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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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GLM-5.3-Flash

Description

GLM-5.3-Flash is a natively multimodal mixture-of-experts model with 320B total parameters and 18B active parameters. It uses a hybrid architecture combining sparse and linear attention and Manifold-Constrained Hyper-Connections for coding, reasoning, agentic, and multimodal workloads.

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

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. It was developed by Z.ai; see link to Non-NVIDIA GLM-5.3-Flash Model Card.

License and Terms of Use

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

Deployment Geography

Global

Use Case

Developers can use GLM-5.3-Flash for coding, reasoning, agentic workflows, long-context text processing, and image or video understanding.

Release Date

NGC 08/31/2026 via GLM-5.3-Flash on NGC

Reference(s)

Model Architecture

Architecture Type: Transformer\nNetwork Architecture: Mixture-of-Experts with hybrid sparse and linear attention and Manifold-Constrained Hyper-Connections\nTotal Parameters: 320B\nActive Parameters: 18B\nVocabulary Size: 154,880

Input

Input Types: Text, Image, Video\nInput Formats: Text: String; Image: Red, Green, Blue (RGB); Video: mp4, mov, webm\nInput Parameters: Text: One-Dimensional (1D); Image: Two-Dimensional (2D); Video: Three-Dimensional (3D)\nOther Input Properties: Supports configurable reasoning effort.\nInput Context Length (ISL): Up to 1,048,576 tokens

Output

Output Types: Text\nOutput Format: String\nOutput Parameters: One-Dimensional (1D)\nOther Output Properties: Autoregressive text generation.

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:

vLLM

Supported Hardware:

NVIDIA Blackwell - B200 Tensor Core GPU
NVIDIA Hopper - H20 Tensor Core GPU
NVIDIA Hopper - H200 Tensor Core GPU

Precision: FP8

Supported Operating System(s): Linux

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)

GLM-5.3-Flash

Training, Testing, and Evaluation Datasets

Training Dataset

Data Modality: Text, Image, Video\nText Training Data Size: [More than 10 Trillion Tokens]\nImage Training Data Size: Undisclosed\nVideo Training Data Size: Undisclosed\nData Collection Method by dataset: Undisclosed\nLabeling Method by dataset: Undisclosed\nProperties: A 30T-token multimodal pre-training corpus.

Testing Dataset

Data Collection Method by dataset: Undisclosed\nLabeling Method by dataset: Undisclosed\nProperties: Undisclosed

Evaluation Dataset

Data Collection Method by dataset: [Hybrid: Automated, Manually-Collected]\nLabeling Method by dataset: [Hybrid: Automated, Manually-Labeled]\nProperties: Evaluated on coding, agentic, reasoning, long-context, and multimodal benchmarks including Terminal-Bench 2.1, DeepSWE, HLE, NL2Repo, and BabyVision.

Inference

Acceleration Engine: vLLM

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.

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.

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.

Get Help

Getting started with the NIM

Deploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the NIM documentation for release documentation, deployment guides and more.

NVIDIA Developer Community Forum

Get access to community knowledge base articles and support cases (NVIDIA Developer Forums).

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It uses a hybrid architecture combining sparse and linear attention and Manifold-Constrained Hyper-Connections for coding, reasoning, agentic, and multimodal workloads.\n\nThis model is ready for commercial or non-commercial use.\n\n## Third-Party Community Consideration\nThis model is not owned or developed by NVIDIA. It was developed by Z.ai; see link to Non-NVIDIA [GLM-5.3-Flash Model Card](https://huggingface.co/zai-org/GLM-5.3-Flash).\n\n## License and Terms of Use\n**GOVERNING DOWNLOAD TERMS:** Use of the 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/zai-org/GLM-5.3-Flash/blob/main/LICENSE).\n\n## Deployment Geography\nGlobal\n\n## Use Case\nDevelopers can use GLM-5.3-Flash for coding, reasoning, agentic workflows, long-context text processing, and image or video understanding.\n\n## Release Date\n**NGC** 08/31/2026 via [GLM-5.3-Flash on NGC](https://catalog.ngc.nvidia.com/orgs/nim/teams/zai-org/models/glm-5.3-flash)\n\n## Reference(s)\n* [Z.ai GLM-5.3-Flash Model Card](https://huggingface.co/zai-org/GLM-5.3-Flash)\n* [GLM-5 Technical Report](https://arxiv.org/abs/2602.15763)\n* [GLM-5.3-Flash Announcement](https://z.ai/blog/glm-5.3-flash)\n\n## Model Architecture\n**Architecture Type:** Transformer\n**Network Architecture:** Mixture-of-Experts with hybrid sparse and linear attention and Manifold-Constrained Hyper-Connections\n**Total Parameters:** 320B\n**Active Parameters:** 18B\n**Vocabulary Size:** 154,880\n\n### Input\n**Input Types:** Text, Image, Video\n**Input Formats:** Text: String; Image: Red, Green, Blue (RGB); Video: mp4, mov, webm\n**Input Parameters:** Text: One-Dimensional (1D); Image: Two-Dimensional (2D); Video: Three-Dimensional (3D)\n**Other Input Properties:** Supports configurable reasoning effort.\n**Input Context Length (ISL):** Up to 1,048,576 tokens\n### Output\n**Output Types:** Text\n**Output Format:** String\n**Output Parameters:** One-Dimensional (1D)\n**Other Output Properties:** Autoregressive text generation.\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**Runtime Engines:**\n- vLLM\n\n**Supported Hardware:**\n- NVIDIA Blackwell - B200 Tensor Core GPU\n- NVIDIA Hopper - H20 Tensor Core GPU\n- NVIDIA Hopper - H200 Tensor Core GPU\n\n**Precision:** FP8\n\n**Supported Operating System(s):** Linux\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)\nGLM-5.3-Flash\n\n## Training, Testing, and Evaluation Datasets\n\n### Training Dataset\n**Data Modality:** Text, Image, Video\n**Text Training Data Size:** [More than 10 Trillion Tokens]\n**Image Training Data Size:** Undisclosed\n**Video Training Data Size:** Undisclosed\n**Data Collection Method by dataset:** Undisclosed\n**Labeling Method by dataset:** Undisclosed\n**Properties:** A 30T-token multimodal pre-training corpus.\n\n### Testing Dataset\n**Data Collection Method by dataset:** Undisclosed\n**Labeling Method by dataset:** Undisclosed\n**Properties:** Undisclosed\n\n### Evaluation Dataset\n**Data Collection Method by dataset:** [Hybrid: Automated, Manually-Collected]\n**Labeling Method by dataset:** [Hybrid: Automated, Manually-Labeled]\n**Properties:** Evaluated on coding, agentic, reasoning, long-context, and multimodal benchmarks including Terminal-Bench 2.1, DeepSWE, HLE, NL2Repo, and BabyVision.\n\n## Inference\n**Acceleration Engine:** vLLM\n\n**Test Hardware:**\n- NVIDIA B200 Tensor Core GPU\n- NVIDIA H20 Tensor Core GPU\n- NVIDIA H200 Tensor Core GPU\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. 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\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\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\n## Get Help\n\n### Getting started with the NIM\nDeploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the [NIM documentation](https://docs.nvidia.com/nim/vision-language-models/latest/introduction.html) for release documentation, deployment guides and more.\n\n### NVIDIA Developer Community Forum\nGet access to community knowledge base articles and support cases ([NVIDIA Developer Forums](https://forums.developer.nvidia.com/)).","displayName","GLM-5.3 Flash",["D",1788313160179],"publisher","z-AI","latestVersion","versions","collectionCounts","imageUrl","/api/image/nim/zai-org/models/glm-5.3-flash","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms",{"_262":263,"_264":16,"_265":266,"_267":20,"_268":269},"isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[256],"displayLabels",[244,245,246],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_211":254,"_248":254,"_249":255,"_72":76},{"_211":252,"_248":252,"_249":253,"_72":76},{"_211":247,"_248":247,"_249":250,"_72":76},"Signed Images","value","filter",{"_251":76,"_248":247},"field","NSPECT-EGPH-1NZV",{"_251":76,"_248":252},"False",{"_251":76,"_248":254},{"_211":257,"_248":258,"_249":259,"_72":260},"NVIDIA NIM","nimmcro_nvidia_nim",{"_251":261,"_248":258},"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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