DeepSeek V3 Update: Algorithm Innovation Leads to a New Paradigm in AI

DeepSeek recently released the V3 version update on the Hugging Face platform—DeepSeek-V3-0324. This model, with 685 billion parameters, has significant improvements in code capabilities, UI design, and reasoning abilities.

At the recent 2025 GTC conference, NVIDIA CEO Jensen Huang highly praised the achievements of DeepSeek. He pointed out that the market's previous belief that DeepSeek's efficient model would reduce demand for chips was incorrect, and that future computing demands will only increase rather than decrease.

As a representative work of algorithm breakthroughs, the relationship between DeepSeek and computing power supply is worth exploring in depth. We can analyze this issue from the perspective of the impact of computing power and algorithms on the development of the AI industry.

The Co-evolution of Computing Power and Algorithms

In the field of AI, the improvement of computing power provides a foundation for running more complex algorithms, enabling models to handle larger-scale data and learn more complex patterns. At the same time, the optimization of algorithms can utilize computing power more efficiently, increasing the efficiency of resource usage.

This symbiotic relationship is reshaping the AI industry landscape:

1. Technical route diversification: Some companies pursue the construction of ultra-large computing power clusters, while others focus on optimizing algorithm efficiency, forming different technical schools.

2. Industrial Chain Restructuring: Some chip manufacturers have become the dominant players in AI computing power through ecosystems, while cloud service providers have lowered deployment barriers through elastic computing power services.

3. Resource allocation adjustment: Enterprises seek a balance between investment in hardware infrastructure and efficient Algorithm development.

4. The Rise of Open Source Communities: Open source models like DeepSeek and LLaMA enable the sharing of algorithm innovations and computational power optimization results, accelerating technology iteration and diffusion.

Technical Innovations of DeepSeek

The rapid rise of DeepSeek is closely related to its technological innovations. Here is a layman's explanation of its main innovations:

Model Architecture Optimization

DeepSeek adopts a combination architecture of Transformer and MOE (Mixture of Experts), and introduces a Multi-Head Latent Attention mechanism (MLA). This architecture functions like an efficient team, where the Transformer handles regular tasks, while the MOE acts as a group of experts within the team, each with their own area of expertise. The MLA mechanism allows the model to flexibly focus on different important details, further enhancing performance.

Training Method Innovation

DeepSeek has proposed the FP8 mixed precision training framework. This framework can dynamically select the appropriate computing precision based on the needs of different stages during training, improving training speed and reducing memory usage while ensuring model accuracy.

Improvement of inference efficiency

DeepSeek introduces Multi-Token Prediction (MTP) technology. Unlike traditional step-by-step prediction methods, MTP technology can predict multiple tokens at once, significantly accelerating inference speed while reducing costs.

breakthrough in reinforcement learning Algorithm

DeepSeek's new reinforcement learning algorithm GRPO (Generalized Reward Penalty Optimization) optimizes the model training process. This algorithm can reduce unnecessary computations while ensuring improved model performance, achieving a balance between performance and cost.

These innovations have formed a complete technological system, reducing computing power requirements throughout the entire chain from training to inference. Now, ordinary consumer-grade graphics cards can run powerful AI models, significantly lowering the threshold for AI applications, allowing more developers and enterprises to participate in AI innovation.

Impact on Chip Manufacturers

The technological innovations of DeepSeek have a dual impact on chip manufacturers. On one hand, DeepSeek's deeper integration with hardware and the related ecosystem may expand the overall market size due to the lowered threshold for AI applications. On the other hand, the optimization of DeepSeek's Algorithm may alter the market demand structure for high-end chips, as some AI models that originally required top-tier GPUs to run may now operate efficiently on mid-range or even consumer-grade graphics cards.

Significance for China's AI Industry

The algorithm optimization of DeepSeek provides a technological breakthrough path for China's AI industry. Against the backdrop of limited high-end chips, the idea of "software compensating for hardware" reduces dependence on top imported chips.

Upstream, efficient algorithms have reduced the pressure on computing power demand, allowing computing service providers to extend hardware usage cycles and improve return on investment through software optimization. Downstream, the optimized open-source models have lowered the barriers to AI application development. Many small and medium-sized enterprises can develop competitive applications based on the DeepSeek model without needing large amounts of computing resources, which will give rise to more vertical AI solution offerings.

The Profound Impact of Web3+AI

Decentralized AI Infrastructure

The algorithm optimization of DeepSeek provides new momentum for Web3 AI infrastructure. The innovative architecture, efficient algorithms, and lower computational power requirements make decentralized AI inference possible. The MoE architecture is naturally suited for distributed deployment, where different nodes can hold different expert networks without requiring a single node to store the complete model, significantly reducing the storage and computational requirements of a single node, thus enhancing the flexibility and efficiency of the model.

The FP8 training framework further reduces the demand for high-end computing resources, allowing more computing resources to be integrated into the node network. This not only lowers the threshold for participating in decentralized AI computing but also enhances the overall computing power and efficiency of the entire network.

Multi-Agent System

1. Intelligent Trading Strategy Optimization: By analyzing real-time market data, predicting short-term price fluctuations, executing on-chain trades, and supervising trading results through the collaboration of multiple agents, it helps users achieve higher returns.

2. Automated execution of smart contracts: the collaborative operation of agents such as monitoring, executing, and supervising the results of smart contracts enables the automation of more complex business logic.

3. Personalized Portfolio Management: AI helps users in real-time to find the best staking or liquidity provision opportunities based on their risk preferences, investment goals, and financial situation.

DeepSeek is breaking through under the constraints of computing power by seeking innovations through algorithms, paving a differentiated development path for China's AI industry. Lowering application barriers, promoting the integration of Web3 and AI, reducing dependence on high-end chips, and empowering financial innovation are reshaping the digital economy landscape. In the future, AI development will no longer be just a competition of computing power, but a competition of collaborative optimization between computing power and algorithms. On this new track, innovators like DeepSeek are redefining the rules of the game with Chinese wisdom.