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

• Pioneer of P2P network providing GPU computing power
• Initially focused on content creation rendering, later expanded to AI computing tasks
• Founded by the cloud graphics company OTOY, which possesses Oscar-winning technology
• The GPU network is used by major companies such as Paramount Pictures and PUBG.
• Collaborate with Stability AI and others to integrate AI models with 3D content rendering workflows.

Akash

• Positioned as a "super cloud" alternative that supports storage, GPU, and CPU computing.
• Developer-friendly tools such as container platforms and computing nodes managed by Kubernetes
• AkashML allows GPU networks to run over 15,000 models on Hugging Face.
• Hosted applications such as the Mistral AI LLM model chatbot

io.net

• Provide access to distributed GPU cloud clusters specifically designed for AI and ML use cases.
• IO-SDK is compatible with frameworks such as PyTorch and Tensorflow
• Supports the creation of 3 different types of clusters, which can be launched within 2 minutes.
• Collaborate with Render, Filecoin, and other DePIN networks to integrate GPUs

Gensyn

• Provide GPU computing power focused on machine learning and deep learning.
• Implementing concepts such as proof of learning to achieve a more efficient verification mechanism
• The cost per hour for a V100 equivalent GPU is about $0.40, significantly reducing costs.
• Provide a decentralized global shared foundational model

Aethir

• Specifically equipped with enterprise GPUs, focusing on computation-intensive fields such as AI, ML, and cloud gaming.
• Optimize GPU resource allocation based on demand and location
• Expand to cloud phone services, launch decentralized cloud smartphones in collaboration with APhone.
• Establish extensive cooperation with Web2 giants such as NVIDIA and Super Micro

Phala Network

• As the execution layer of Web3 AI solutions
• Use Trusted Execution Environment ( TEE ) to address privacy issues
• The execution layer enables AI agents to be controlled by on-chain smart contracts.
• AI agent contracts can integrate with top large language models like OpenAI, Llama, etc.

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.