The NVIDIA DeepSeek-V4-Flash-NVFP4 model is a quantized version of DeepSeek AI's DeepSeek-V4-Flash model, an autoregressive Mixture-of-Experts language model that uses an optimized Transformer architecture with hybrid attention (Compressed Sparse Attention and Heavily Compressed Attention) and Manifold-Constrained Hyper-Connections. For more information, refer to the DeepSeek-V4-Flash model card. The NVIDIA DeepSeek-V4-Flash-NVFP4 model is quantized with 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-V4-Flash) Model Card.
DeepSeek V4 is well-suited for advanced reasoning, agentic AI applications, tool use scenarios, and complex problem-solving in domains such as mathematics, software engineering, and enterprise AI assistants.
Architecture Type: Transformers Network Architecture: Mixture-of-Experts (MoE) with Hybrid Attention (Compressed Sparse Attention + Heavily Compressed Attention) Number of Model Parameters: 284B in total and 13B activated
Input:
Input Type(s): Text Input Format(s): String Input Parameters: 1D (One-Dimensional): Sequences Other Properties Related to Input: Supports multi-turn conversations with system prompts, user messages, and assistant responses. Maximum context length of 1 million tokens. Uses a custom encoding pipeline (encoding_dsv4) with three reasoning modes: Non-think (fast), Think High (logical analysis), and Think Max (full reasoning extent).
Output:
Output Type(s): Text Output Format: String Output Parameters: One-Dimensional (1D): Sequences Other Properties Related to Output: Supports structured JSON output, function/tool calling, and reasoning content when enabled.
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 DeepSeek-V4-Flash NVFP4 quantized with nvidia-modelopt v0.44.0
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 Datasets:
Data Modality: Undisclosed Data Collection Method by dataset: Undisclosed Labeling Method by dataset: Undisclosed Data Size : Undisclosed Properties: Undisclosed
Evaluation Dataset:
Datasets: GPQA Diamond, AA-LCR, τ²-Bench Telecom, SciCode, IFBench Data Collection Method by dataset: Hybrid: Automated, Human Labeling Method by dataset: Hybrid: Human, Automated DataSet Properties: We evaluated the model on reasoning, long-context recall, agentic tool-use, coding, and instruction-following 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-Flash 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 the vllm/vllm-openai:nightly-aarch64 Docker image (vLLM 0.22.1rc1.dev504), tensor-parallel 4:
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"):
The accuracy benchmark results are presented in the table below:
Precision
GPQA Diamond
AA-LCR
τ²-Bench Telecom
SciCode
IFBench
Baseline (ours)
0.894
0.658
0.943
0.481
0.788
NVFP4
0.891
0.655
0.942
0.481
0.795
Benchmarked with temperature=1.0, top_p=1.0, max num tokens 384000
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.
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