In the rapidly evolving landscape of artificial intelligence (AI), innovation and performance are the two beacon lights guiding companies toward success. A prime example of this phenomenon is the recent surge of a groundbreaking application known as DeepSeek, which has taken the AI industry by storm since its global emergence at the end of January 2025. The application boasts an impressive daily active user (DAU) count of 22.15 million, second only to the omnipresent ChatGPT. Furthermore, it has remarkably climbed to the number one position in Apple’s app store across 157 countries and regions. This meteoric rise can be attributed to a series of technological innovations and engineering capabilities that position DeepSeek as a leader in global tech trends.

At the heart of DeepSeek’s impressive performance is its third version, DeepSeek V3, which has redefined what a cost-effective AI product can be. Implementing a self-developed architecture known as Mixture of Experts (MoE), DeepSeek V3 comprises a staggering total of 671 billion parameters, activating 37 billion parameters per token. The technology introduces several noteworthy breakthroughs including sparse expert models, multi-head attention mechanisms, and innovative training objectives. This comprehensive approach has significantly enhanced inference efficiency, establishing it as a competitor against established models like GPT-4o.

A game-changer in the training process was the introduction of an FP8 mixed precision training strategy, marking the first widespread implementation of this approach. This strategy not only balanced stability and price-effectiveness but also resulted in a remarkably low training cost of just 5.57 million U.S. dollars while taking under two months to complete. This reduced cost structure means that the API is priced at a mere 0.5 Yuan for every million input tokens, a drastic decrease that is set to broaden the accessibility of large-scale AI models across various sectors.

But the innovations do not stop there. DeepSeek R1 series has also made significant strides in enhancing inference capabilities by harnessing the power of reinforcement learning (RL). In a competitive market dominated by advanced large language models, the R1 series' unique technical approach coupled with exceptional performance is steadily gaining recognition as an industry focal point.

R1 Zero stands out as a cornerstone of the R1 series, making a trailblazing decision to bypass traditional large language model training pathways—in particular, the extensive supervised fine-tuning (SFT) process that has long been considered essential. Instead of relying on vast quantities of manually annotated data, R1 Zero emphatically embraces reinforcement learning to pre-train the model directly. This decision was fraught with challenges as the development team faced a myriad of technical hurdles. However, their unyielding efforts bore fruit, proving that applying reinforcement learning within large language models possesses immense potential for substantial enhancement.

This allows R1 Zero to learn and optimize its capabilities through interactions with different environments autonomously, achieving competitive levels comparable to OpenAI’s GPT models. This remarkable advancement has not only cemented R1 Zero’s place in the large language model landscape but has also paved the way for future developments within the R1 series.

Building upon R1 Zero's initial success, the subsequent versions of R1 underwent rigorous optimization. A significant challenge faced by large language models in real-world applications is maintaining consistency in language. If the responses generated by a model lack coherence in logic, style, and content, user experience and the effectiveness of the application can suffer gravely. The R1 team addressed this through meticulous algorithm improvements. By delving deeply into the internal logic and semantic relations of the language, they successfully introduced a new algorithmic architecture and training strategies that allow R1 to maintain better contextual coherence and consistency when generating text. Be it writing lengthy articles or managing multi-turn dialogues, R1 provides responses that are logically sound and stylistically coherent, offering users a more natural and fluid interaction experience.

On a foundational technical level, the R1 series underwent significant alterations, particularly with Nvidia’s Parallel Thread Execution (PTX) instruction set. This instruction set is vital for programming Nvidia GPUs and plays a crucial role in the operational efficacy of large language models. However, the traditional PTX instruction set has certain limitations regarding cross-platform compatibility, which hinders the broad application of large language models across diverse hardware platforms. R1's optimizations have dramatically enhanced this compatibility, meaning that R1 can efficiently operate not only on Nvidia platforms but also adapt seamlessly to other manufacturers' hardware. More critically, this improvement opens possibilities for compatibility with domestic chipsets, which are evolving rapidly in terms of performance and stability. This adaptability of the R1 series with local chip technology contributes significantly to the independent development of the domestic AI industry, helping to break the monopoly held by foreign hardware technologies and enabling the growth of a robust domestic AI ecosystem.

The impressive capabilities of R1 are demonstrating enormous potential in various industrial applications. Its efficient inference capabilities allow for the speedy generation of accurate results, even when processing vast amounts of data or managing complex tasks. For instance, within the realm of smart customer service, R1 is capable of quickly interpreting user inquiries and delivering precise responses in a matter of seconds, significantly enhancing both the efficiency and quality of customer interactions. Furthermore, the low-cost advantage offered by R1 distinguishes it in industry applications, as the cost factor is crucial for enterprises seeking large-scale deployment of AI technologies. With more manageable operating costs, a greater number of companies can afford to incorporate large language models into their operations, reducing barriers to entry in adopting AI technologies. By fostering this widespread adaptation across various sectors, R1 helps to democratize AI technology, unlocking new opportunities and transformative possibilities for economic and social progress.

Another exciting feature of DeepSeek’s offerings is the Janus-Pro model, which excels in image understanding and generation. Deploying a dual-encoder architecture allows it to tackle both tasks simultaneously while utilizing a shared Transformer network. Enhanced by a three-phase training optimization process, the Janus-Pro model significantly improves its adaptability to real-world scenarios, outperforming many prominent Western counterparts such as DALL·E 3.

The impact of DeepSeek is likely to be profound across three key aspects of the industry: firstly, it signals a transition from a “scale-driven” approach towards one that places emphasis on “quality first.” Secondly, distillation technologies propel the development of lightweight models that meet high performance and efficiency benchmarks, facilitating their more extensive deployment on end-side applications. Finally, as domestic and international giants in the tech space take an interest in these groundbreaking advancements, a trend towards technological parity may emerge, although engineering capabilities and ecosystem development will remain pivotal for companies looking to establish competitive advantages.