Model Overview

Description:

The NVIDIA Qwen3.8-27B NVFP4 model is a quantized version of Alibaba's Qwen3.8-27B model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information on the model, please check here. The model is quantized with Model Optimizer.

This model is ready for commercial or 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 (Qwen3.8-27B) Model Card from Qwen.

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.

Release Date:

Hugging Face 09/08/2026 via https://huggingface.co/nvidia/Qwen3.8-27B-NVFP4

References

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

Model Architecture:

Architecture Type: Transformer
Network Architecture: Qwen3.8-27B (Qwen3_5ForConditionalGeneration)
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: One-Dimensional (1D): 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.

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):

This checkpoint uses mixed NVFP4/FP8 quantization and was produced with nvidia-modelopt v0.48.0.

Training and Evaluation Datasets:

Calibration Dataset:

Link: Nemotron-Post-Training-Dataset-v3
Data Modality: Text
Data Collection Method by dataset: Varies by dataset.
Labeling Method by dataset: Varies by dataset.
Properties: The Nemotron-Post-Training-Dataset-v3 is a multi-million-sample corpus developed by NVIDIA for Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to power alignment, reasoning, and agentic capabilities in the Nemotron-3 model family.

Training Dataset:

Data Modality: Undisclosed
Data Collection Method by dataset: Undisclosed
Labeling Method by dataset: Undisclosed
Properties: Undisclosed
Image Training Data Size: Undisclosed
Text Training Data Size: Undisclosed
Video Training Data Size: Undisclosed

Evaluation Dataset:

Datasets: GPQA Diamond, Terminal-Bench, AA-LCR, MMMU-Pro, SciCode, IFBench
Data Collection Method by dataset: Hybrid: Automated, Manually-Collected
Labeling Method by dataset: Hybrid: Manually-Labeled, Automated
Properties: We evaluated the model on text-based reasoning, coding, agentic tasks, long-context recall, instruction following, and multimodal reasoning. GPQA Diamond contains graduate-level multiple-choice questions written by domain experts in biology, physics, and chemistry. Terminal-Bench evaluates agents on terminal-based tasks. AA-LCR (Artificial Analysis Long Context Recall) evaluates a model's ability to accurately retrieve and recall information from long input contexts. MMMU-Pro is a challenging multimodal understanding benchmark that measures college-level reasoning across diverse disciplines. SciCode evaluates scientific coding capabilities. IFBench evaluates instruction-following capabilities across diverse and structured task constraints.

Inference:

Acceleration Engine: vLLM
Test Hardware: NVIDIA Grace Blackwell GB300

Post-Training Quantization

Qwen3.8-27B was quantized using a mixed-precision recipe. NVFP4 quantization was applied to the MLP layers and language model head (lm_head), while FP8 quantization was applied to the self-attention and linear-attention layers. The NVFP4 layers were calibrated on 2,048 samples using the Model Optimizer Local-Hessian calibration algorithm.

Usage

To serve this checkpoint with vLLM, you can start the docker vllm/vllm-openai:nightly and run the sample command below:

vllm serve nvidia/Qwen3.8-27B-NVFP4 \
    --port 8000 \
    --kv-cache-dtype fp8_e4m3 \
    --tensor-parallel-size 4 \
    --max-model-len 262144 \
    --reasoning-parser qwen3 \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_coder \
    --mm-encoder-tp-mode data \
    --seed 0 \
    --gpu-memory-utilization 0.85 \
    --max-num-seqs 32 \
    --max-num-batched-tokens 32768 \
    --enable-chunked-prefill

To serve this checkpoint with SGLang, you can start the docker lmsysorg/sglang:dev and run the sample command below:

sglang serve \
  --trust-remote-code \
  --model-path nvidia/Qwen3.8-27B-NVFP4 \
  --kv-cache-dtype fp8_e4m3 \
  --mem-fraction-static 0.85 \
  --chunked-prefill-size 2048 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder \
  --mamba-full-memory-ratio 4.59 \
  --host 0.0.0.0 \
  --port 30000 \
  --mamba-radix-cache-strategy extra_buffer \
  --mamba-ssm-dtype float32

For more details please refer to SGLang cookbook.

Evaluation

The accuracy benchmark results are presented in the table below:

Benchmark	Qwen3.8-27B BF16	Qwen3.8-27B NVFP4
GPQA Diamond	88.92	88.01
Terminal-Bench	75.56	74.02
AA-LCR	72.63	73.38
MMMU-Pro	75.14	74.86
SciCode	47.93	48.41
IFBench	80.07	78.93

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 link.