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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",{"_258":259,"_260":16,"_261":262,"_263":20,"_264":265},"isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[252],"displayLabels",[243,244],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_210":250,"_246":250,"_247":251,"_73":79},{"_210":245,"_246":245,"_247":248,"_73":79},"NSPECT-5B3X-9CIG","value","filter",{"_249":79,"_246":245},"field","False",{"_249":79,"_246":250},{"_210":253,"_246":254,"_247":255,"_73":256},"NVIDIA NIM","nimmcro_nvidia_nim",{"_249":257,"_246":254},"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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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. 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