What is DeepSeek AI Model? Explained

DeepSeek is an open‐weight large language model (LLM) suite developed by Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co., Ltd., a research arm of China’s High-Flyer hedge fund. Launched in November 2023, DeepSeek’s flagship models—DeepSeek-LLM, V2, V3 and the reasoning‐focused R1—have shaken the AI industry by delivering state-of-the-art performance at a fraction of the cost of leading Western models such as GPT-4.

Introduction to DeepSeek

DeepSeek’s mission is to democratize advanced AI by open-sourcing its model weights under the MIT License and sharing technical disclosures. By leveraging innovations such as Mixture-of-Experts (MoE) layers, rotary positional embeddings and chain-of-thought reinforcement learning, DeepSeek has achieved comparable or superior benchmark scores while spending as little as US$5.6 million on training—versus the US$50–100 million typically cited for GPT-4.

Key Innovations

1.      Mixture-of-Experts (MoE)
DeepSeek-V3 employs an MoE architecture with 671 billion parameters but only activates ~37 billion per token, reducing compute cost and improving efficiency.

2.      Chain-of-Thought Reinforcement Learning
R1 models—including DeepSeek-R1 and R1-Zero—use pure or hybrid reinforcement learning (RL) to generate step-by-step reasoning traces before arriving at answers. This approach enhances logical inference and mathematical problem-solving.

3.      Extended Context Windows
From V2 onward, DeepSeek models support up to 128 K tokens of context using the YaRN framework, enabling long-form document understanding and multi-turn conversations.

DeepSeek Model Lineup

• DeepSeek-LLM 7B
  • 7 billion
  • 4 K tokens
  • Low (US$6 M)
  • General text generation and chat

• DeepSeek-LLM 67B
  • 67 billion
  • 4 K tokens
  • Moderate
  • Higher‐capacity language tasks

• DeepSeek-V2
  • 671 billion (MoE)
  • 128 K tokens
  • ~US$6 M
  • Long-context analysis and advanced chat interfaces

• DeepSeek-V3 
  • 671 billion (MoE)
  • 128 K tokens
  • ~US$5.6 M
  • Efficient large-scale inference and customization
• DeepSeek-R1
  • 671 billion (MoE)
  • 32 K tokens
  • Hybrid RL cost
  • Complex reasoning, math, coding, logical inference

 How DeepSeek Works

1.      Pre‐training on trillions of tokens (English and Chinese) using a pre-norm decoder Transformer with SwiGLU feed-forward layers and RMSNorm.

2.      Supervised Fine-Tuning (SFT) on human-annotated data for helpfulness and safety.

3.      Reinforcement Learning from Human Feedback (RLHF) and model-based reward models to refine reasoning capabilities.

4.      Distillation to create smaller, deployable variants without sacrificing core performance.

Why DeepSeek Matters

DeepSeek’s cost-efficient training and open-source licensing “upend AI” by making cutting-edge LLM capabilities accessible beyond deep pockets. Its emergence has prompted reevaluations of hardware suppliers and geopolitical AI dynamics, underscoring that innovation can flourish even under export restrictions.