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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-5.2-FP8 Overview

Description:

GLM-5.2 is a flagship model for long-horizon tasks, offering a solid 1M-token context. It features advanced coding capabilities with flexible effort levels, improved architecture through IndexShare (reusing indexers across sparse attention layers to reduce FLOPs), and enhanced MTP layer for speculative decoding. The model is open-source under MIT license with no regional limits.
\nGLM-5.2-FP8 was developed by Zhipu AI as a part of GLM.
\nThis model is ready for commercial/non-commercial use.

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA Zhipu AI Model Card.

License/Terms of Use:

MIT License

Deployment Geography:

Global

Use Case:

Developers building AI agent systems, chatbots, and applications requiring advanced reasoning and long-context understanding.

Release Date:

HuggingFace: 06/16/2026 via https://huggingface.co/zai-org/GLM-5.2-FP8

Reference(s):

GLM-5: from Vibe Coding to Agentic Engineering

Model Architecture:

Architecture Type: Transformer
\nNetwork Architecture: Mixture of Experts (MoE)
\nThis model was developed based on GLM-5.1.
\nNumber of model parameters: 753B

Input:

Input Type(s): Text
\nInput Format(s): String
\nInput Parameters: One-Dimensional (1D)
\nOther Properties Related to Input: Context length up to 1,048,576 tokens. Uses SentencePiece tokenizer with vocabulary size not specified.

Output:

Output Type(s): Text
\nOutput Format: String
\nOutput Parameters: One-Dimensional (1D)
\nOther Properties Related to Output: Maximum output length configurable via max_new_tokens parameter (default 4096, max 32768). Supports streaming output. UTF-8 encoded text.

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(s):

Transformers
vLLM
TensorRT-LLM

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Hopper

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.

(Optional) This AI model can be embedded as an Application Programming Interface (API) call into the software environment described above.

Model Version(s):

GLM-5.2-FP8

GLM-5.2 supports deployment with frameworks like SGLang, vLLM, Transformers, KTransformers, and Unsloth. Each framework has specific version requirements and documentation links provided.

Training, Testing, and Evaluation Datasets:

Training Dataset:

Data Modality: Text

Text Training Data Size: More than 1 Trillion Tokens
\nData Collection Method by dataset: Not Applicable
\nLabeling Method by dataset: Not Applicable
\nProperties (Quantity, Dataset Descriptions, Sensor(s)): Not Applicable (N/A)

Testing Dataset:

Data Collection Method by dataset: Not Applicable
\nLabeling Method by dataset: Not Applicable
\nProperties (Quantity, Dataset Descriptions, Sensor(s)): Not Applicable (N/A)

Evaluation Dataset:

Benchmark Score: HLE: 40.5, HLE (w/ Tools): 54.7, CritPt: 20.9, AIME 2026: 99.2, HMMT Nov. 2025: 94.4, HMMT Feb. 2026: 92.5, IMOAnswerBench: 91.0, GPQA-Diamond: 91.2, SWE-bench Pro: 62.1, NL2Repo: 48.9, DeepSWE: 46.2, ProgramBench: 63.7, Terminal Bench 2.1 (Terminus-2): 81.0, Terminal Bench 2.1 (Best Reported Harness): 82.7, FrontierSWE (Dominance): 74.4, PostTrainBench: 34.3, SWE-Marathon: 13.0, MCP-Atlas (Public Set): 76.8, Tool-Decathlon: 48.2

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\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\">

Benchmark	GLM-5.2	GLM-5.1	Qwen3.7-Max	MiniMax M3	DeepSeek-V4-Pro	Claude Opus 4.8	GPT-5.5	Gemini 3.1 Pro
Reasoning
HLE	40.5	31	41.4	37	37.7	49.8*	41.4*	45
HLE (w/ Tools)	54.7	52.3	53.5	-	48.2	57.9*	52.2*	51.4*
CritPt	20.9	4.6	13.4	3.7	12.9	20.9	27.1	17.7
AIME 2026	99.2	95.3	97	-	94.6	95.7	98.3	98.2
HMMT Nov. 2025	94.4	94	95	84.4	94.4	96.5	96.5	94.8
HMMT Feb. 2026	92.5	82.6	97.1	84.4	95.2	96.7	96.7	87.3
IMOAnswerBench	91.0	83.8	90	-	89.8	83.5	-	81
GPQA-Diamond	91.2	86.2	90	93	90.1	93.6	93.6	94.3
Coding
SWE-bench Pro	62.1	58.4	60.6	59	55.4	69.2	58.6	54.2
NL2Repo	48.9	42.7	47.2	42.1	35.5	69.7	50.7	33.4
DeepSWE	46.2	18	18	20	8	58	70	10
ProgramBench	63.7	50.9	-	-	47.8	71.9	70.8	39.5
Terminal Bench 2.1 (Terminus-2)	81.0	63.5	75	65	64	85	84	74
Terminal Bench 2.1 (Best Reported Harness)	82.7	69	-	-	-	78.9	83.4	70.7
FrontierSWE (Dominance)	74.4	30.5	-	-	29.0	75.1	72.6	39.6
PostTrainBench	34.3	20.1	-	-	-	37.2	28.4	21.6
SWE-Marathon	13.0	1.0	-	-	-	26.0	12.0	4.0
Agentic
MCP-Atlas (Public Set)	76.8	71.8	76.4	74.2	73.6	77.8	75.3	69.2
Tool-Decathlon	48.2	40.7	-	-	52.8	59.9	55.6	48.8

Data Collection Method by dataset: Hybrid: Automated/Human
\nLabeling Method by dataset: Hybrid: Automated/Human
\nProperties (Quantity, Dataset Descriptions, Sensor(s)): Evaluated on diverse reasoning benchmarks including mathematical problem solving, code generation challenges, question answering tasks, and instruction following assessments. Comprehensive evaluation across standard academic benchmarks for language understanding, mathematical reasoning, and code generation capabilities.

Inference:

Acceleration Engine: TensorRT-LLM\nTest Hardware: NVIDIA A100 80GB

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

Generated by NVIDIA Model Card Generator Toolkit.

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It features advanced coding capabilities with flexible effort levels, improved architecture through IndexShare (reusing indexers across sparse attention layers to reduce FLOPs), and enhanced MTP layer for speculative decoding. The model is open-source under MIT license with no regional limits. \nGLM-5.2-FP8 was developed by Zhipu AI as a part of GLM. \n_This model is ready for commercial/non-commercial use._ \n\n## Third-Party Community Consideration \nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA [Zhipu AI Model Card](https://huggingface.co/zai-org/GLM-5.2-FP8). \n\n### License/Terms of Use:\n[MIT License](https://opensource.org/licenses/MIT)\n### Deployment Geography: \nGlobal\n\n### Use Case: \nDevelopers building AI agent systems, chatbots, and applications requiring advanced reasoning and long-context understanding. \n \n\n### Release Date:\n**HuggingFace:** 06/16/2026 via https://huggingface.co/zai-org/GLM-5.2-FP8 \n\n \n\n## Reference(s):\n[GLM-5: from Vibe Coding to Agentic Engineering](https://arxiv.org/abs/2602.15763) \n\n## Model Architecture: \n**Architecture Type:** Transformer \n**Network Architecture:** Mixture of Experts (MoE) \n**This model was developed based on GLM-5.1.** \n**Number of model parameters:** 753B \n\n \n \n## Input: \n**Input Type(s):** Text \n**Input Format(s):** String \n**Input Parameters:** One-Dimensional (1D) \n**Other Properties Related to Input:** Context length up to 1,048,576 tokens. Uses SentencePiece tokenizer with vocabulary size not specified. \n \n\n## Output: \n**Output Type(s):** Text \n**Output Format:** String \n**Output Parameters:** One-Dimensional (1D) \n**Other Properties Related to Output:** Maximum output length configurable via max_new_tokens parameter (default 4096, max 32768). Supports streaming output. UTF-8 encoded text. \n \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 Engine(s):**\n* Transformers\n* vLLM\n* TensorRT-LLM\n\n**Supported Hardware Microarchitecture Compatibility:**\n* NVIDIA Ampere\n* NVIDIA Hopper\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(Optional) This AI model can be embedded as an Application Programming Interface (API) call into the software environment described above. \n\n## Model Version(s): \nGLM-5.2-FP8 \n\nGLM-5.2 supports deployment with frameworks like SGLang, vLLM, Transformers, KTransformers, and Unsloth. Each framework has specific version requirements and documentation links provided. \n \n## Training, Testing, and Evaluation Datasets: \n\n\n## Training Dataset:\n\n**Data Modality:** Text \n\n**Text Training Data Size:** More than 1 Trillion Tokens \n**Data Collection Method by dataset:** Not Applicable \n**Labeling Method by dataset:** Not Applicable \n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** Not Applicable (N/A)\n\n### Testing Dataset:\n\n**Data Collection Method by dataset:** Not Applicable \n**Labeling Method by dataset:** Not Applicable \n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** Not Applicable (N/A)\n\n### Evaluation Dataset:\n**Benchmark Score:** HLE: 40.5, HLE (w/ Tools): 54.7, CritPt: 20.9, AIME 2026: 99.2, HMMT Nov. 2025: 94.4, HMMT Feb. 2026: 92.5, IMOAnswerBench: 91.0, GPQA-Diamond: 91.2, SWE-bench Pro: 62.1, NL2Repo: 48.9, DeepSWE: 46.2, ProgramBench: 63.7, Terminal Bench 2.1 (Terminus-2): 81.0, Terminal Bench 2.1 (Best Reported Harness): 82.7, FrontierSWE (Dominance): 74.4, PostTrainBench: 34.3, SWE-Marathon: 13.0, MCP-Atlas (Public Set): 76.8, Tool-Decathlon: 48.2 \n| Benchmark | GLM-5.2 | GLM-5.1 | Qwen3.7-Max | MiniMax M3 | DeepSeek-V4-Pro | Claude Opus 4.8 | GPT-5.5 | Gemini 3.1 Pro |\n|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n| Reasoning | | | | | | | | | |\n| HLE | 40.5 | 31 | 41.4 | 37 | 37.7 | 49.8* | 41.4* | 45 |\n| HLE (w/ Tools) | 54.7 | 52.3 | 53.5 | - | 48.2 | 57.9* | 52.2* | 51.4* |\n| CritPt | 20.9 | 4.6 | 13.4 | 3.7 | 12.9 | 20.9 | 27.1 | 17.7 |\n| AIME 2026 | 99.2 | 95.3 | 97 | - | 94.6 | 95.7 | 98.3 | 98.2 |\n| HMMT Nov. 2025 | 94.4 | 94 | 95 | 84.4 | 94.4 | 96.5 | 96.5 | 94.8 |\n| HMMT Feb. 2026 | 92.5 | 82.6 | 97.1 | 84.4 | 95.2 | 96.7 | 96.7 | 87.3 |\n| IMOAnswerBench | 91.0 | 83.8 | 90 | - | 89.8 | 83.5 | - | 81 |\n| GPQA-Diamond | 91.2 | 86.2 | 90 | 93 | 90.1 | 93.6 | 93.6 | 94.3 |\n| Coding | | | | | | | | | |\n| SWE-bench Pro | 62.1 | 58.4 | 60.6 | 59 | 55.4 | 69.2 | 58.6 | 54.2 |\n| NL2Repo | 48.9 | 42.7 | 47.2 | 42.1 | 35.5 | 69.7 | 50.7 | 33.4 |\n| DeepSWE | 46.2 | 18 | 18 | 20 | 8 | 58 | 70 | 10 |\n| ProgramBench | 63.7 | 50.9 | - | - | 47.8 | 71.9 | 70.8 | 39.5 |\n| Terminal Bench 2.1 (Terminus-2) | 81.0 | 63.5 | 75 | 65 | 64 | 85 | 84 | 74 |\n| Terminal Bench 2.1 (Best Reported Harness) | 82.7 | 69 | - | - | - | 78.9 | 83.4 | 70.7 |\n| FrontierSWE (Dominance) | 74.4 | 30.5 | - | - | 29.0 | 75.1 | 72.6 | 39.6 |\n| PostTrainBench | 34.3 | 20.1 | - | - | - | 37.2 | 28.4 | 21.6 |\n| SWE-Marathon | 13.0 | 1.0 | - | - | - | 26.0 | 12.0 | 4.0 |\n| Agentic | | | | | | | | | |\n| MCP-Atlas (Public Set) | 76.8 | 71.8 | 76.4 | 74.2 | 73.6 | 77.8 | 75.3 | 69.2 |\n| Tool-Decathlon | 48.2 | 40.7 | - | - | 52.8 | 59.9 | 55.6 | 48.8 | \n\n**Data Collection Method by dataset:** Hybrid: Automated/Human \n**Labeling Method by dataset:** Hybrid: Automated/Human \n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** Evaluated on diverse reasoning benchmarks including mathematical problem solving, code generation challenges, question answering tasks, and instruction following assessments. Comprehensive evaluation across standard academic benchmarks for language understanding, mathematical reasoning, and code generation capabilities.\n\n\n\n## Inference:\n**Acceleration Engine:** TensorRT-LLM\n**Test Hardware:** NVIDIA A100 80GB\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. 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GOVERNING DOWNLOAD TERMS: Use of the model is governed by the NVIDIA Open Model Agreement.

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