What is the difference between V4-Pro and V4-Flash?

V4-Pro has 1.6 trillion parameters and is optimized for maximum performance, while V4-Flash has 284 billion parameters and is designed for faster inference and lower resource requirements.

Is DeepSeek-V4 fully open-source?

Yes, both model variants are open-source and available on Hugging Face under permissive licenses, allowing for both research and commercial use.

How does the hybrid attention architecture work?

The hybrid attention architecture combines efficient attention mechanisms to reduce the quadratic complexity of standard transformers, enabling long context windows at a fraction of the compute and memory cost.

What hardware is recommended to run V4-Pro?

Due to its 1.6T parameter size, V4-Pro requires high-memory GPUs (e.g., multiple A100s or H100s) for inference, while V4-Flash can run on more modest hardware.

Can I fine-tune DeepSeek-V4 for my specific task?

Yes, the models are designed for fine-tuning, and the open-source release includes base checkpoints suitable for further adaptation.

DeepSeek-V4

The open-source era of 1M context intelligence

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Listed website: huggingface.co
Published in directory: Apr 25, 2026
Last record update: Apr 25, 2026

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Overview
5 key features
4 use cases

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What is DeepSeek-V4

DeepSeek-V4 is an open-source series of Mixture-of-Experts (MoE) language models designed for ultra-long context processing, supporting up to 1 million tokens by default. It includes two variants: V4-Pro with 1.6 trillion parameters and V4-Flash with 284 billion parameters, both leveraging a novel hybrid attention architecture to reduce computational and memory costs. This collection represents a significant step in democratizing large-scale AI models for developers and researchers.

Key Features

1 million token context window by default

Novel hybrid attention architecture for efficiency

Two model sizes: V4-Pro (1.6T params) and V4-Flash (284B params)

Open-source and available on Hugging Face

Highly efficient MoE design for reduced compute costs

Use Cases

Researchers exploring long-context language understanding and generation
Developers building applications requiring deep document analysis, such as legal or medical text processing
AI startups seeking cost-effective, high-performance models for custom fine-tuning and deployment
Enterprises needing to process large volumes of data (e.g., codebases, books) with minimal latency

Why do startups need this tool?

DeepSeek-V4 offers startups access to cutting-edge long-context AI without prohibitive costs. Its open-source nature allows for custom deployment and fine-tuning, enabling rapid prototyping and differentiation in AI-driven products. The efficient architecture reduces infrastructure expenses, making it a viable option for resource-constrained teams.