nvidia/Qwen3.5-397B-A17B-NVFP4-V2

Model Overview

Description:

The NVIDIA Qwen3.5-397B-A17B NVFP4 V2 model is the quantized version of Alibaba's Qwen3.5-397B-A17B model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Qwen3.5-397B-A17B NVFP4 V2 model is quantized with Model Optimizer.

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

References

Nvidia Model Optimizer: https://github.com/NVIDIA/Model-Optimizer

License/Terms of Use:

Apache license 2.0

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:

Hugging Face 06/29/2026 via https://huggingface.co/nvidia/Qwen3.5-397B-A17B-NVFP4-V2

Model Architecture:

Architecture Type: Transformers
Network Architecture: Qwen3.5-397B-A17B
Number of Model Parameters: 397B in total and 17B activated

Input:

Input Type(s): Text, Image, Video
Input Format(s): String, Red, Green, Blue (RGB), Video (MP4/WebM)
Input Parameters: One-Dimensional (1D), Two-Dimensional (2D), Three-Dimensional (3D)
Other Properties Related to Input: Context length up to 262K

Output:

Output Type(s): Text
Output Format: String
Output Parameters: 1D (One-Dimensional): Sequences
Other Properties Related to Output: Context length up to 262K

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):

• 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 2.0 version and is quantized with nvidia-modelopt v0.45.0.dev173+g52f1ccbee

Training and Evaluation Datasets:

Calibration Dataset:

Link: cnn_dailymail, Nemotron-Post-Training-Dataset-v2
Data Collection Method by dataset: Automated.
Labeling Method by dataset: 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. The Nemotron-Post-Training-Dataset-v2 is a post-training dataset curated by NVIDIA containing multi-turn conversations across diverse topics.

Training Dataset:

Data Modality: Undisclosed
Data Collection Method by dataset: Undisclosed
Labeling Method by dataset: Undisclosed
Properties: Undisclosed
Audio Training Data Size: Undisclosed
Image Training Data Size: Undisclosed
Text Training Data Size: Undisclosed
Video Training Data Size: Undisclosed
Non-Audio, Image, Text Training Data Size: Undisclosed

Evaluation Dataset:

• Datasets: MMMU Pro, GPQA Diamond, SciCode, AA-LCR, IFBench, Tau2 Bench Telecom
  ** Data Collection Method by dataset: Hybrid: Automated, Human
  ** Labeling Method by dataset: Hybrid: Human, Automated
  ** Properties: We evaluated the model on multimodal reasoning, coding, agentic tool-use, and instruction-following benchmarks: MMMU Pro is the more challenging version of the Massive Multi-discipline Multimodal Understanding benchmark, measuring college-level multimodal reasoning across diverse disciplines with expanded answer choices and a vision-only input setting; GPQA Diamond contains 448 graduate-level multiple-choice questions written by domain experts in biology, physics, and chemistry; SciCode evaluates scientific coding capabilities; AA-LCR (Artificial Analysis Long Context Recall) evaluates a model's ability to accurately retrieve and recall information from long input contexts; IFBench is a benchmark for evaluating instruction-following capabilities across diverse and structured task constraints; Tau2 Bench Telecom evaluates agentic tool-use and policy-adherence capabilities in dual-control telecom customer-service scenarios where the model interacts with a simulated user and external tools to resolve account issues.
  

Inference:

Acceleration Engine: SGLang
Test Hardware: B300

Post Training Quantization

This model was obtained by quantizing the weights and activations of Qwen3.5-397B-A17B to NVFP4 data type, ready for inference with SGLang. The routed experts are quantized to NVFP4 using MSE based scale setting while the attention and shared experts are quantized to per-tensor FP8 format. Only the weights and activations of the linear operators within transformer blocks are quantized.

Usage

To serve this checkpoint with SGLang, you can start the docker lmsysorg/sglang:v0.5.12.post1-cu130 and run the sample command below:

python3 -m sglang.launch_server --model nvidia/Qwen3.5-397B-A17B-NVFP4 --tensor-parallel-size 4 --quantization modelopt_mixed --disable-radix-cache --trust-remote-code

Tip: add --mamba-ssm-dtype bfloat16 to store the Mamba SSM state in BF16 (default is FP32) for faster decode on Blackwell.

Evaluation

The accuracy benchmark results are presented in the table below:

Precision	MMMU Pro	GPQA Diamond	SciCode	AA-LCR	IFBench	Tau2 Bench Telecom
FP8	0.787	0.872	0.467	0.688	0.761	0.954
NVFP4 V2	0.784	0.877	0.481	0.678	0.765	0.952

Baseline: Qwen3.5-397B-A17B-FP8. Benchmarked with temperature=0.6, top_p=0.95, max num tokens 64000. For tau2 bench telecom we use max num tokens 128000

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.

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

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

SUBCARDS:

Explainability

Field:	Response:
Intended Task/Domain:	Text generation, reasoning, summarization, and question answering.
Model Type:	Text and Image-to-text transformer
Intended Users:	This model is intended for developers, researchers, and customers building/utilizing LLMs, while balancing accuracy and efficiency.
Output:	Text String(s)
Describe how the model works:	Generates text by predicting the next word or token based on the context provided in the input sequence using multiple self-attention layers
Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:	Not Applicable
Technical Limitations & Mitigation:	The 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. Therefore, before deploying any applications of this model, developers should perform safety testing and tuning tailored to their specific applications of the model.
Verified to have met prescribed quality standards?	Yes
Performance Metrics:	Accuracy, Throughput, and user-side throughput
Potential Known Risk	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.
Licensing:	Your usage is governed by the following Apache license 2.0

Bias

Field:	Response:
Participation considerations from adversely impacted groups protected classes in model design and testing:	None
Measures taken to mitigate against unwanted bias:	None

Safety & Security

Field:	Response:
Model Application(s):	Chat, Instruction Following, Chatbot Development, Code Generation, Reasoning
Describe life critical application (if present):	Not Applicable
Use Case Restrictions:	Abide by the Apache license 2.0
Model and Dataset Restrictions:	The Principle of least privilege (PoLP) is applied limiting access for dataset generation. Restrictions enforce dataset access during training, and dataset license constraints adhered to. Model checkpoints are made available on Hugging Face, and may become available on cloud providers' model catalog.

Privacy

Field:	Response:
Generatable or Reverse engineerable personal data?	No
Personal data used to create this model?	No
Was consent obtained for any personal data used?	Not Applicable
How often is dataset reviewed?	Before Release
Was data from user interactions with the AI model (e.g. user input and prompts) used to train the model?	No
Is there provenance for all datasets used in training?	Yes
Does data labeling (annotation, metadata) comply with privacy laws?	Yes
Is data compliant with data subject requests for data correction or removal, if such a request was made?	Not Applicable
Applicable NVIDIA Privacy Policy	https://www.nvidia.com/en-us/about-nvidia/privacy-policy/