DeepSeek Evolution: From V1 to V4

1. Origins: The AI Dream of a Quantitative Giant

The story doesn't begin in Silicon Valley, but in China's High-Flyer (幻方量化). This quantitative hedge fund with supercomputing center-level computing power incubated the DeepSeek team to explore Artificial General Intelligence (AGI). They embarked on this extraordinary path with geek spirit and a belief in open source.

2. DeepSeek V1: First Cry

Release: 2023

DeepSeek V1 was the team's first attempt. Although V1's parameter scale and performance weren't the most top-tier in the large model battle at the time, it demonstrated the team's solid technical foundation. V1 was primarily a Coder model, focusing on code generation, which laid the foundation for DeepSeek's later dominance in programming.

• Keywords: Code LLM, Open-source exploration, 7B/33B parameters.

3. DeepSeek V2: Architectural Innovation, David vs Goliath

Release: May 2024

V2 was DeepSeek's breakthrough battle. In this version, the team boldly introduced the MLA (Multi-head Latent Attention) architecture. This innovation greatly reduced KV Cache memory usage during inference, making DeepSeek V2's inference cost only a fraction of GPT-4's at equivalent parameters.

V2's release shocked the open-source community. People discovered that this model from China was not only cheap but actually good.

• Keywords: MLA architecture, Price killer, First MoE attempt, 236B parameters.

4. DeepSeek V3: Masterpiece, Rivaling Closed-Source

Release: December 2024

If V2 was stunning, then V3 was completely mind-blowing. DeepSeek V3 pushed parameter scale to 671B (37B active), adopting a more mature MoE architecture. For the first time in multiple benchmarks, it competed evenly with GPT-4o and Claude 3.5 Sonnet, even surpassing them in Chinese contexts and specific programming tasks.

V3's release completely changed the stereotype that "open-source models can't beat closed-source models." DeepSeek became the preferred API for developers worldwide.

• Keywords: 671B MoE, FP8 training, Surpassing GPT-4, Open-source milestone.

5. DeepSeek V4: The Path to Legend

Release: 2026 (Expected)

Standing on V3's shoulders, V4 didn't choose conservative iteration but once again revolutionized the underlying architecture — Engram (MoE 2.0). V4's goal is no longer "rivaling" but surpassing. It aims to solve the two most painful points of large models: long-context forgetting and reasoning logic defects.

V4 marks DeepSeek's transformation from follower to leader. It proves that pure technological idealism still has the power to change the world in this era of commercial hype.

6. Conclusion

From V1 to V4, every step DeepSeek took was extremely firm. They had no flashy launch events, no endless PR articles, just lines of open-source code and detailed technical reports.

This is DeepSeek, the backbone of Chinese AI.