DeepSeek: The AI Innovator from Technological Breakthrough to Multi-domain Empowerment
DeepSeek has achieved remarkable results in technological innovation, making breakthroughs in key areas such as model architecture and training optimization. Through innovative algorithms and architectural designs, it optimizes the training efficiency and performance of the model, enhances the processing and analysis capabilities of various types of data, and provides a solid technical support for industrial applications. Its depth and accuracy in data processing effectively reduce data noise interference and tap the potential value of data, opening up a new technical path for industrial applications. In the short term, DeepSeek can rapidly improve the operational efficiency of various industries, optimize business processes, and reduce operating costs. Taking the manufacturing industry as an example, with the data analysis capabilities of DeepSeek, enterprises can precisely regulate the production process and reduce resource waste. In the medium term, DeepSeek will continuously promote the improvement and optimization of the key business processes of various industries, and improve the level of refined management in the industry. In the long term, DeepSeek is expected to trigger profound changes in multiple industries, especially in the digital content creation industry, which will redefine the content production model. Overall, AI innovators represented by DeepSeek will undoubtedly become the key driving force for the intelligent transformation and innovative development of China's industries, reshaping the global AI industry pattern from single competition to global competition and cooperation.
Core Perspectives
- The breakthrough in algorithms breaks the computing power hegemony. In the future AGI arena, it is no longer an arms race of chip stacking, but a mental marathon of algorithm innovation.
- The wave of AI democratization is coming - "Open source community + Low-cost training solution", enabling small and medium-sized enterprises to obtain the admission ticket to compete with technology giants for the first time.
- AI empowers industries to achieve the "Chinese speed", with vertical scenarios accelerating penetration, and AI applications moving from "the cloud" to "the edge".
- Multimodal fusion leads the interaction revolution. When AI begins to understand the world with "five senses", "embodied intelligence" is no longer far away.
- The Chinese solution reshapes the global AI discourse power, and China is transforming from an AI rule acceptor to a standard co-governor.
1. The Rise of DeepSeek, Low-Cost AI Disrupting the Global Technology Landscape
At the beginning of 2025, DeepSeek-R1 was officially released and open-sourced. Its reasoning performance is comparable to the official version of OpenAI-o1. With advantages such as "low cost + high performance + open source", it has become a phenomenal benchmark in the global technology industry. DeepSeek-R1 has reached the performance level of the world's top closed-source models through innovations at the model level, under the background of restrictions on high-end chips. This technological breakthrough has sparked international discussions, and foreign media have evaluated it as "making AI technology cheaper and more inclusive", marking the first time that Chinese enterprises have surpassed international giants at the underlying algorithm level.
The DeepSeek company, a subsidiary of HFM Quantitative, was established in July 2023 and is an innovative technology company dedicated to achieving General Artificial Intelligence (AGI). In December 2024, DeepSeek-V3 was released, with performance comparable to leading closed-source models overseas. According to the official technical paper, the total training cost of the V3 model is $5.576 million, while the training cost of models such as GPT-4o is about $100 million. In January 2025, DeepSeek-R1 was released, and in tasks such as mathematics, code, and natural language reasoning, its performance is comparable to the official version of OpenAI-o1. After the explosion of DeepSeek-R1, DeepSeek immediately released the Janus-Pro multimodal large model and entered the text-to-image field.
According to Bloomberg, DeepSeek's artificial intelligence assistant ranks high on the list of the most downloaded mobile applications in 140 markets. Large foreign technology companies such as Microsoft, NVIDIA, and Amazon have successively launched and deployed to support users to access the DeepSeek-R1 model. As of February 2025, the number of DeepSeek open source community developers has exceeded 500,000, and its technical architecture has been included in the research cases by top institutions such as Stanford University.
The rise of DeepSeek has strategic significance that cannot be ignored at the technical, industrial, and ecological levels. At the technical level, the United States has long constructed a "computing power hegemony" through technical blockades. With technological innovation, DeepSeek has successfully broken through the high dependence of the traditional Transformer architecture on computing power, broken the computing power bottleneck, lowered the threshold for AI applications, and greatly promoted the popularization of AI technology. At the industrial level, DeepSeek has opened up a new paradigm of "algorithm breakthrough instead of hardware dependence", reducing the dependence on foreign high-end chips. It enables domestic enterprises to independently carry out AI research and development and production, driving the coordinated development of the upstream and downstream industries and reconstructing the discourse power of the AI supply chain. At the ecological level, DeepSeek implements an open source strategy and fully opens its core assets. This measure attracts global developers to conduct secondary development and innovation based on its open source achievements, providing a strong driving force for the rapid iteration of AI technology and reshaping the global open source large model ecological pattern.
2. DeepSeek's Technological Breakthrough, Redefining the AGI Development Coordinate System
DeepSeek has achieved multi-dimensional technological breakthroughs and innovations based on model framework innovation, model training optimization, efficient reinforcement learning, and data distillation technology. In terms of model framework innovation, the dynamic sparse routing algorithm is introduced to break the limitations of the traditional Transformer architecture. It adjusts the activation range and connection weights of the attention heads in real-time according to the semantic of the input text, significantly strengthening semantic association and logical capture when processing long documents and conversation scenarios. It not only improves the reasoning efficiency by 40%, but also reduces the dependence on video memory. A hierarchical knowledge distillation system is constructed with a "teacher-student-assistant" three-level distillation architecture to accurately capture complex semantic logics in the model lightweight process, especially in tasks such as code generation to improve performance. At the same time, its multimodal fusion capability supports the input of multiple data such as images and audio, laying a solid foundation for cross-domain applications.
1. Model Framework Innovation
At the model framework level, DeepSeek boldly innovates and adopts the MoE (Mixture of Experts model) architecture. As a network layer structure, MoE is composed of expert networks, gating networks, and selectors. DeepSeek's training mainly uses a sparse MoE architecture, and the gating mechanism only activates a small number of experts on a few devices, controlling the consumption of training resources while expanding the model capacity. Specifically, its innovation is mainly reflected in two aspects: First, refined expert segmentation divides experts into mN units and activates mK experts according to weights to achieve fine-grained decomposition of knowledge. When the computing cost remains unchanged, the number of experts is increased and flexibly activated, enabling experts to precisely learn diverse knowledge and maintain a high level of specialization. Second, shared expert isolation retains K shared experts to capture general knowledge, allowing other routing experts to get rid of common knowledge and reduce redundancy among non-shared experts.
(Illustrations: Schematic diagram of DeepSeek MoE architecture, Source: DeepSeek official paper)
2. Model Training Optimization
In terms of model training optimization, the common large model training generally selects BF16 or FP32/TF32 precision as the data calculation and storage format to ensure a higher training accuracy. However, the DeepSeek team takes a different approach and adopts a mixed-precision framework. Under this framework, most of the dense computing operations are carried out in FP8 format, while a few key operations strategically retain the original data format. This approach effectively balances the training efficiency and numerical stability. DeepSeek has made a series of innovations on the FP8 training framework. One is fine-grained quantization, which decomposes the data into smaller groups and adjusts each group using a specific multiplier to maintain high accuracy. The second is the mixed-precision strategy. DeepSeek maintains the original precision for several key modules, such as the embedding module, the output head, the mixed expert gating module, the normalization operator, and the attention operator. Through this way, it provides a better solution for model training.
(Illustrations: Schematic diagram of DeepSeek-V3 mixed-precision framework, Source: DeepSeek official paper)
3. Efficient Reinforcement Learning
In the post-training stage of the model, DeepSeek innovatively applies the GRPO algorithm to the reinforcement learning process, significantly improving the mathematical reasoning ability of the large language model (LLMs). Taking the training of R1-Zero as an example, the DeepSeek team abandons the reinforcement learning based on human feedback (RLHF) commonly used in the training of LLMs in the past, and instead completely relies on the reinforcement learning that applies the GRPO technology. Reinforcement learning mainly includes two key links: One is how to give the agent decision feedback, and the other is how the agent optimizes according to the feedback. Different from other technical routes, the advantage of GRPO is that when providing feedback for the agent's decision-making, there is no need to rely on the value model (Value Model). Its core idea is to use the average level of the candidate outputs within the group to replace the state value calculated by the value model as the comparison benchmark, and then calculate the advantage value of the current model. This innovation enables GRPO to save video memory and computing power while avoiding the errors caused by the value model, providing a more efficient and accurate implementation method for reinforcement learning.
(Illustrations: Schematic diagram of the GRPO algorithm, Source: DeepSeek official paper)
4. Data Distillation Technology
DeepSeek combines data distillation with model distillation to achieve the effective transfer of knowledge from large and complex models to small and efficient models. This fusion strategy not only significantly enhances the model performance but also greatly reduces the computing cost. Specifically, DeepSeek uses a high-performance teacher model to generate or optimize data, covering aspects such as data enhancement, pseudo-label generation, and data distribution optimization. The teacher model can expand or modify the original data to generate rich training data samples, thereby improving the diversity and representativeness of the data. At the same time, DeepSeek adopts the supervised fine-tuning (SFT) method to transfer the knowledge of the teacher model to the student model to achieve the optimization of model distillation. Through the organic combination of data distillation and model distillation, DeepSeek's distillation model performs outstandingly in the reasoning benchmark test. For example, DeepSeek-R1-Distill-Qwen-7B achieved a 55.5% Pass@1 result on AIME 2024, surpassing the current most advanced open-source model QwQ-32B-Preview.
3. DeepSeek Empowering Various Industries, Unlocking New Boundaries of AI Applications
In the short term, DeepSeek will quickly play an active role in industries with an urgent need for efficiency improvement, such as the financial field to quickly process transaction data to optimize risk assessment; the intelligent manufacturing industry to optimize the production process with its help and shorten the product delivery cycle. In the medium term, the medical industry is expected to achieve more accurate early disease screening and improvement of diagnosis plans with the help of DeepSeek; the education industry can use it to build a more mature personalized learning system and gradually change the traditional teaching mode; the digital content creation industry may develop a new creative ecosystem based on it. From a long-term perspective, DeepSeek will gradually evolve from a large model into a vertical model that suits the characteristics and needs of industrial development, promoting in-depth changes in the industry and thus reshaping the industrial pattern.
1. Breakthroughs in the Intelligent Manufacturing Field
In the intelligent manufacturing field, DeepSeek is leading the transformation of the production mode. It can deeply mine production data and build a solid barrier for fault prediction through refined monitoring and analysis technologies, effectively reducing the equipment failure rate and improving the smoothness and efficiency of the production line. For example, Foxconn introduced DeepSeek in the smartphone assembly line to coordinate robot operations, shorten the tact time, and improve production capacity and product competitiveness. At the same time, DeepSeek also plays an important role in product quality inspection and production process optimization in many manufacturing enterprises such as BYD and CATL. In addition, with the help of DeepSeek to build a supply chain intelligent management platform, enterprises can comprehensively and accurately analyze multi-source data, scientifically formulate procurement plans and inventory strategies, effectively improve inventory turnover, and reduce supply chain costs.
2. Medical and Health Revolution
In the medical and health field, DeepSeek can analyze the patient's medical history and symptoms to provide suggestions for doctors to diagnose diseases and assist doctors in making more accurate decisions. In the field of traditional Chinese medicine, DeepSeek also shows unique advantages. By introducing specific technologies, it can conduct the six-channel syndrome differentiation and zang-fu syndrome differentiation of traditional Chinese medicine to complete the work of assisting in the syndrome differentiation of traditional Chinese medicine, thereby improving the accuracy of syndrome differentiation and injecting scientific and technological power into the modernization of traditional Chinese medicine. In addition, DeepSeek also plays an important role in multimodal clinical data governance. It can integrate and analyze clinical data from different sources, improve the efficiency and accuracy of data governance, and provide comprehensive and reliable data support for medical institutions. At the same time, DeepSeek also shows great potential in personalized health management. Taking Meinian Health as an example, its blood sugar management AI agent "Tangdou" can provide more accurate health management suggestions for customers after being connected to DeepSeek by combining its own system and data set. This personalized health management solution can not only help customers effectively control blood sugar levels but also improve their health awareness and quality of life.
3. Financial Technology Evolution
In the field of financial technology, the addition of DeepSeek has brought an intelligent upgrade to the industry. With its powerful data processing and analysis capabilities, it has become a powerful assistant for financial institutions to improve business efficiency and service quality. Specifically, DeepSeek is widely used in multiple links such as intelligent contract quality inspection, automated valuation and reconciliation, credit material recognition and review, information retrieval and report writing. For example, Jiangsu Bank introduced DeepSeek to achieve intelligent contract quality inspection and automated valuation and reconciliation, greatly saving the workload and releasing productivity; Suchang Bank improves the recognition accuracy and review efficiency of credit materials through the DeepSeek-VL2 multimodal model; Nanjing Bank builds an assistant for front-line customer managers based on the DeepSeek-R1 model to improve the efficiency of information retrieval and sorting and assist in writing enterprise analysis reports; GF Securities, Orient Securities and other securities companies use DeepSeek to provide intelligent question-answering services for customers, shorten the response time, and improve customer satisfaction.
4. Empowering Education and Scientific Research
In the field of education and scientific research, DeepSeek is leading the new trend of intelligent teaching. Educational institutions such as Gaotu Education actively connect to DeepSeek and use its powerful data analysis capabilities to tailor learning plans and strategies for students. Through the learning assessment system, DeepSeek can deeply mine students' learning data and generate targeted learning suggestions to help students master knowledge points more efficiently. At the same time, integrating DeepSeek into the AI teaching assistant can effectively improve the efficiency of information integration and feedback, making it more convenient for teachers to understand students' learning situations and adjust teaching strategies in a timely manner. Taking the "Digital Gardener" intelligent