From a technical point of view, DeepSeek's V1 model uses a hybrid expert architecture (MoE) to support multitasking and excel in scenarios such as code generation and mathematical reasoning. The R0 model is based on reinforcement learning training, focusing on code generation and complex mathematical problem solving, and the inference ability can be transferred to a small model through distillation technology. This technical route not only improves the performance of the model, but also reduces the cost of training and inference.
市場表現方面，DeepSeek的關注度隨著模型發佈迅速飆升。數據顯示，2024年12月28日DeepSeek指數達到約6000萬，2025年1月31日更是達到9.8億。
DeepSeek的技術優勢在於其高性能和低訓練成本。與Meta的Llama 3.1模型相比，DeepSeek-V3在2048塊H800 GPU上訓練3.7天，硬體成本僅為558萬美元，而Llama 3.1模型的訓練成本高達9240萬美元，高出16倍。在推理成本方面，DeepSeek V3的價格約為OpenAI GPT-4o價格的十分之一，R1模型的價格更是OpenAI o1價格的二十分之一。
This cost advantage not only makes DeepSeek more competitive in the market, but also lowers the barrier to entry for SMEs and entrepreneurs. The open-source strategy has further boosted the market adoption of DeepSeek. Compared with the closed-source model, the open source model can attract more developers to participate and promote the common progress of technology. DeepSeek has a high degree of open source and meets many standards of open source AI definition 0.0 (OSAID 0.0), including model weight disclosure, transparency of some training data, and open source code.
DeepSeek's technical roadmap analysis shows that its model architecture integrates hybrid expert (MoE) and reinforcement learning techniques. The MoE architecture allocates data to appropriate expert models for processing through routing and experts, saving computing resources. The Multi-Head Latent Attention (MLA) technology uses low-rank joint compression to reduce the memory usage in the inference process and improve the inference efficiency.
In terms of performance improvement, DeepSeek uses the Population Relative Policy Optimization (GRPO) technology to improve the traditional Proximal Policy Optimization (PPO) algorithm, which improves the computing efficiency and reduces the memory footprint. The combination of these technologies enables DeepSeek to achieve significant breakthroughs in both performance and cost.
DeepSeek's technical strength and market potential have been recognized by industry giants. OpenAI founder Sam Altman commented that DeepSeek R1 is an impressive model that delivers superior performance at a lower cost. Microsoft CEO Satya Nadella also praised DeepSeek's technological innovation, saying that its open-source model enables efficient inference time calculations.
DeepSeek has been built with a number of cloud platforms and applicationscooperateRelationship. thesecooperateIt not only provides DeepSeek with a wider market channel, but also provides users with more diverse use scenarios.
DeepSeek is used in a variety of scenarios, including stand-alone use and tool combinations. Independent use scenarios include text creation, information consultation, knowledge reasoning, etc., and users only need to input demand instructions to directly obtain the generated content. The toolset is based on the tool ecosystem synergy driven by text instructions to achieve the "DeepSeek+" innovative workflow. DeepSeek can be combined with XMind to quickly make mind maps; Combined with Feishu, intelligent data management and multi-dimensional form collaboration can be realized. Combined with Photoshop, batch processing of images can be automated.
These use cases not only improve work efficiency, but also provide users with more intelligent solutions. With the right mix of tools, users can maintain an efficient and organized workflow in complex environments.
DeepSeek's open-source strategy will accelerate the prosperity of the AI application ecosystem. The open source model can reduce the cost and threshold for traditional enterprises and entrepreneurs to access AI, and promote the diversification and sustainable development of the entire AI application ecosystem. Inference models will become the mainstream form of AI technology. Inference models deduce answers step by step by decomposing complex problems and simulating human thought processes, especially for multi-link and complex tasks.
DeepSeek's technology roadmap and market strategy have made it occupy an important position in the AI field. Its advantages of high performance, low training cost and inference cost, as well as the use cases of open source strategies and tool combinations, have laid a solid foundation for its future market development.
Although DeepSeek's technical advantages are significant, it is still necessary to be vigilant against AI hallucinations during use. AI hallucinations refer to false or misleading information generated by AI. Tests show that the hallucination rate of the DeepSeek R3 model is significantly higher than that of the V0 model, indicating that users need to judge and screen the results when using AI.
DeepSeek is a completely practical manual - from technical principles to tips and tricks - to Top Technology
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