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GOVERNING DOWNLOAD TERMS: Use of the model is governed by the NVIDIA Open Model Agreement. ADDITIONAL INFORMATION: MIT License.

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DeepSeek-V4.1-Flash

Description

DeepSeek-V4.1-Flash is a multimodal Mixture-of-Experts model for long-context\nreasoning, coding, agentic workflows, and visual understanding. It accepts text\nand image inputs and generates text.

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

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. DeepSeek AI developed and built\nthe model for this application and use case; see link to Non-NVIDIA\nDeepSeek AI DeepSeek-V4.1-Flash Model Card.

License and Terms of Use

GOVERNING TERMS: Use of this container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for NVIDIA AI Products; and the use of this model is governed by the NVIDIA Open Model Agreement. ADDITIONAL INFORMATION: MIT License.

Deployment Geography

Global

Use Case

Developers and enterprises can use DeepSeek-V4.1-Flash to build multimodal\nassistants, long-context analysis systems, coding applications, visual\nunderstanding workflows, and agentic applications.

Release Date

NGC 09/30/2026 via DeepSeek-V4.1-Flash model on NGC

Reference(s)

DeepSeek-V4.1-Flash model page

Model Architecture

Architecture Type: Transformer

Network Architecture: A 40-layer causal encoder-decoder multimodal\nMixture-of-Experts architecture with a 20-layer causal encoder and a 20-layer\ndecoder.

Total Parameters: 552B backbone parameters, plus 196B sparsely accessed\nEngram conditional-memory parameters

Active Parameters: 8B per token during prefill and 16B per token during\ndecode

Input

Input Types: Text, Image

Input Formats: Text: String; Image: Red, Green, Blue (RGB)

Text Input Parameters: One-Dimensional (1D)

Image Input Parameters: Two-Dimensional (2D)

Other Input Properties: Text prompts, chat messages, image inputs, and\ninterleaved image-and-text inputs. The model supports context lengths up to 1M\ntokens.

Output

Output Types: Text

Output Format: String

Output Parameters: One-Dimensional (1D)

Other Output Properties: Generated natural language, structured text,\ncode, and image-grounded answers.

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 Engines:

SGLang

Operating Systems: Linux

Supported Hardware: NVIDIA B200 Tensor Core GPU, NVIDIA H20 Tensor Core GPU, NVIDIA H200 Tensor Core GPU

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s)

DeepSeek-V4.1-Flash 2.1.4-variant. The model can be integrated into AI systems\nthrough the NIM's OpenAI-compatible APIs.

Training, Testing, and Evaluation Datasets

Training Dataset

Data Modality: Text and image

Text Training Data Size: [More than 10 Trillion Tokens]

Image Training Data Size: Undisclosed

Data Collection Method by dataset: Undisclosed

Labeling Method by dataset: Undisclosed

Properties: Data count: 45T tokens; modalities: text and image; content\nnature: Undisclosed; linguistic characteristics: Undisclosed.

Testing Dataset

Data Collection Method by dataset: Undisclosed

Labeling Method by dataset: Undisclosed

Properties: Data count: Undisclosed; modalities: text and image; content\nnature: Undisclosed; linguistic characteristics: Undisclosed.

Evaluation Dataset

Data Collection Method by dataset: Undisclosed

Labeling Method by dataset: Undisclosed

Properties: Data count: Undisclosed; modalities: text and image; content\nnature: Undisclosed; linguistic characteristics: Undisclosed.

Inference

Runtime: SGLang

Acceleration Engine: SGLang

Precision: 8-bit floating-point (FP8)

Test Hardware:

NVIDIA B200 Tensor Core GPU
NVIDIA H20 Tensor Core GPU
NVIDIA H200 Tensor Core GPU

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. Developers should work with their internal developer team to ensure this model meets requirements for the relevant industry and use case and address unforeseen product misuse.

Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.

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

Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.

Get Help

NVIDIA Developer Community Forum

Get access to community knowledge base articles and support cases through the NVIDIA Developer Forums.

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It accepts text\nand image inputs and generates text.\n\nThis model is ready for commercial or non-commercial use.\n\n## Third-Party Community Consideration\n\nThis model is not owned or developed by NVIDIA. DeepSeek AI developed and built\nthe model for this application and use case; see link to Non-NVIDIA\n[DeepSeek AI DeepSeek-V4.1-Flash Model Card](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash).\n\n## License and Terms of Use\n\n**GOVERNING TERMS:** Use of this container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of this model is governed by the [NVIDIA Open Model Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/). **ADDITIONAL INFORMATION:** [MIT License](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash).\n\n## Deployment Geography\n\nGlobal\n\n## Use Case\n\nDevelopers and enterprises can use DeepSeek-V4.1-Flash to build multimodal\nassistants, long-context analysis systems, coding applications, visual\nunderstanding workflows, and agentic applications.\n\n## Release Date\n\n**NGC** 09/30/2026 via [DeepSeek-V4.1-Flash model on NGC](https://catalog.ngc.nvidia.com/orgs/nim/teams/deepseek-ai/models/deepseek-v4.1-flash)\n\n## Reference(s)\n\n- [DeepSeek-V4.1-Flash model page](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash)\n\n## Model Architecture\n\n**Architecture Type:** Transformer\n\n**Network Architecture:** A 40-layer causal encoder-decoder multimodal\nMixture-of-Experts architecture with a 20-layer causal encoder and a 20-layer\ndecoder.\n\n**Total Parameters:** 552B backbone parameters, plus 196B sparsely accessed\nEngram conditional-memory parameters\n\n**Active Parameters:** 8B per token during prefill and 16B per token during\ndecode\n\n### Input\n\n**Input Types:** Text, Image\n\n**Input Formats:** Text: String; Image: Red, Green, Blue (RGB)\n\n**Text Input Parameters:** One-Dimensional (1D)\n\n**Image Input Parameters:** Two-Dimensional (2D)\n\n**Other Input Properties:** Text prompts, chat messages, image inputs, and\ninterleaved image-and-text inputs. The model supports context lengths up to 1M\ntokens.\n\n### Output\n\n**Output Types:** Text\n\n**Output Format:** String\n\n**Output Parameters:** One-Dimensional (1D)\n\n**Other Output Properties:** Generated natural language, structured text,\ncode, and image-grounded answers.\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n## Software Integration\n\n**Runtime Engines:**\n\n- SGLang\n\n**Operating Systems:** Linux\n\n**Supported Hardware:** NVIDIA B200 Tensor Core GPU, NVIDIA H20 Tensor Core GPU, NVIDIA H200 Tensor Core GPU\n\nThe integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.\n\n## Model Version(s)\n\nDeepSeek-V4.1-Flash 2.1.4-variant. The model can be integrated into AI systems\nthrough the NIM's OpenAI-compatible APIs.\n\n## Training, Testing, and Evaluation Datasets\n\n### Training Dataset\n\n**Data Modality:** Text and image\n\n**Text Training Data Size:** [More than 10 Trillion Tokens]\n\n**Image Training Data Size:** Undisclosed\n\n**Data Collection Method by dataset:** Undisclosed\n\n**Labeling Method by dataset:** Undisclosed\n\n**Properties:** Data count: 45T tokens; modalities: text and image; content\nnature: Undisclosed; linguistic characteristics: Undisclosed.\n\n### Testing Dataset\n\n**Data Collection Method by dataset:** Undisclosed\n\n**Labeling Method by dataset:** Undisclosed\n\n**Properties:** Data count: Undisclosed; modalities: text and image; content\nnature: Undisclosed; linguistic characteristics: Undisclosed.\n\n### Evaluation Dataset\n\n**Data Collection Method by dataset:** Undisclosed\n\n**Labeling Method by dataset:** Undisclosed\n\n**Properties:** Data count: Undisclosed; modalities: text and image; content\nnature: Undisclosed; linguistic characteristics: Undisclosed.\n\n## Inference\n\n**Runtime:** SGLang\n\n**Acceleration Engine:** SGLang\n\n**Precision:** 8-bit floating-point (FP8)\n\n**Test Hardware:**\n\n- NVIDIA B200 Tensor Core GPU\n- NVIDIA H20 Tensor Core GPU\n- NVIDIA H200 Tensor Core GPU\n\n## Ethical Considerations\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer team to ensure this model meets requirements for the relevant industry and use case and address unforeseen product misuse.\n\nUsers are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\nPlease make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.\n\n## Get Help\n\n### NVIDIA Developer Community Forum\n\nGet access to community knowledge base articles and support cases through the [NVIDIA Developer Forums](https://forums.developer.nvidia.com/).","displayName","DeepSeek-V4.1-Flash",["D",1790803766860],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nim/deepseek-ai/models/deepseek-v4.1-flash","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms",{"_264":265,"_266":16,"_267":268,"_269":20,"_270":271},"isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[258],"displayLabels",[249,250],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_216":256,"_252":256,"_253":257,"_74":80},{"_216":251,"_252":251,"_253":254,"_74":80},"NSPECT-5B3X-9CIG","value","filter",{"_255":80,"_252":251},"field","False",{"_255":80,"_252":256},{"_216":259,"_252":260,"_253":261,"_74":262},"NVIDIA NIM","nimmcro_nvidia_nim",{"_255":263,"_252":260},"feature","nvidia_nim","governingTerms","

GOVERNING DOWNLOAD TERMS: Use of the model is governed by the NVIDIA Open Model Agreement. ADDITIONAL INFORMATION: MIT License.

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They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. 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,[501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538],[378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395],{"_396":496,"_216":496,"_172":173,"_368":497},{"_396":492,"_216":492,"_172":173,"_368":493},{"_396":487,"_216":487,"_172":173,"_368":488},{"_396":483,"_216":483,"_172":173,"_368":484},{"_396":478,"_216":478,"_172":173,"_368":479},{"_396":473,"_216":473,"_172":173,"_368":474},{"_396":466,"_216":466,"_172":173,"_368":467},{"_396":458,"_216":458,"_172":173,"_368":459},{"_396":453,"_216":453,"_172":173,"_368":454},{"_396":448,"_216":448,"_172":173,"_368":449},{"_396":440,"_216":440,"_172":173,"_368":441},{"_396":435,"_216":435,"_172":173,"_368":436},{"_396":430,"_216":430,"_172":173,"_368":431},{"_396":425,"_216":425,"_172":173,"_368":426},{"_396":418,"_216":418,"_172":173,"_368":419},{"_396":413,"_216":413,"_172":173,"_368":414},{"_396":406,"_216":406,"_172":173,"_368":407},{"_396":397,"_216":397,"_172":173,"_368":398},"hostName","amazon-adsystem.com",[399,400],{"_203":405,"_402":397,"_403":20,"_172":404},{"_203":401,"_402":397,"_403":20,"_172":404},"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",[408,409],{"_203":412,"_402":406,"_403":20,"_172":411},{"_203":410,"_402":406,"_403":20,"_172":411},"IDE","This domain is owned by Doubleclick (Google). 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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,[627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657],[593,594,595,596,597,598],{"_396":478,"_216":478,"_172":173,"_368":622},{"_396":453,"_216":453,"_172":173,"_368":617},{"_396":612,"_216":612,"_172":173,"_368":613},{"_396":607,"_216":607,"_172":173,"_368":608},{"_396":603,"_216":603,"_172":173,"_368":604},{"_396":599,"_216":599,"_172":173,"_368":600},"nvcrm.my.site.com",[601],{"_203":602,"_402":599,"_403":20,"_172":173},"BrowserId","nvidia.partners",[605],{"_203":606,"_402":603,"_403":20,"_172":439},"edge","app.hushly.com",[609],{"_203":610,"_402":607,"_403":20,"_172":611},"*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",[614],{"_203":615,"_402":612,"_403":20,"_172":616},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. 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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","omniverse.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. 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