DeepSeek-V4-Flash-0731

Description

DeepSeek-V4-Flash-0731 is a 304B-parameter sparse Mixture-of-Experts language model for text generation, coding, reasoning, long-context, and agentic workflows. It supports a one-million-token context and includes an attached speculative decoding module.

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 DeepSeek-V4-Flash-0731 Model Card.

License and Terms of Use:

GOVERNING DOWNLOAD TERMS: Use of the model is governed by the NVIDIA Open Model Agreement. ADDITIONAL INFORMATION: MIT License.

Deployment Geography:

Global

Use Case:

Use Case: Text generation, coding, reasoning, long-context, and agentic tool-use workflows.

Release Date:

NGC 08/13/2026 via link

Reference(s):

References:

• DeepSeek-V4-Flash-0731 Model Page
• DeepSeek-V4 Technical Report

Model Architecture:

Architecture Type: Transformer Network Architecture: Sparse Mixture of Experts with hybrid Compressed Sparse Attention and Heavily Compressed Attention, Manifold-Constrained Hyper-Connections, and an attached speculative decoding module Total Parameters: 304B Active Parameters: 13B

Input:

Input Types: Text Input Formats: String Input Parameters: One Dimensional (1D) Other Input Properties: Supports multi-turn messages encoded in OpenAI-compatible format and low, high, and max reasoning-effort levels. Input Context Length (ISL): 1 million tokens

Output:

Output Types: Text Output Format: String Output Parameters: One Dimensional (1D) Other Output Properties: Supports text completions and reasoning content.

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:

Runtime Engines:

• SGLang

Supported Hardware:

• NVIDIA Blackwell: NVIDIA B200 Tensor Core GPU, NVIDIA RTX PRO 6000D
• NVIDIA DGX Spark: validated multi-node profile
• NVIDIA Hopper: NVIDIA H100 Tensor Core GPU, NVIDIA H200 Tensor Core GPU, NVIDIA H20 Tensor Core GPU

Operating System: 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)

DeepSeek-V4-Flash-0731

Training, Testing, and Evaluation Datasets:

Training Dataset

Data Modality: Text Text Training Data Size: [More than 10 Trillion Tokens] Data Collection Method by dataset: Undisclosed Labeling Method by dataset: Undisclosed Properties: The DeepSeek-V4 family was pretrained on more than 32 trillion diverse tokens and then post-trained for reasoning and agentic capabilities.

Testing Dataset

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

Evaluation Dataset

Evaluation Benchmark Score: Terminal Bench 2.1: 82.7; NL2Repo: 54.2; Cybergym: 76.7; DeepSWE: 54.4; Toolathlon-Verified: 70.3. Data Collection Method by dataset: [Hybrid: Automated, Manually-Collected] Labeling Method by dataset: [Hybrid: Automated, Manually-Labeled] Properties: Evaluated on coding-agent, repository, cybersecurity, software-engineering, and tool-use benchmarks.

Inference

Acceleration Engine: SGLang Test Hardware:

• NVIDIA B200 Tensor Core GPU
• NVIDIA RTX PRO 6000D
• NVIDIA H100 Tensor Core GPU
• NVIDIA H200 Tensor Core GPU
• NVIDIA H20 Tensor Core GPU
• NVIDIA DGX Spark

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 developer team to ensure these software components meet requirements for the relevant industry and use case and address 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

Get Help

For deployment guidance, see the NVIDIA NIM documentation. For this Experimental, non-enterprise-supported release, use the NVIDIA Developer Community Forum for community support.