The rapid development in artificial intelligence (AI) technologies has recently focused especially around large language models (LLMs). While OpenAI ChatGPT, Google Gemini and the models developed by Meta stand as leaders in the market, DeepSeek, a China-based startup, has achieved a surprising rise in a short time. DeepSeek, which stands out with both "deep search" and innovative training techniques, challenges the giants of the industry by offering high performance with less cost and fewer chips.
DeepSeek is an AI solution based on the "deep search" approach, combining artificial intelligence and natural language processing (NLP) technologies. It aims to facilitate the process of accessing information by providing fast, accurate and contextual answers to complex text-based questions.
DeepSeek was founded in Hangzhou, China in 2023 by Liang Wenfeng, an information and electronics engineer. Liang had previously supported projects focused on artificial intelligence in the incubation program of the High-Flyer fund he founded in 2015. The company's vision is to achieve a level of "artificial general intelligence" (AGI) that can catch up with or surpass humans in various fields.
DeepSeek's first major announcement was the open source coding model called DeepSeek Coder, released in November 2023. Subsequently, the model capacity has steadily increased and diversified with different releases.
DeepSeek LLM
DeepSeek-v2
DeepSeek Coder-v2
DeepSeek-v3
DeepSeek-R1
One of the key advantages of DeepSeek is that it uses fewer GPUs (e.g. 2,000 GPUs) to achieve results close to the massive infrastructure (10,000 GPUs) required by models like ChatGPT. This is due to the use of efficient architectures such as MoE (Mixture of Experts) and reinforcement learning (RL).
Several key innovations stand out in the success of DeepSeek models:
Both DeepSeek and ChatGPT offer services in the areas of AI-powered chat and text processing. However, their focus and areas of flexibility are different:
Like other AI models, DeepSeek collects and processes user data. The fact that it is stored on servers based in China raises questions about privacy. However, the open source nature of the model allows independent researchers to examine the code. Still, users are advised to be careful when sharing sensitive data.
While traditional search engines are based on keywords, DeepSeek looks at the whole text and analyzes the context.
Thanks to its large context window (128,000 tokens and above), it can crawl large data sets such as books, articles or complex code files.
Thanks to techniques such as distillation, RL and MoE, it breaks the "big model = high cost" equation and achieves high performance with fewer chips.
The open-source nature and active developer community adds to DeepSeek's credibility.
According to Forbes, DeepSeek will not pursue an aggressive commercialization plan in the short term and will continue to focus on research. However, its strategic partnerships with major chipmakers such as AMD indicate that it will appear in different industries in the future. Alexandr Wang, CEO of ScaleAI, the world's leading data tagging company, describes DeepSeek's models as having the potential to be "earth-shattering."
DeepSeek represents a significant shift in the AI landscape, demonstrating that innovative approaches and efficient architectures can challenge established players without requiring massive computational resources. Its focus on open-source development, cost-effectiveness, and specialized capabilities positions it as a formidable competitor in the rapidly evolving world of artificial intelligence.
As the AI industry continues to mature, DeepSeek's success story highlights the importance of innovation over pure scale, suggesting that the future of AI may be more diverse and accessible than previously imagined.
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