The NVIDIA DeepSeek-V3.1-FP4 model is the quantized version of the DeepSeek AI's DeepSeek-V3.1 model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA DeepSeek V3.1 FP4 model is quantized with 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 (DeepSeek V3.1) 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.
Input Type(s): Text Input Format(s): String Input Parameters: 1D (One Dimensional): Sequences Other Properties Related to Input: DeepSeek recommends adhering to the following configurations when utilizing the DeepSeek-V3.1 series models, including benchmarking, to achieve the expected performance: \
Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs.
Avoid adding a system prompt; all instructions should be contained within the user prompt.
For mathematical problems, it is advisable to include a directive in your prompt such as: "Please reason step by step, and put your final answer within \boxed{}."
When evaluating model performance, it is recommended to conduct multiple tests and average the results.
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 v0.39.0
Training Dataset:
** 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
Evaluation Dataset:
** Data Collection Method by dataset: Hybrid: Human, Automated
** Labeling Method by dataset: Hybrid: Human, Automated
Acceleration Engine: TensorRT-LLM Test Hardware: B200
Post Training Quantization
This model was obtained by quantizing the weights and activations of DeepSeek V3.1 to FP4 data type, ready for inference with TensorRT-LLM. Only the weights and activations of the linear operators within transformer blocks are quantized. This optimization reduces the number of bits per parameter from 8 to 4, reducing the disk size and GPU memory requirements by approximately 1.6x.
Usage
Deploy with TensorRT-LLM
To deploy the quantized FP4 checkpoint with TensorRT-LLM LLM API, follow the sample codes below (you need 8xB200 GPU and TensorRT LLM built from source with the latest main branch):
LLM API sample usage:
from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
def main():
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
sampling_params = SamplingParams(max_tokens=32)
llm = LLM(model="nvidia/DeepSeek-V3.1-FP4", tensor_parallel_size=8, enable_attention_dp=True)
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
# The entry point of the program needs to be protected for spawning processes.
if __name__ == '__main__':
main()
Evaluation
The accuracy benchmark results are presented in the table below:
Precision
MMLU-Pro
GPQA Diamond
LiveCodeBench
SCICODE
AIME 2025
FP8 (AA Ref)
85
79
78
42
85
FP4
83
77
79
44
85
*Max OSL for evals can be as high as 64K.
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.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.
