DeepSeek V3 Update: Redefining the Direction of AI Development

Recently, DeepSeek released the latest V3 version update, with model parameters reaching 68.5 billion, showing significant improvements in coding capabilities, UI design, and reasoning abilities. This update has sparked heated discussions in the industry regarding the relationship between computing power and algorithms, especially at the recently concluded 2025 GTC conference, where industry insiders emphasized that efficient models will not reduce the demand for chips, and future computing needs will only increase.

The Symbiotic Evolution of Computing Power and Algorithms

In the field of AI, the enhancement of computing power provides a foundation for complex algorithms to run, while the optimization of algorithms can utilize computing power more efficiently. This symbiotic relationship is reshaping the AI industry landscape:

1. Divergence in technical routes: some companies pursue the construction of super-large computing power clusters, while others focus on optimizing algorithm efficiency.
2. Industry Chain Restructuring: Chip manufacturers become leaders in AI computing power through ecosystems, while cloud service providers lower deployment thresholds through elastic computing power services.
3. Resource allocation adjustment: Enterprises seek a balance between investment in hardware infrastructure and the development of efficient algorithms.
4. Rise of Open Source Communities: Open source models promote algorithm innovation and the sharing of computing power optimization results, accelerating technological iteration.

Technical Innovations of DeepSeek

The success of DeepSeek is inseparable from its technological innovations, which are mainly reflected in the following aspects:

Model Architecture Optimization

Using a Transformer + MOE combined architecture, introducing a Multi-Head Latent Attention mechanism (MLA). This architecture acts like a super team, with the Transformer handling regular tasks, the MOE functioning like an expert group addressing specific issues, and the MLA allowing the model to flexibly focus on important details.

Innovative Training Methods

Propose an FP8 mixed precision training framework that dynamically selects computational precision based on training requirements, improving training speed and reducing memory usage while ensuring accuracy.

Improvement in inference efficiency

Introducing Multi-Token Prediction (MTP) technology, which predicts multiple tokens at once, significantly improving inference speed and reducing costs.

Breakthrough in Reinforcement Learning Algorithms

The new GRPO algorithm optimizes the model training process, achieving a balance between performance enhancement and cost reduction by minimizing unnecessary computations.

These innovations have formed a complete technical system that reduces computing power requirements across the entire chain from training to inference, allowing ordinary consumer-grade graphics cards to run powerful AI models, significantly lowering the barriers to AI applications.

Impact on Chip Manufacturers

DeepSeek optimizes algorithms through the PTX layer, which has a dual impact on chip manufacturers: on one hand, it deepens the binding with hardware and the ecosystem, potentially expanding the overall market size; on the other hand, algorithm optimization may change the market demand structure for high-end chips.

Significance for China's AI Industry

DeepSeek's algorithm optimization provides a technological breakthrough path for China's AI industry. Against the backdrop of restrictions on high-end chips, the idea of "software complementing hardware" reduces dependence on top imported chips. Upstream computing power service providers can extend the hardware usage cycle through software optimization, while downstream lowers the threshold for AI application development, giving rise to more AI solutions in vertical fields.

The Profound Impact of Web3 + AI

Decentralized AI Infrastructure

DeepSeek's innovations enable decentralized AI inference. The MoE architecture is suitable for distributed deployment, and the FP8 training framework reduces the demand for high-end computing resources, allowing more computing resources to join the node network.

Multi-Agent Systems

1. Intelligent Trading Strategy Optimization: By the collaborative operation of multiple specialized agents, it helps users achieve higher returns.
2. Automation of smart contract execution: Achieving automation of more complex business logic.
3. Personalized portfolio management: AI searches for the best staking or liquidity provision opportunities in real-time based on user needs.

DeepSeek seeks breakthroughs through algorithmic innovation, opening up differentiated development paths for the AI industry. The future development of AI will be a competition of collaborative optimization between computing power and algorithms, with innovators redefining the rules of the game with new ideas.