nvidia/Qwen3.8-2.4T-A95B-NVFP4

Model Overview

Description:

Qwen3.8 delivers substantial gains across coding, professional work, research, and long-horizon agentic tasks. The NVIDIA Qwen3.8-2.4T-A95B-NVFP4 model is the quantized version of Alibaba's Qwen3.8-2.4T-A95B model.For more information, please check here. The NVIDIA Qwen3.8-2.4T-A95B-NVFP4 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 (Qwen3.8-2.4T-A95B) Model Card.

License/Terms of Use:

GOVERNING TERMS: Use of the model is governed by the NVIDIA Open Model Agreement. Additional Information: Qwen3.8-2.4T-A95B

Use Case:

Qwen3.8-2.4T-A95B delivers comprehensive improvements across coding, work, research, and long-horizon tasks.

Release Date:

Hugging Face 08/27/2026 via https://huggingface.co/nvidia/Qwen3.8-2.4T-A95B-NVFP4

References

• NVIDIA Model Optimizer: https://github.com/NVIDIA/Model-Optimizer
• Qwen3.8-2.4T-A95B model card

Deployment Geography:

Global

Model Architecture:

Architecture Type: Transformers
Network Architecture: Mixture-of-Experts (MoE) with Hybrid Attention and fine-grained MoE blocks
Number of Model Parameters: 2.4T in total and 95B activated

Input:

Input Type(s): Text
Input Format(s): String
Input Parameters: One-Dimensional (1D)
Other Properties Related to Input: ** Context length up to 1 million tokens

Output:

Output Type(s): Text
Output Format: String
Output Parameters: One-Dimensional (1D): Sequences
Other Properties Related to Output: Outputs may include natural-language responses, code, tool-calling content, and structured outputs depending on deployment configuration and application-level tooling.

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 and is quantized with NVIDIA Model Optimizer v0.46.0.

Training, Testing and Evaluation Datasets:

Calibration Dataset:

Link: Nemotron-Post-Training-Dataset-v2
Data Collection Method by dataset: Automated.
Labeling Method by dataset: Automated.
Properties: 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.

Testing Dataset:

Data Collection Method by dataset: Undisclosed
Labeling Method by dataset: Undisclosed
Data Size: Undisclosed
Properties: Undisclosed.

Evaluation Dataset:

Datasets: GPQA Diamond, SciCode, IFBench, AA-LCR, Terminal Bench 2.1
Data Collection Method by dataset: Hybrid: Automated, manually-collected
Labeling Method by dataset: Hybrid: manually-labelled, Automated
Properties: We evaluated the model on text-based reasoning, coding, long-context recall, and agentic tool-use benchmarks: GPQA Diamond contains 448 graduate-level multiple-choice questions written by domain experts in biology, physics, and chemistry; SciCode evaluates scientific coding capabilities; IFBench is a benchmark for evaluating instruction-following capabilities across diverse and structured task constraints; AA-LCR (Artificial Analysis Long Context Recall) evaluates a model's ability to accurately retrieve and recall information from long input contexts; 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.

Inference:

Acceleration Engine: vLLM, SGLang
Test Hardware: NVIDIA GB200, NVIDIA B300

Post Training Quantization

This model was obtained by quantizing the weights and activations of Qwen3.8-2.4T-A95B-NVFP4 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, run a command similar to the one below:

vllm serve nvidia/Qwen3.8-2.4T-A95B-NVFP4 \
  --port 8000 \
  --tensor-parallel-size 8 \
  --max-model-len 262144 \
  --reasoning-parser qwen3

To serve the same checkpoint with SGLang, run a command similar to:

python -m sglang.launch_server \
  --model-path nvidia/Qwen3.8-2.4T-A95B-NVFP4 \
  --port 8000 \
  --tp-size 8 \
  --context-length 262144 \
  --reasoning-parser qwen3

Evaluation

The accuracy benchmark results are presented in the table below:

Precision	GPQA Diamond	HLE	SciCode	AA-LCR	IFBench	Terminal Bench 2.1
Baseline(BF16)	92.55	41.43	54.44	71.5	79.93	76.03
NVFP4	92.58	40.55	56.21	71.63	81.73	76.4

Baseline: Qwen3.8-2.4T-A95B.Benchmarked with temperature=1.0, top_p=0.95, top_k=20. GPQA Diamond, SciCode, AA-LCR and IFBench used max_new_tokens=65,536; HLE used max_new_tokens=131,072; Terminal Bench 2.1 used max_new_tokens=262,144.

Model Limitations:

The base model may generate inaccurate, incomplete, or otherwise undesirable outputs, even when prompts are benign. Because this is a large reasoning-capable model, output quality can vary significantly with prompt construction, reasoning mode, tool configuration, and context length. The model may also reflect biases or content artifacts present in its upstream training data, so deployers should apply application-specific safeguards and evaluate the model in the intended environment before production use.

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.