The NVIDIA Qwen3.6-27B NVFP4 model is the quantized version of Alibaba's Qwen3.6-27B model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Qwen3.6-27B NVFP4 model is quantized with Model Optimizer.
This model is ready for commercial or non-commercial use.
License/Terms of Use:
GOVERNING DOWNLOAD TERMS: Use of the model is governed by the Apache 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.
Architecture Type: Transformers Network Architecture: Hybrid Attention (Gated DeltaNet and Gated Attention) Number of Model Parameters: 27B
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: None
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.
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 v0.45.0
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
Evaluation Dataset:
Datasets: MMLU Pro, GPQA Diamond, HLE, τ²-Bench Telecom, MMMU Pro, SciCode, AIME 2025, AA-LCR, IFBench Data Collection Method by dataset: Hybrid: Automated, Human Labeling Method by dataset: Hybrid: Human, Automated Properties: We evaluated the model on text-based reasoning, coding, agentic tool-use, and multimodal benchmarks: MMLU Pro is a multi-task language understanding benchmark with challenging multiple-choice questions across diverse academic domains; GPQA Diamond contains 448 graduate-level multiple-choice questions written by domain experts in biology, physics, and chemistry; HLE (Humanity's Last Exam) is an expert-level academic benchmark with 2158 text-only questions across mathematics, humanities and the natural sciences; τ²-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; 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; SciCode evaluates scientific coding capabilities; AIME 2025 contains problems from the American Invitational Mathematics Examination; 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.
Inference:
Acceleration Engine: vLLM Test Hardware: NVIDIA GB300
Post Training Quantization
This model was obtained by quantizing the weights and activations of Qwen3.6-27B to NVFP4 data type, ready for inference with vLLM. Only the weights and activations of the linear operators within transformer blocks are quantized. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 2.5x.
Usage
To serve this checkpoint with vLLM, you can start the docker vllm/vllm-openai:nightly and run the sample command below:
The accuracy benchmark results are presented in the table below:
Precision
MMLU Pro
GPQA Diamond
HLE
τ²-Bench Telecom
MMMU Pro
SciCode
AIME 2025
AA-LCR
IFBench
FP8
86.1
86.0
21.7
95.2
74.6
44.8
93.1
68.8
65.1
NVFP4
86.3
85.5
21.8
95.4
74.3
44.5
92.7
68.3
65.5
Baseline: Qwen3.6-27B-FP8. Benchmarked with temperature=1.0, top_p=0.95, max_num_tokens=81920 except SciCode with temperature=0.6, and τ²-Bench Telecom with temperature=0.0 and top_p=1.0.
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. 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.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.
