The Intersection of AI and DePIN: The Rise of Distributed GPU Computing Networks

Since 2023, AI and DePIN have become popular trends in the Web3 space, with AI having a market value of about $30 billion and DePIN having a market value of about $23 billion. This article focuses on the intersection of the two and explores the development of related protocols.

In the AI technology stack, the DePIN network provides practicality for AI through computing resources. The development of large tech companies has led to a shortage of GPUs, making it difficult for other developers to obtain enough GPUs for computation. This often results in developers opting for centralized cloud providers, but due to the need to sign inflexible long-term high-performance hardware contracts, efficiency is low.

DePIN essentially provides a more flexible and cost-effective alternative, incentivizing resource contributions that align with network goals through token rewards. In the AI field, DePIN crowdsources GPU resources from individual owners to data centers, forming a unified supply for users who need access to hardware. These DePIN networks not only offer customizability and on-demand access for developers requiring computing power but also provide additional income for GPU owners.

There are numerous AI DePIN networks in the market, and this article will explore the roles, objectives, and highlights that have been achieved by each protocol.

Overview of AI DePIN Network

Render is a pioneer in providing GPU computing power in a P2P network, initially focused on rendering graphics for content creation, and later expanded its scope to include computational tasks ranging from Neural Radiance Fields ( NeRF ) to generative AI through the integration of tools like Stable Diffusion.

Highlights:

1. Founded by OTOY, a cloud graphics company with Oscar-winning technology.

2. GPU networks have been used by major entertainment companies such as Paramount Pictures, PUBG, and Star Trek.

3. Collaborate with Stability AI and Endeavor to integrate AI models with 3D content rendering workflows using Render's GPU.

4. Approve multiple computing clients and integrate more GPUs from the DePIN network.

Akash positions itself as a "supercloud" alternative to traditional platforms like AWS( that support storage, GPU, and CPU computing ). With developer-friendly tools such as the Akash container platform and Kubernetes-managed compute nodes, it enables seamless software deployment across environments, allowing it to run any cloud-native application.

Highlights:

1. A wide range of computing tasks from general computing to web hosting.

2. AkashML allows its GPU network to run over 15,000 models on Hugging Face while integrating with Hugging Face.

3. Akash hosts some noteworthy applications, such as Mistral AI's LLM model chatbot, Stability AI's SDXL text-to-image model, and Thumper AI's new foundational model AT-1.

4. The platform for building the metaverse, AI deployment, and federated learning is leveraging Supercloud.

io.net provides access to distributed GPU cloud clusters, which are specifically designed for AI and ML use cases. It aggregates GPUs from data centers, crypto miners, and other decentralized networks. The company was previously a quantitative trading firm and shifted to its current business after a significant increase in the prices of high-performance GPUs.

Highlights:

1. Its IO-SDK is compatible with frameworks such as PyTorch and Tensorflow, and its multi-layer architecture can automatically and dynamically scale according to computing requirements.

2. Supports the creation of 3 different types of clusters, which can be started within 2 minutes.

3. Collaborate with other DePIN networks ( such as Render, Filecoin, Aethir, and Exabits ) to integrate GPU resources.

Gensyn provides GPU computing power focused on machine learning and deep learning computations. It claims to achieve a more efficient verification mechanism than existing methods by combining concepts such as proof of learning, graph-based precise positioning protocols, and incentive games involving staking and slashing of computing providers.

Highlights:

1. The expected hourly cost of a V100 equivalent GPU is about $0.40, significantly saving costs.

2. The pre-trained base model can be fine-tuned for more specific tasks through proof stacking.

3. These foundational models will be decentralized, globally owned, and provide additional functionalities beyond the hardware computing network.

Aethir is specifically equipped with enterprise GPUs, focusing on compute-intensive areas, primarily AI, machine learning ( ML ), cloud gaming, and so on. The containers in its network act as virtual endpoints for executing cloud-based applications, transferring workloads from local devices to containers to achieve low-latency experiences. To ensure high-quality service for users, they relocate GPUs closer to data sources based on demand and location, thereby adjusting resources.

Highlights:

1. In addition to AI and cloud gaming, Aethir has also expanded into cloud mobile services, launching a decentralized cloud smartphone in collaboration with APhone.

2. Establish extensive cooperation with large Web2 companies such as NVIDIA, Super Micro, HPE, Foxconn, and Well Link.

3. Collaborate with multiple partners in Web3 such as CARV, Magic Eden, Sequence, Impossible Finance, etc. (.

Phala Network serves as the execution layer for Web3 AI solutions. Its blockchain is a trustless cloud computing solution designed to address privacy issues by using a Trusted Execution Environment )TEE(. Its execution layer is not used as a computing layer for AI models, but rather enables AI agents to be controlled by on-chain smart contracts.

Highlights:

1. Act as a verifiable computing coprocessor protocol, while enabling AI agents to access on-chain resources.

2. Its AI agent contracts can access top large language models such as OpenAI, Llama, Claude, and Hugging Face through Redpill.

3. The future will include zk-proofs, multi-party computation )MPC(, fully homomorphic encryption )FHE( and other multi-proof systems.

4. Future support for H100 and other TEE GPUs to enhance computing power.

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Project Comparison

| | Render | Akash | io.net | Gensyn | Aethir | Phala | |--------|-------------|------------------|---------------------|---------|---------------|----------| | Hardware | GPU & CPU | GPU & CPU | GPU & CPU | GPU | GPU | CPU | | Business Focus | Graphic 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 task | 20% USDC, 4% AKT | 2% USDC, 0.25% reserve fee | Low fees | 20% per session | Proportional to the staked amount | | Security | Render Proof | Stake Proof | Computation Proof | Stake Proof | Render Capability Proof | Inherited from Relay Chain | | Completion Proof | - | - | Time Lock Proof | Learning Proof | Rendering Work Proof | TEE Proof | | Quality Guarantee | Dispute | - | - | Verifier and Reporter | Checker Node | Remote Proof | | GPU Cluster | No | Yes | Yes | Yes | Yes | No |

) Importance

Availability of Clusters and Parallel Computing

The distributed computing framework implements a GPU cluster, providing more efficient training without compromising model accuracy, while also enhancing scalability. Training complex AI models requires powerful computing capabilities, often relying on distributed computing to meet the demands. OpenAI's GPT-4 model has over 1.8 trillion parameters and was trained over a period of 3-4 months using approximately 25,000 Nvidia A100 GPUs across 128 clusters.

Previously, Render and Akash only provided single-use GPUs, which may have limited the market demand for GPUs. However, most key projects have now integrated clusters for parallel computing. io.net collaborates with other projects like Render, Filecoin, and Aethir to incorporate more GPUs into its network, and has successfully deployed over 3,800 clusters in the first quarter of 2024. Although Render does not support clusters, it operates similarly to clusters by breaking down a single frame into multiple different nodes to process different ranges of frames simultaneously. Phala currently only supports CPUs but allows for the clustering of CPU workers.

Incorporating cluster frameworks into AI workflow networks is very important, but the number and type of cluster GPUs required to meet the needs of AI developers is another issue.

Data Privacy

Developing AI models requires the use of large datasets, which may come from various sources and take different forms. Sensitive datasets may face the risk of exposure to model providers. Taking adequate security measures is crucial for the use of AI. Therefore, having various data privacy methods is essential to return data control to data providers.

Most projects use some form of data encryption to protect data privacy. Render uses encryption and hashing when publishing rendering results back to the network, while io.net and Gensyn employ some form of data encryption. Akash uses mTLS authentication, allowing only tenant-selected providers to receive data.

io.net has recently partnered with Mind Network to launch fully homomorphic encryption ###FHE(, allowing encrypted data to be processed without the need for prior decryption. This innovation can better ensure data privacy than existing encryption technologies.

Phala Network introduces a Trusted Execution Environment ) TEE (, which connects to the secure area within the main processor of the device. Through this isolation mechanism, it can prevent external processes from accessing or modifying data, regardless of their permission level. In addition to TEE, it also incorporates the use of zk-proofs in its zkDCAP validator and jtee command line interface, for integration with programs that use RiscZero zkVM.

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) Calculation Completion Certificate and Quality Inspection

The GPUs provided by these projects can offer computing power for a range of services. Due to the wide spectrum of services, from rendering graphics to AI computations, the final quality of such tasks may not always meet user standards. A proof of completion can be used to indicate that the specific GPU rented by the user was indeed used to run the required service, and quality checks are beneficial for users requesting the completion of such work.

After the computation is completed, both Gensyn and Aethir will generate proofs to indicate that the work has been completed, while the proof from io.net indicates that the performance of the rented GPU has been fully utilized without any issues. Both Gensyn and Aethir will conduct quality checks on the completed computations. For Gensyn, it uses validators to re-run parts of the generated proofs to cross-check with the proofs, while reporters serve as an additional layer of checks on the validators. Aethir uses check nodes to determine service quality and penalizes services that fall below standards. Render recommends using a dispute resolution process, where if the review committee finds issues with a node, that node will be penalized. After Phala is completed, a TEE proof will be generated to ensure that the AI agent performs the required operations on-chain.

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 ( | - |

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) Requirements for high-performance GPUs

Due to the need for high-performance GPUs for AI model training, developers tend to use GPUs such as Nvidia's A100 and H100. The inference performance of the H100 is 4 times faster than that of the A100.