Model Overview
Description:
The NVIDIA GLM-5 NVFP4 model is the quantized version of ZAI’s GLM-5 model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA GLM-5 NVFP4 model is quantized with Model Optimizer.
This 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 (GLM-5) Model Card from ZAI.
References
Nvidia Model Optimizer: https://github.com/NVIDIA/Model-Optimizer
License/Terms of Use:
Deployment Geography:
Global
Use Case:
Developers looking to take off-the-shelf, pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.
Release Date:
Huggingface 03/16/2026 via https://huggingface.co/nvidia/GLM-5-NVFP4
Model Architecture:
Architecture Type: Transformers Network Architecture: GLM-5 Number of Model Parameters: 744B in total and 40B activated
Input:
Input Type(s): Text Input Format(s): String Input Parameters: One-Dimensional (1D) Other Properties Related to Input: Context length up to 200K
Output:
Output Type(s): Text Output Format: String Output Parameters: 1D (One-Dimensional): Sequences Other Properties Related to Output: N/A
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:
Supported Runtime Engine(s):
- vLLM
- SGLang
Supported Hardware Microarchitecture Compatibility:
- NVIDIA Blackwell
Preferred 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):
The model version is NVFP4 1.0 version and is quantized with nvidia-modelopt v0.42.0
Training, Testing, and Evaluation Datasets:
Calibration Dataset:
** Link: cnn_dailymail, Nemotron-Post-Training-Dataset-v2 ** Data Collection Method by dataset: Automated. ** Labeling method: Automated. ** Properties: The cnn_dailymail dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail.
Training Dataset:
** Data Modality: Undisclosed ** Data Collection Method by dataset: Undisclosed ** Labeling Method by dataset: Undisclosed ** Properties: Undisclosed
Testing Dataset:
** Data Collection Method by dataset: Undisclosed ** Labeling Method by dataset: Undisclosed ** Properties: Undisclosed
Evaluation Dataset:
** Data Collection Method by dataset: Hybrid: Human, Automated ** Labeling Method by dataset: Hybrid: Human, Automated ** Properties: We evaluated the model on benchmarks including GPQA, which is a dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry.
Inference:
Acceleration Engine: vLLM, SGLang Test Hardware: B300
Post Training Quantization
This model was obtained by quantizing the weights and activations of GLM-5 to NVFP4 data type, ready for inference with vLLM and SGLang. Only the weights and activations of the linear operators within transformer blocks in MoE are quantized.
Usage
To serve this checkpoint with vLLM, you can start the docker vllm/vllm-openai:latest and run the sample command below:
vllm serve nvidia/GLM-5-NVFP4 --tensor-parallel-size 8 --trust-remote-code --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm45 --enable-chunked-prefill --max-num-batched-tokens 131072 --gpu-memory-utilization 0.80
To serve this checkpoint with SGLang, you can start the docker lmsysorg/sglang:nightly-dev-cu13-20260305-33c92732 and run the sample command below (when the nightly docker becomes unavailable, use lmsysorg/sglang:latest):
python3 -m sglang.launch_server --model nvidia/GLM-5-NVFP4 --tensor-parallel-size 8 --quantization modelopt_fp4 --tool-call-parser glm47 --reasoning-parser glm45 --trust-remote-code --chunked-prefill-size 131072 --mem-fraction-static 0.80
If you would like to enable expert parallel when launch the SGLang endpoint, please build docker with provided dockerfile.
Evaluation
The accuracy benchmark results are presented in the table below:
Baseline: GLM-5-FP8. Benchmarked with temperature=1.0, top_p=0.95, max num tokens 131072
Model Limitations:
The base model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.
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.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.