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):

• SGLang
• vLLM

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 is quantized DeepSeek-V4-Pro-NVFP4 with nvidia-modelopt v0.44

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: Hybrid: Human, Automated Labeling Method by dataset: Hybrid: Human, Automated

Testing Dataset:

Data Collection Method by dataset: Hybrid: Human, Automated Labeling Method by dataset: Hybrid: Human, Automated Dataset Properties: Undisclosed

Evaluation Dataset:

Data Collection Method by dataset: Hybrid: Human, Automated Labeling Method by dataset: Hybrid: Human, Automated Dataset Properties: We evaluated the model on text-based reasoning, coding, and agentic tool-use benchmarks: GPQA Diamond contains 448 graduate-level multiple-choice questions written by domain experts in biology, physics, and chemistry; 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; SciCode evaluates scientific coding capabilities; IFBench is a benchmark for evaluating instruction-following capabilities across diverse and structured task constraints.

Inference:

Acceleration Engine: SGLang, vLLM Test Hardware: NVIDIA Blackwell B200

Post Training Quantization

This model was obtained by quantizing the weights and activations of DeepSeek-V4-Pro to NVFP4 data type, ready for inference with SGLang and vLLM. Only the weights and activations of the linear operators within transformer blocks in MoE are quantized.

Usage

Deploy with vLLM

Verified serving this NVFP4 checkpoint on GB300 with vllm/vllm-openai:nightly-aarch64 (vLLM 0.22.1rc1.dev491), tensor-parallel 4:

vllm serve nvidia/DeepSeek-V4-Pro-NVFP4 \ 
    --tensor-parallel-size 4 --trust-remote-code --kv-cache-dtype fp8

Deploy with SGLang

Requires SGLang PR #25820. The integration auto-detects NVFP4 from the checkpoint's hf_quant_config.json (weights are stored in FP8 with "moe_quant_algo": "NVFP4"):

python3 -m sglang.launch_server --model nvidia/DeepSeek-V4-Pro-NVFP4 --tensor-parallel-size 8 --trust-remote-code

Evaluation

The accuracy benchmark results are presented in the table below:

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. 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.

Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.