DeepSeek-V3 is an advanced language model developed with a Mixture-of-Experts (MoE) architecture, featuring a total of 671 billion parameters, of which 37 billion are activated per token. This model is designed to optimize inference performance and reduce training costs by leveraging cutting-edge technologies such as Multi-head Latent Attention (MLA) and DeepSeekMoE.
Key Features of DeepSeek-V3
1. Advanced Architecture
DeepSeek-V3 uses a Mixture-of-Experts (MoE) architecture, which activates only a selected subset of its parameters during the processing of each token. This approach significantly enhances computational efficiency compared to traditional dense models, while also reducing the computational requirements for inference.
| Feature | Description |
|---|---|
| Total number of parameters | 671 billion |
| Activated parameters/token | 37 billion |
| Technologies used | MoE, MLA, DeepSeekMoE |
2. Trained on a Large Volume of Data
The model was pre-trained on a massive dataset containing 14.8 trillion high-quality tokens, collected from a wide range of sources. After pre-training, DeepSeek-V3 was fine-tuned using both supervised learning and reinforcement learning techniques, enhancing its understanding and generation capabilities in natural language tasks.
| Training Data | Amount |
| Number of tokens | 14.8 trillion |
| Optimization phases | Supervised, Reinforcement Learning |
3. Superior Performance
Based on evaluation results, DeepSeek-V3 outperforms many other open-source models and achieves performance comparable to leading closed-source models such as GPT-4o and Claude-3.5-Sonnet. This makes it one of the most powerful solutions currently available in the field of generative artificial intelligence.
| Model | Performance vs GPT-4o |
| DeepSeek-V3 | Comparable |
| Other open-source models | Inferior |
4. Training Efficiency
A notable aspect of DeepSeek-V3 is its training efficiency. The entire training process required only 2.788 million H800 GPU hours, which is relatively low given the model’s complexity and size. This demonstrates not only the power of its architecture but also the optimization of operational costs.
Open-Source Availability and Community Impact
DeepSeek-V3 has been released as an open-source model, and its code is available through the official GitHub repository (DeepSeek-V3 on GitHub). This enables developers and researchers to leverage its capabilities, customize it, and enhance it for specific applications.
Furthermore, the model powers a highly popular AI assistant that has surpassed ChatGPT in the ranking of the most downloaded free apps on Apple’s App Store in the United States.
Conclusion
DeepSeek-V3 represents a major innovation in the field of generative artificial intelligence. With its efficient architecture, vast training dataset, and competitive performance, it positions itself as a cutting-edge option for both academic research and commercial applications. Its open-source availability opens up new opportunities for the development and deployment of AI-based solutions on a large scale.
DeepSeek AI, optimized for the Italian language, further contributes to the spread of AI in the national context, offering a powerful tool to improve the quality and precision of digital interactions in Italian.