/blob/main/data/chembl_filtered_1k.txt
\nhttps://www.ebi.ac.uk/chembl
\nData Collection Method by dataset: Human
\nLabeling Method by dataset: N/A
\nProperties: Randomly selected 1k test molecules from ChEMBL to evaluate synthesizable molecule reconstruction.
\n\nZINC250k Test Set
\nLink: https://www.kaggle.com/datasets/basu369victor/zinc250k
\nData Collection Method by dataset: Synthetic
\nLabeling Method by dataset: N/A
\nProperties: Randomly selected 1k test molecules from ZINC250k to evaluate synthesizable molecule reconstruction.
\n\n### Inference\n\nEngine: Torch
\nTest Hardware: Ampere / NVIDIA A100\n\n### Ethical Considerations\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. 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\nUsers are responsible for ensuring the physical properties of model-generated molecules are appropriately evaluated and comply with applicable safety regulations and ethical standards.\n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://app.intigriti.com/programs/nvidia/nvidiavdp/detail).\n\n\n### Bias\nParticipation considerations from adversely impacted groups (protected classes) in model design and testing: None
\nMeasures taken to mitigate against unwanted bias: None\n\n### Explainability\nIntended Application(s) & Domain(s): Molecular drug discovery and design
\nModel Type: Molecular synthesis pathway generation
\nIntended Users: Developers in the academic or pharmaceutical industries who want to predict synthesis pathways for molecules and who build artificial intelligence applications to perform property guided molecule optimization and novel molecule generation.
\nOutput: Text
\nDescribe how the model works: ReaSyn uses a Transformer encoder-decoder architecture and requires a \"target molecule\" as its input (SMILES format). The model then generates a synthetic pathway for a synthesizable molecule that that molecule, or, if needed, an analog of the input molecule.
\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: Not Applicable
\nTechnical Limitations: The model's reasoning steps, while incorporating reactants, reaction types, and intermediate products, do not account for other important reaction information like environmental conditions or yields. This could be a limitation in real-world drug discovery scenarios.
\nVerified to have met prescribed quality standards?: Yes
\nPerformance Metrics: Reconstruction rate, Similarity, Diversity (Pathway), Diversity (BB).
\nPotential Known Risks: The framework, while effective for generating drug candidates, also has the possibility of generating synthetic pathways for toxic drugs. This requires an additional scheme to be adopted to filter out harmful molecules during the generation search.
\nLicensing & Terms of Use: The use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\n### Privacy\nGeneratable or reverse engineerable personal data?: No
\nPersonal data used to create this model?: No
\nHow often is the dataset reviewed?: Before Release
\nIs there provenance for all datasets used in training?: Yes
\nDoes data labeling (annotation, metadata) comply with privacy laws?: Yes
\nApplicable Privacy Policy: [NVIDIA Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/)\n\n### Safety\nModel Application(s): Synthesis Pathway Prediction in drug, chemical, and materials design.
\nDescribe life critical impact (if present): Experimental results: Additional in silico and experimental tests are recommended before using the predicted synthesis paths in downstream applications.
\nUse Case Restrictions: Abide by [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).
\nModel and Dataset Restrictions: The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.","displayName","ReaSyn",["D",1758309494708],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/clara/resources/reasyn","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[271,272,273,274],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_241":289,"_276":290,"_277":291,"_279":88},{"_241":285,"_276":286,"_277":287,"_279":88},{"_241":281,"_276":282,"_277":283,"_279":88},{"_241":275,"_276":275,"_277":278,"_279":88},"NSPECT-NZMC-4JKB","value","filter",{"_280":88,"_276":275},"category","field","Healthcare","indus_healthcare",{"_280":284,"_276":282},"industry","Drug Discovery","uscs_drug_discovery",{"_280":288,"_276":286},"application","AI","soln_ai",{"_280":292,"_276":290},"technology",{"_152":1465,"_154":1466},"ngcApps",[1418],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1399":1400,"_1401":1402,"_1403":1404,"_1405":1406,"_1407":304,"_110":328,"_1408":1409,"_1410":59,"_1411":91,"_1412":91,"_1413":1414,"_1415":91,"_1416":1417},"amplitude",{"_1396":1397,"_1000":-7,"_1398":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1390":1391,"_1392":1393,"_1394":1395},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1275":220,"_1276":1277,"_1278":1279,"_1280":1281},"announcements",[],"consentState",{"_366":1265,"_1266":91,"_1267":34,"_1268":34,"_1269":1270},"consentConfig",{"_348":349,"_350":351,"_352":353,"_354":355,"_356":357,"_358":359,"_360":361,"_362":363,"_364":365,"_366":367},"showContactAdmin",1784099233121,[],[],[342,343,344,345,346],{"_48":63,"_50":74,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":347,"_58":59},6,"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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It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[446],{"_113":447,"_413":444,"_414":34,"_212":448},"sp","Testing","google.com",[451],{"_113":452,"_413":449,"_414":34,"_212":453},"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.","tags.srv.stackadapt.com",[456,457,458,459],{"_113":464,"_413":454,"_414":34,"_212":461},{"_113":463,"_413":454,"_414":34,"_212":461},{"_113":462,"_413":454,"_414":34,"_212":461},{"_113":460,"_413":454,"_414":34,"_212":461},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[467],{"_113":468,"_413":465,"_414":34,"_212":469},"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",[472,473,474,475],{"_113":481,"_413":470,"_414":34,"_212":477},{"_113":479,"_413":470,"_414":34,"_212":480},{"_113":478,"_413":470,"_414":34,"_212":477},{"_113":476,"_413":470,"_414":34,"_212":477},"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_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[484,485,486,487],{"_113":493,"_413":482,"_414":34,"_212":491},{"_113":492,"_413":482,"_414":34,"_212":491},{"_113":490,"_413":482,"_414":34,"_212":491},{"_113":488,"_413":482,"_414":34,"_212":489},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","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.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[496],{"_113":497,"_413":494,"_414":34,"_212":498},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[501],{"_113":502,"_413":499,"_414":34,"_212":503},"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",[506],{"_113":507,"_413":504,"_414":34,"_212":448},"R","6sc.co",[510],{"_113":511,"_413":508,"_414":34,"_212":512},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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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.",[639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659],[604,605,606,607,608],{"_407":499,"_241":499,"_212":220,"_380":630},{"_407":465,"_241":465,"_212":220,"_380":625},{"_407":618,"_241":618,"_212":220,"_380":619},{"_407":614,"_241":614,"_212":220,"_380":615},{"_407":609,"_241":609,"_212":220,"_380":610},"nvcrm.my.site.com",[611],{"_113":612,"_413":609,"_414":34,"_212":613},"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",[616],{"_113":617,"_413":614,"_414":34,"_212":448},"edge","github.com",[620,621],{"_113":624,"_413":618,"_414":34,"_212":623},{"_113":622,"_413":618,"_414":34,"_212":623},"logged_in","This domain is owned by Github. The main business activity is: Source code repository and collaboration platform","_octo",[626,627],{"_113":629,"_413":465,"_414":34,"_212":469},{"_113":628,"_413":465,"_414":34,"_212":469},"lidc","li_gc",[631,632,633,634],{"_113":638,"_413":499,"_414":34,"_212":503},{"_113":637,"_413":499,"_414":34,"_212":503},{"_113":636,"_413":499,"_414":34,"_212":503},{"_113":635,"_413":499,"_414":34,"_212":503},"imid_secure","imid_created_secure","imid","imid_created",{"_113":698,"_413":553,"_414":34,"_212":695},{"_113":696,"_413":553,"_414":34,"_212":697},{"_113":694,"_413":553,"_414":34,"_212":695},{"_113":692,"_413":553,"_414":91,"_212":693},{"_113":690,"_413":661,"_414":34,"_212":691},{"_113":689,"_413":670,"_414":91,"_212":220},{"_113":687,"_413":661,"_414":34,"_212":688},{"_113":685,"_413":553,"_414":34,"_212":686},{"_113":682,"_413":683,"_414":34,"_212":684},{"_113":681,"_413":557,"_414":34,"_212":220},{"_113":679,"_413":553,"_414":34,"_212":680},{"_113":676,"_413":677,"_414":34,"_212":678},{"_113":674,"_413":675,"_414":34,"_212":220},{"_113":673,"_413":661,"_414":34,"_212":220},{"_113":672,"_413":553,"_414":34,"_212":220},{"_113":671,"_413":557,"_414":34,"_212":220},{"_113":669,"_413":670,"_414":91,"_212":220},{"_113":667,"_413":661,"_414":34,"_212":668},{"_113":665,"_413":553,"_414":91,"_212":666},{"_113":663,"_413":553,"_414":34,"_212":664},{"_113":660,"_413":661,"_414":91,"_212":662},"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.","_biz_kvpA","This cookie is likely related to business analytics and key-value pairs for storing user preferences, which may be used to enhance functionality and user experience on the website.","_gdxxxxxxxxxxxxx","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.modal.cuda","This cookie name suggests it is related to a modal popup functionality on NVIDIA's website, possibly to remember user preferences or interactions with the modal.","readme_library","docs.api.nvidia.com","_im_ses.1006927","geo_locator","nv.modal.deepstream","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.","_im_id.1006927","_hjUserAttributesHash","docs.nvidia.com","This cookie name is associated with Hotjar which is used to know when any user attribute has changed and needs to be updated.","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","nv.modal.cudnn","This cookie name suggests it is related to modal popup handling or user interface preferences on NVIDIA's website, specifically for the cuDNN library section.","readme_language","nv.modal.hpc","This cookie name suggests it is related to a modal pop-up on a website, possibly to control the display of a high-priority cookie notice or other important information to the user.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","_biz_uid","This cookie 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.\n","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.","_biz_nA","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 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