NVSaliENC Overview

Description:

NVSaliENC is an advanced perceptual video encoding module designed to improve visual quality in compressed video streams, specifically targeting critical regions as defined by human visual perception. While each generation of traditional video codecs provides measurable gains in objective quality at fixed bitrates, these improvements often do not correspond to noticeable visual differences for end users. Legacy codecs are limited in their ability to optimize perceptual quality, especially within the 2–5° visual angle around the viewer’s gaze center—a region where the human eye is most sensitive to detail.

NVSaliENC bridges this gap by employing a state-of-the-art deep learning-based saliency prediction model, expertly optimized for real-time inference using NVIDIA TensorRT. It is designed for seamless integration with NVIDIA NVENC, enabling practical and efficient deployment in production workflows. Unlike previous saliency solutions, NVSaliENC achieves real-time performance, making it suitable for latency-sensitive scenarios such as broadcasting, live streaming, and cloud gaming.

By generating high-precision saliency maps, NVSaliENC empowers NVENC to prioritize perceptual video compression for the most visually important regions, thereby enhancing end-user experience while reducing bandwidth usage and computational demand.

License/Terms of Use:

DLPP model license in NVIDIA GPU driver: https://www.nvidia.com/en-us/drivers/geforce-license/.

Deployment Geography:

Global

Use Case:

Individual users who own an NVIDIA GPU and a NVENC can use it to improve the video encoding visual quality.

Release Date:

NGC: 11/21/2025

References

NTECH 2025: NVSaliENC Human Vision-Guided Compression for Psycho-Visual Video Optimization

Model Architecture:

Architecture Type: Mixed Transformer-CNN based Network Architecture
\nNetwork Architecture:

The Mix-UNet network combines the strengths of transformer-based attention and multi-scale convolutional features to accurately capture both local and global visual cues relevant to human attention. We adopt a set of CNN decoders to extract spatial information from multi-scale features, and the residual connections help prevent any loss of information during the downsizing steps of the transformer encoders.

Number of model parameters: 5.2 * 10^7

Input:

Input Type(s): An image and an optional encoding mode
\nInput Format(s): YUV NV12, String
\nInput Parameters: Two-Dimensional (2D)
\nOther Properties Related to Input:

Y channel: B X H X W X 1(Batch Size x Height x Width x Channel)
UV channel: B X H/2 X W/2 X 2(Batch Size x Height/2 x Width/2 x Channel) in interleaved format.
An optional input can be provided to specify the encoding use case: either VBR (variable bitrate) or CQ (constant quality).

Output:

Output Type(s): Image
\nOutput Format: Delta QP map
\nOutput Parameters: Two-Dimensional (2D)
\nOther Properties Related to Output: The output is a delta QP map in block format. Suppose the input resolution is H×W. The block size is 32 for H.265 and 64 for AV1. The dimensions of the delta QP map are (H // block_size, W // block_size). The value range is (-128, 127), where negative QP values allocate more bit resources to the target region.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems None. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), our model achieves faster training and inference times compared to CPU-only solutions.

Software Integration:

Runtime Engine(s):

TensorRT

Supported Hardware Microarchitecture Compatibility:

NVIDIA Turing
NVIDIA Ampere
NVIDIA Jetson
NVIDIA Lovelace

[Preferred/Supported] Operating System(s):

Linux
Linux 4 Tegra

Model Version(s):

Version 1.0: first stable release which supports vbr and cq mode. Requirement: TRT 10.14.

Training, Testing, and Evaluation Datasets:

Dataset Overview:

The datasets aim to enhance encoding quality around predicted salient regions, aligning with human vision system.

Training Dataset:

Link: NVSaliENC Datasets: NSPECT-4Q2T-2ZKV

Data Collection Method by dataset: Hybrid: Synthetic, Human.

Labeling Method by dataset:

The general saliency annotations include fixations generated from mouse trajectories. To improve the data quality, isolated fixations with low local density have been excluded.
The second plane saliency is synthesized by detecting the boxes containing texts.

Properties (Quantity, Dataset Descriptions, Sensor(s)): Resolution: 640x480.

Dataset License(s): NVSaliENC Datasets: https://jirasw.nvidia.com/browse/DGPTT-2487

Testing Dataset:

Link: NVSaliENC Datasets: NSPECT-4Q2T-2ZKV

Data Collection Method by dataset: Hybrid: Synthetic, Human.

Labeling Method by dataset:

The general saliency annotations include fixations generated from mouse trajectories. To improve the data quality, isolated fixations with low local density have been excluded.
The second plane saliency is synthesized by detecting the boxes containing texts.

Properties (Quantity, Dataset Descriptions, Sensor(s)): Resolution: 640x480.

Dataset License(s): NVSaliENC Datasets: https://jirasw.nvidia.com/browse/DGPTT-2487

Evaluation Dataset:

Data Collection Method by dataset:

Human

Properties (Quantity, Dataset Descriptions, Sensor(s)): A few hundred videos from customers are used for evaluation.

Inference:

Acceleration Engine: TensorRT

Test Hardware:

[NVIDIA A10]
[NVIDIA T4]

Realtime Inference Latency:
\nThe latency data was obtained when the input resolution was 3840x2160.

\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\">

GPU	Runtime (ms)
A10	2.948
T4	7.700

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.

For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards None.

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

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While each generation of traditional video codecs provides measurable gains in objective quality at fixed bitrates, these improvements often do not correspond to noticeable visual differences for end users. Legacy codecs are limited in their ability to optimize perceptual quality, especially within the 2–5° visual angle around the viewer’s gaze center—a region where the human eye is most sensitive to detail.\n\nNVSaliENC bridges this gap by employing a state-of-the-art deep learning-based saliency prediction model, expertly optimized for real-time inference using NVIDIA TensorRT. It is designed for seamless integration with NVIDIA NVENC, enabling practical and efficient deployment in production workflows. Unlike previous saliency solutions, NVSaliENC achieves real-time performance, making it suitable for latency-sensitive scenarios such as broadcasting, live streaming, and cloud gaming.\n\nBy generating high-precision saliency maps, NVSaliENC empowers NVENC to prioritize perceptual video compression for the most visually important regions, thereby enhancing end-user experience while reducing bandwidth usage and computational demand. \n\n### License/Terms of Use: \nDLPP model license in NVIDIA GPU driver: https://www.nvidia.com/en-us/drivers/geforce-license/.\n\n### Deployment Geography: \nGlobal\n\n### Use Case: \nIndividual users who own an NVIDIA GPU and a NVENC can use it to improve the video encoding visual quality. \n\n### Release Date: \n**NGC:** 11/21/2025 \n\n### References\n[NTECH 2025](https://nvidia.sharepoint.com/:p:/r/sites/NTECH2/Shared%20Documents/2025/Robotics,%20Vision%20and%20Autonomous%20Vehicles/Presentations/NTECH2025_567_NVSaliENC_Human_Vision-Guided_Compression_for_Psycho-Visual_Video_Optimization.pptx?d=wa272806e966747deb9ee3997bbe98a76&csf=1&web=1&e=qzQFb1): NVSaliENC Human Vision-Guided Compression for Psycho-Visual Video Optimization\n\n## Model Architecture: \n**Architecture Type:** Mixed Transformer-CNN based Network Architecture \n**Network Architecture:** \n\nThe Mix-UNet network combines the strengths of transformer-based attention and multi-scale convolutional features to accurately capture both local and global visual cues relevant to human attention. We adopt a set of CNN decoders to extract spatial information from multi-scale features, and the residual connections help prevent any loss of information during the downsizing steps of the transformer encoders. \n \n**Number of model parameters:** 5.2 * 10^7 \n\n## Input: \n**Input Type(s):** An image and an optional encoding mode \n**Input Format(s):** YUV NV12, String \n**Input Parameters:** Two-Dimensional (2D) \n**Other Properties Related to Input:** \n\n* Y channel: B X H X W X 1(Batch Size x Height x Width x Channel)\n* UV channel: B X H/2 X W/2 X 2(Batch Size x Height/2 x Width/2 x Channel) in interleaved format.\n* An optional input can be provided to specify the encoding use case: either VBR (variable bitrate) or CQ (constant quality). \n\n## Output: \n**Output Type(s):** Image \n**Output Format:** Delta QP map \n**Output Parameters:** Two-Dimensional (2D) \n**Other Properties Related to Output:** The output is a delta QP map in block format. Suppose the input resolution is H×W. The block size is 32 for H.265 and 64 for AV1. The dimensions of the delta QP map are (H // block_size, W // block_size). The value range is (-128, 127), where negative QP values allocate more bit resources to the target region. \n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems None. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), our model achieves faster training and inference times compared to CPU-only solutions. \n\n## Software Integration: \n**Runtime Engine(s):** \n* TensorRT \n\n**Supported Hardware Microarchitecture Compatibility:** \n\n* NVIDIA Turing \n* NVIDIA Ampere \n* NVIDIA Jetson \n* NVIDIA Lovelace \n\n**[Preferred/Supported] Operating System(s):** \n\n* Linux \n* Linux 4 Tegra \n\n## Model Version(s): \n\n* Version 1.0: first stable release which supports vbr and cq mode. Requirement: TRT 10.14.\n \n## Training, Testing, and Evaluation Datasets: \n\n### Dataset Overview: \nThe datasets aim to enhance encoding quality around predicted salient regions, aligning with human vision system.\n\n## Training Dataset:\n**Link:** NVSaliENC Datasets: NSPECT-4Q2T-2ZKV \n\n**Data Collection Method by dataset:** Hybrid: Synthetic, Human. \n\n**Labeling Method by dataset:** \n* The general saliency annotations include fixations generated from mouse trajectories. To improve the data quality, isolated fixations with low local density have been excluded.\n* The second plane saliency is synthesized by detecting the boxes containing texts. \n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** Resolution: 640x480.\n\n**Dataset License(s):** NVSaliENC Datasets: https://jirasw.nvidia.com/browse/DGPTT-2487 \n\n## Testing Dataset:\n**Link:** NVSaliENC Datasets: NSPECT-4Q2T-2ZKV \n\n**Data Collection Method by dataset:** Hybrid: Synthetic, Human. \n\n**Labeling Method by dataset:** \n* The general saliency annotations include fixations generated from mouse trajectories. To improve the data quality, isolated fixations with low local density have been excluded.\n* The second plane saliency is synthesized by detecting the boxes containing texts. \n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** Resolution: 640x480.\n\n**Dataset License(s):** NVSaliENC Datasets: https://jirasw.nvidia.com/browse/DGPTT-2487 \n\n## Evaluation Dataset:\n\n**Data Collection Method by dataset:** \n* Human \n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** A few hundred videos from customers are used for evaluation. \n\n## Inference: \n**Acceleration Engine:** TensorRT \n\n**Test Hardware:** \n* \\[NVIDIA A10\\] \n* \\[NVIDIA T4\\] \n\n**Realtime Inference Latency:** \nThe latency data was obtained when the input resolution was 3840x2160.\n\n| GPU | Runtime (ms) |\n| :---: | :---: |\n| A10 | 2.948 |\n| T4 | 7.700 |\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\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards None. \n\nPlease report model quality, risk, security vulnerabilities or concerns NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","NVSaliENC",["D",1767746929245],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/multimedia/models/nvsalienc","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[244,245,246,247,248],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_213":267,"_250":268,"_252":269,"_254":255},{"_213":263,"_250":264,"_252":265,"_254":255},{"_213":260,"_250":261,"_252":262,"_254":255},{"_213":258,"_250":258,"_252":259,"_254":255},{"_213":249,"_250":251,"_252":253,"_254":255},"TensorRT","value","pltfm_tensorrt","filter",{"_256":257,"_250":251},"category","label","field","platform","NSPECT-YCAV-W5KW",{"_256":255,"_250":258},"CUDA","pltfm_cuda",{"_256":257,"_250":261},"Computer Vision","soln_computer_vision",{"_256":266,"_250":264},"technology","AI","soln_ai",{"_256":266,"_250":268},{"_112":1248,"_114":1249},"ngcApps",[1201],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1182":1183,"_1184":1185,"_1186":1187,"_1188":1189,"_1190":281,"_143":307,"_1191":1192,"_1193":56,"_1194":91,"_1195":91,"_1196":1197,"_1198":91,"_1199":1200},"amplitude",{"_1179":1180,"_820":-7,"_1181":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1173":1174,"_1175":1176,"_1177":1178},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787958195574,"1.12.0","defaultSearchSuggestions",{"_1060":173,"_1061":1062,"_1063":1064,"_1065":1066},"announcements",[],"consentState",{"_345":1050,"_1051":31,"_1052":31,"_1053":91,"_1054":1055},"consentConfig",{"_327":328,"_329":330,"_331":332,"_333":334,"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346},"showContactAdmin",1787958195575,[],[],[321,322,323,324,325,326],{"_45":59,"_47":70,"_49":107,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":107,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":66,"_49":107,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":107,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":54,"_55":56},{"_45":46,"_47":48,"_49":107,"_51":-5,"_53":-5,"_55":56},"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. 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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",[458],{"_78":459,"_392":456,"_393":31,"_172":460},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[463],{"_78":464,"_392":461,"_393":31,"_172":465},"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",[468],{"_78":469,"_392":466,"_393":31,"_172":429},"R","6sc.co",[472],{"_78":473,"_392":470,"_393":31,"_172":474},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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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,[577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601],[548,549,550,551,552],{"_386":461,"_213":461,"_172":173,"_359":572},{"_386":435,"_213":435,"_172":173,"_359":567},{"_386":562,"_213":562,"_172":173,"_359":563},{"_386":557,"_213":557,"_172":173,"_359":558},{"_386":553,"_213":553,"_172":173,"_359":554},"nvidia.partners",[555],{"_78":556,"_392":553,"_393":31,"_172":429},"edge","app.hushly.com",[559],{"_78":560,"_392":557,"_393":31,"_172":561},"*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",[564],{"_78":565,"_392":562,"_393":31,"_172":566},"_octo","This domain is owned by Github. 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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.",[660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688],[640,641,642,643],{"_386":655,"_213":655,"_172":173,"_359":656},{"_386":650,"_213":650,"_172":173,"_359":651},{"_386":448,"_213":448,"_172":173,"_359":647},{"_386":562,"_213":562,"_172":173,"_359":644},[645],{"_78":646,"_392":562,"_393":91,"_172":566},"_gh_sess",[648],{"_78":649,"_392":448,"_393":91,"_172":455},"YSC","bizible.com",[652],{"_78":653,"_392":650,"_393":31,"_172":654},"_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",[657],{"_78":658,"_392":655,"_393":31,"_172":659},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_78":730,"_392":507,"_393":31,"_172":731},{"_78":715,"_392":729,"_393":91,"_172":716},{"_78":698,"_392":510,"_393":31,"_172":623},{"_78":698,"_392":611,"_393":31,"_172":623},{"_78":727,"_392":507,"_393":31,"_172":728},{"_78":715,"_392":513,"_393":91,"_172":716},{"_78":725,"_392":507,"_393":31,"_172":726},{"_78":715,"_392":510,"_393":91,"_172":716},{"_78":723,"_392":610,"_393":31,"_172":724},{"_78":715,"_392":632,"_393":91,"_172":716},{"_78":695,"_392":510,"_393":31,"_172":697},{"_78":721,"_392":507,"_393":91,"_172":722},{"_78":719,"_392":507,"_393":31,"_172":720},{"_78":717,"_392":507,"_393":91,"_172":718},{"_78":715,"_392":606,"_393":91,"_172":716},{"_78":713,"_392":507,"_393":31,"_172":714},{"_78":711,"_392":507,"_393":31,"_172":712},{"_78":709,"_392":507,"_393":91,"_172":710},{"_78":707,"_392":615,"_393":31,"_172":708},{"_78":695,"_392":606,"_393":31,"_172":697},{"_78":705,"_392":507,"_393":31,"_172":706},{"_78":703,"_392":507,"_393":91,"_172":704},{"_78":701,"_392":507,"_393":31,"_172":702},{"_78":699,"_392":507,"_393":31,"_172":700},{"_78":698,"_392":507,"_393":31,"_172":623},{"_78":695,"_392":696,"_393":31,"_172":697},{"_78":693,"_392":507,"_393":31,"_172":694},{"_78":691,"_392":507,"_393":91,"_172":692},{"_78":689,"_392":507,"_393":31,"_172":690},"_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. 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These cookies are required for the site to function and cannot be turned off.",[877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949],[737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761],{"_386":874,"_213":874,"_172":173,"_359":875},{"_386":868,"_213":868,"_172":173,"_359":869},{"_386":865,"_213":865,"_172":173,"_359":866},{"_386":853,"_213":853,"_172":173,"_359":854},{"_386":850,"_213":850,"_172":173,"_359":851},{"_386":847,"_213":847,"_172":173,"_359":848},{"_386":842,"_213":842,"_172":173,"_359":843},{"_386":448,"_213":448,"_172":173,"_359":839},{"_386":836,"_213":836,"_172":173,"_359":837},{"_386":440,"_213":440,"_172":173,"_359":834},{"_386":435,"_213":435,"_172":173,"_359":832},{"_386":827,"_213":827,"_172":173,"_359":828},{"_386":822,"_213":822,"_172":173,"_359":823},{"_386":817,"_213":817,"_172":173,"_359":818},{"_386":814,"_213":814,"_172":173,"_359":815},{"_386":562,"_213":562,"_172":173,"_359":811},{"_386":420,"_213":420,"_172":173,"_359":808},{"_386":557,"_213":557,"_172":173,"_359":801},{"_386":553,"_213":553,"_172":173,"_359":795},{"_386":789,"_213":789,"_172":173,"_359":790},{"_386":784,"_213":784,"_172":173,"_359":785},{"_386":779,"_213":779,"_172":173,"_359":780},{"_386":775,"_213":775,"_172":173,"_359":776},{"_386":770,"_213":770,"_172":173,"_359":771},{"_386":762,"_213":762,"_172":173,"_359":763},"polyfill.io",[764,765],{"_78":768,"_392":762,"_393":31,"_172":769},{"_78":766,"_392":762,"_393":31,"_172":767},"cf_ob_info","This cookie is associated with sites using CloudFlare. 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Name	Size	Updated	Actions
libnvsalienc.a	262.97 MB	January 7, 2026 UTC	curl wget Download
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