DeepSeek is built on cutting-edge large language model (LLM) architecture, optimized for real-world performance, scalability, and open accessibility. This page offers a deep dive into the core technologies that power DeepSeek, from its Mixture-of-Experts (MoE) design to its fine-tuning methodology and deployment stack.
🔬 Model Architecture: Mixture-of-Experts (MoE)
At the heart of DeepSeek-V3 lies a Mixture-of-Experts (MoE) architecture comprising 671 billion total parameters, with 37 billion active per token. Instead of using all model weights for every input (as in dense models), the MoE approach activates only the most relevant “experts” — specialized subnetworks trained for different types of input.
✅ Benefits of MoE:
- High performance with fewer computational resources
- Dynamic routing of tokens to optimal experts
- Scalable inference for large-scale applications
This design enables DeepSeek to match the performance of proprietary models like GPT-4 while remaining efficient and open-source.
📚 Training Dataset & Tokenization
DeepSeek was trained on 14.8 trillion high-quality tokens, carefully curated to include diverse multilingual content, coding data, scientific research, and real-world conversational examples.
Key components:
- Multilingual coverage for broad language understanding
- Code-rich sources including GitHub, StackOverflow, and documentation
- Tokenization: Utilizes a custom tokenizer optimized for long-context understanding and code structure
⚙️ Instruction Fine-Tuning
DeepSeek is instruction-tuned using advanced supervised fine-tuning (SFT) techniques and Reinforcement Learning from AI Feedback (RLAIF), enabling the model to:
- Understand multi-step instructions
- Handle long conversational threads
- Deliver task-specific completions (e.g., programming, translation, summarization)
🔐 Privacy-Centric Deployment
DeepSeek is deployed in a login-free, stateless architecture, ensuring:
- No chat history storage
- No personal data tracking
- Full compliance with privacy expectations in Europe, the U.S., and beyond
This architecture makes DeepSeek a secure, lightweight choice for developers and enterprises seeking a trustworthy AI interface.
🧠 Long Context Window
DeepSeek supports up to 128K context window, enabling:
- In-depth document analysis
- Full-page code understanding
- Retaining memory over long conversations
This makes DeepSeek ideal for research, document summarization, legal analysis, and complex problem solving.
⚡ Deployment & Inference Stack
We use GPU-accelerated inference with modern distributed systems to deliver real-time response speeds globally.
Our stack includes:
- NVIDIA A100 / H100 hardware
- Model parallelism using TensorParallel or vLLM
- Autoscaling for traffic surges
- Global CDN to reduce latency worldwide
🧩 Open Source & Extensibility
DeepSeek is licensed under the MIT License, allowing developers to:
- Run models locally or on-premises
- Customize the inference pipeline
- Integrate with their own applications or APIs
We encourage experimentation, community contribution, and integration across different platforms.
🚀 What’s Next?
We are actively working on:
- Expanding multilingual capabilities
- Open-sourcing fine-tuning tools and datasets
- Providing API endpoints for developers
- Launching enterprise-ready SDKs and on-prem deployment guides
Summary
| Feature | Value |
|---|---|
| Model | DeepSeek-V3 |
| Parameters | 671B total, 37B active |
| Context | 128,000 tokens |
| Architecture | MoE |
| License | MIT |
| Training Data | 14.8T tokens |
| Use Cases | General-purpose reasoning, programming, translation, writing |
| Hosting | Stateless, login-free, privacy-centric |
Learn More
Want to experiment with the model, run benchmarks, or contribute to the open-source repo? Stay tuned for our upcoming Developer Hub and GitHub repository release.