AI and DePIN: The Decentralization Revolution of Computing Resources

Recently, AI and DePIN have become popular trends in the Web3 space, with market values reaching 30 billion and 23 billion USD respectively. This article will explore the intersection of the two, particularly the development of AI DePIN networks.

In the AI technology stack, the DePIN network brings practical value to AI by providing computing resources. Due to the GPU shortage caused by large tech companies, other developers find it difficult to obtain sufficient GPU resources to build AI models. The traditional approach is to choose centralized cloud services, but this often requires signing inflexible long-term contracts.

The DePIN network offers a more flexible and cost-effective alternative. It integrates decentralized GPU resources through token incentives, providing a unified supply for users in need of computing power. This not only allows developers to obtain customized on-demand computing resources but also provides GPU owners with an additional source of income.

Currently, there are multiple AI DePIN networks available on the market, each with its own characteristics. This article will provide a brief introduction and comparison of the main projects:

Render is a pioneer in GPU computing networks, originally focused on content rendering, and later expanded into the AI computing field. It has been adopted by major companies like Paramount Pictures and collaborated with AI companies like Stability AI.

Akash is positioned as a "super cloud" alternative to traditional cloud services, supporting storage, GPU, and CPU computing. Its AkashML can run tens of thousands of models on Hugging Face and has hosted several well-known AI projects.

io.net focuses on AI and machine learning use cases, aggregating GPU resources from data centers, miners, and more. It supports the rapid deployment of various types of GPU clusters.

Gensyn focuses on machine learning computation, improving efficiency through an innovative verification mechanism. It can fine-tune pre-trained models and plans to provide decentralized foundational models.

Aethir focuses on the enterprise-level GPU market, targeting compute-intensive areas such as AI and cloud gaming. It adjusts resource allocation according to demand and collaborates with several large technology companies.

Phala Network acts as the execution layer for Web3 AI solutions, allowing AI agents to be controlled by on-chain smart contracts. It uses a Trusted Execution Environment (TEE) to protect privacy.

These projects have distinct characteristics in terms of hardware types, business focus, pricing models, and so on. Key differences include:

• GPU clusters and parallel computing capabilities: Most projects now support GPU clusters to meet the needs of complex AI models.

• Data privacy protection: Utilizing various technical means such as encryption and TEE to protect sensitive data.

• Proof of computation completion and quality inspection: Verifying whether the computation has been completed as required through various mechanisms.

• High-performance GPU supply: Some projects have integrated thousands of A100/H100 GPUs to meet the training needs of large models.

• Utilization of consumer-grade GPU/CPU: Some projects also integrate the idle computing power of ordinary users to serve small-scale computing needs.

The AI DePIN network is still in its early stages and faces some challenges. However, with the growth of hardware supply and task volume, these networks are gradually proving their value. In the future, they are expected to play an important role in the trillion-dollar AI market, providing developers with more economical and efficient computing resource options.