Qwen3.5-397B-A17B

Description

Qwen3.5-397B-A17B is a multimodal foundation model featuring a Hybrid Mixture-of-Experts architecture with early fusion vision-language training, designed for state-of-the-art performance across chat, retrieval-augmented generation, vision-language understanding, video understanding, and agentic workflows. Key highlights include:

• Unified Vision-Language Foundation: Early fusion training on multimodal tokens achieves cross-generational parity with prior text-only models and outperforms dedicated vision-language models across reasoning, coding, agents, and visual understanding benchmarks.
• Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.
• Scalable RL Generalization: Reinforcement learning scaled across million-agent environments with progressively complex task distributions for robust real-world adaptability.
• Global Linguistic Coverage: Expanded support to 201 languages and dialects, enabling inclusive, worldwide deployment with nuanced cultural and regional understanding.
• Thinking Mode: Operates in thinking mode by default, generating internal reasoning content (<think>...</think>) before producing final responses, with an option to disable for direct responses.

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 Qwen3.5-397B-A17B Model Card

License and Terms of Use:

GOVERNING TERMS: Use of this model is governed by the NVIDIA Open Model License Agreement. Additional Information: Apache 2.0 License.

Deployment Geography:

Global

Use Case:

Use Case: Designed for developers and enterprises building multimodal AI applications including conversational chat, retrieval-augmented generation (RAG), vision-language understanding and reasoning, tool/function calling, and agentic workflows.

Release Date:

Build.NVIDIA.com: 02/16/2026 via link Huggingface: 02/16/2026 via link

Model Architecture:

Architecture Type: Transformer (Causal Language Model with Vision Encoder) Network Architecture: Qwen3.5 (Hybrid Mixture-of-Experts with Gated DeltaNet) Total Parameters: 397B Active Parameters: 17B Vocabulary Size: 248,320 Number of Layers: 60 Hidden Dimension: 4,096 Hidden Layout: 15 × (3 × (Gated DeltaNet → MoE) → 1 × (Gated Attention → MoE)) Gated DeltaNet: 64 linear attention heads for V, 16 for QK; head dimension 128 Gated Attention: 32 heads for Q, 2 for KV; head dimension 256; RoPE dimension 64 Mixture-of-Experts: 512 total experts; 10 routed + 1 shared activated per token; expert intermediate dimension 1,024 Multi-Token Prediction (MTP): Trained with multi-steps

Input:

Input Types: Text, Image, Video Input Formats: String, Red, Green, Blue (RGB), Video (MP4/WebM) Input Parameters: One-Dimensional (1D), Two-Dimensional (2D), Three-Dimensional (3D) Other Input Properties: Supports text, image, and video inputs via a unified multimodal architecture with early fusion training on multimodal tokens. Combines a Vision Transformer (ViT) encoder with a Hybrid MoE language model featuring Gated DeltaNet layers with global attention and fine-grained MoE routing (10 routed + 1 shared expert out of 512 total). Input Context Length (ISL): 262,144 tokens natively; extensible up to 1,010,000 tokens via YaRN RoPE scaling

Output:

Output Types: Text Output Format: String Output Parameters: One-Dimensional (1D) Other Output Properties: Generates text responses based on multimodal inputs. Operates in thinking mode by default (internal reasoning via <think>...</think> tags before final response; can be disabled). Natively supports tool/function calling, agentic workflows (Qwen-Agent, MCP servers), and multi-turn conversations. Output Context Length (OSL): Recommended 32,768 tokens for most queries; up to 81,920 tokens for complex reasoning tasks

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: Full support (supports MTP, tool calling, text-only mode, reasoning parser)
• Hugging Face Transformers: Lightweight serving support (continuous batching)

Supported Hardware:

• NVIDIA Ampere: A100
• NVIDIA Blackwell: B100, B200, GB200
• NVIDIA Hopper: H100, H200

Preferred/Supported Operating Systems: 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)

Qwen3.5-397B-A17B 2.0.6 EA

Training, Testing, and Evaluation Datasets:

Training Dataset

Data Modality: Image, Text, Video Training Data Collection: Undisclosed, Automated Training Labeling: Automated Training Properties: Trillions of multimodal tokens across image, text, and video domains, covering 201 languages and dialects. Specific dataset names and data licenses are not disclosed.

Testing Dataset

Testing Data Collection: Undisclosed Testing Labeling: Undisclosed Testing Properties: Undisclosed

Evaluation Dataset

Evaluation Data Collection: Public benchmark datasets as listed below Evaluation Labeling: Ground-truth labels from established benchmark suites Evaluation Properties: Evaluated in thinking mode with recommended sampling parameters (temperature=0.6, top_p=0.95, top_k=20)

Best Practices

To achieve optimal performance, Qwen recommends the following settings:

1. Sampling Parameters:

• Qwen suggests using Temperature=0.6, TopP=0.95, TopK=20, and MinP=0 for thinking mode and using Temperature=0.7, TopP=0.8, TopK=20, and MinP=0 for non-thinking mode.
• For supported frameworks, you can adjust the presence_penalty parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.

2. Adequate Output Length: Qwen recommends using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, Qwen suggests setting the max output length to 81,920 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.

3. Standardize Output Format: Qwen recommends using prompts to standardize model outputs when benchmarking.

• Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
• Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the answer field with only the choice letter, e.g., "answer": "C"."

4. No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.

5. Long Video Understanding: To optimize inference efficiency for plain text and images, the size parameter in the released video_preprocessor_config.json is conservatively configured. It is recommended to set the longest_edge parameter in the video_preprocessor_config file to 469,762,048 (corresponding to 224k video tokens) to enable higher frame-rate sampling for hour-scale videos and thereby achieve superior performance. For example,

{"longest_edge": 469762048, "shortest_edge": 4096}

Alternatively, override the default values via engine startup parameters. For implementation details, refer to: vLLM.

Inference

Acceleration Engine: vllm Test Hardware: NVIDIA B200 GPU Recommended Serving Configuration: TP=8, context length 262,144, reasoning parser qwen3; supports Multi-Token Prediction (MTP) for improved throughput

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 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.

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