The NVIDIA Qwen3-Coder-Next-NVFP4 model is a quantized version of Qwen's Qwen3-Coder-Next model, an autoregressive language model that uses an optimized Transformer architecture with Mixture of Experts (MoE). For more information, refer to the Qwen3-Coder-Next model card. The NVIDIA Qwen3-Coder-Next-NVFP4 model was quantized using the TensorRT Model Optimizer.
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-Coder-Next) Model Card.
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 (Hybrid) Network Architecture: Qwen3NextForCausalLM Model Details:
Total Parameters: 80.1B
Active Parameters: 3.1B (Sparse Mixture-of-Experts)
Expert Configuration: 512 total experts, 10 activated per token + 1 shared expert.
Attention Mechanisms: Hybrid layout combining Gated DeltaNet (linear attention for long-context efficiency) and Gated Attention (sliding window/standard attention).
Context Window: 262,144 tokens (native).
Input:
Input Type(s): Text Input Format(s): String Input Parameters: 1D (One-Dimensional): Sequences Other Properties Related to Input:
Output:
Output Type(s): Text Output Format: String Output Parameters: 1D (One-Dimensional): Sequences Other Properties Related to Output: N/A
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 is quantized with nvidia-modelopt 0.41.0rc2.dev72+g886781332
This model was obtained by quantizing the weights and activations of Qwen3-Coder-Next to NVFP4 data type, ready for inference with SGLang. Only the weights and activations of the linear operators within transformer blocks are quantized, as well as the KV-cache to FP8. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 4x.
Usage
Deploy with SGLang
To serve the quantized NVFP4 checkpoint with SGLang:
Please install from source:
git clone git@github.com:sgl-project/sglang.git
Once the repo is cloned, do uv pip install -e . "python" and run the serve command.
When a release is cut with the bugfix for this model's launch, we will update this model card.
Reproduce with ModelOpt
You may want to produce this checkpoint yourself. To reproduce the NVFP4 quantized checkpoint using TensorRT Model Optimizer:
The NVIDIA Qwen3-Coder-Next-NVFP4 model maintains high-precision reasoning while operating at 4-bit. Evaluation was performed using the LM-Evaluation-Harness.
Benchmark
Precision
Score
Recovery
SWE-Bench Pro
BF16
44.3%
100%
NVFP4
43.9%
99.1%
HumanEval (Python)
BF16
92.4%
100%
NVFP4
91.8%
99.3%
GPQA Diamond
BF16
53.4%
100%
NVFP4
52.6%
98.5%
LiveCodeBench v6
BF16
41.2%
100%
NVFP4
40.5%
98.3%
Note: NVFP4 and FP8 KVCache provides a significant memory footprint reduction (~3.5x vs BF16) with negligible accuracy degradation on coding and reasoning tasks.
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.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.
