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AI and DePIN Integration: The Rise of Decentralized GPU Networks Leading a New Pattern of AI Computation
The Fusion of AI and DePIN: The Rise of Decentralized GPU Networks
Since 2023, AI and DePIN have become popular trends in the Web3 space, with market capitalizations reaching $30 billion and $23 billion, respectively. This article focuses on the intersection of the two, exploring the development of related protocols.
In the AI technology stack, the DePIN network empowers AI by providing computing resources. The development of large technology companies has led to a GPU shortage, making it difficult for other developers to obtain enough GPUs for computation. This often forces developers to choose centralized cloud service providers, but they must sign inflexible long-term high-performance hardware contracts, which are inefficient.
DePIN provides a more flexible and cost-effective alternative by utilizing token incentives for resource contributions that align with network goals. In the AI field, DePIN crowdsources GPU resources from individual owners to data centers, creating a unified supply for users. These networks not only offer customized and on-demand services for developers in need of computing power but also create additional income for idle GPU owners.
Overview of AI DePIN Network
Render
Akash
io.net
Gensyn
Aethir
Phala Network
Project Comparison
| | Render | Akash | io.net | Gensyn | Aethir | Phala | |--------|-------------|------------------|---------------------|---------|---------------|----------| | Hardware | GPU & CPU | GPU & CPU | GPU & CPU | GPU | GPU | CPU | | Business Focus | Graphics Rendering and AI | Cloud Computing, Rendering and AI | AI | AI | AI, Cloud Gaming and Telecommunications | On-chain AI Execution | | AI Task Type | Inference | Both | Both | Training | Training | Execution | | Work Pricing | Performance-Based Pricing | Reverse Auction | Market Pricing | Market Pricing | Bidding System | Equity Calculation | | Blockchain | Solana | Cosmos | Solana | Gensyn | Arbitrum | Polkadot | | Data Privacy | Encryption & Hashing | mTLS Authentication | Data Encryption | Secure Mapping | Encryption | TEE | | Work Fees | 0.5-5% per job | 20% USDC, 4% AKT | 2% USDC, 0.25% reserve fee | Low fees | 20% per session | Proportional to the staked amount | | Security | Rendering Proof | Proof of Stake | Proof of Computation | Proof of Stake | Rendering Capability Proof | Inherited from Relay Chain | | Completion Proof | - | - | Time-Lock Proof | Learning Proof | Rendering Work Proof | TEE Proof | | Quality Assurance | Dispute | - | - | Verifier and Reporter | Checker Node | Remote Proof | | GPU Cluster | No | Yes | Yes | Yes | Yes | No |
Hardware Statistics
| | Render | Akash | io.net | Gensyn | Aethir | Phala | |-------------|--------|-------|--------|------------|------------|--------| | Number of GPUs | 5600 | 384 | 38177 | - | 40000+ | - | | Number of CPUs | 114 | 14672 | 5433 | - | - | 30000+ | | H100/A100 Quantity | - | 157 | 2330 | - | 2000+ | - | | H100 Cost/Hour | - | $1.46 | $1.19 | - | - | - | | A100 Cost/Hour | - | $1.37 | $1.50 | $0.55 ( expected ) | $0.33 ( expected ) | - |
Conclusion
The AI DePIN field is still relatively emerging and faces some challenges. However, the number of tasks executed and hardware has significantly increased, highlighting the demand for alternatives to Web2 cloud providers. In the future, these decentralized GPU networks are expected to play a key role in providing developers with cost-effective computing alternatives, making a significant contribution to the landscape of AI and computing infrastructure.