Market Analysis

The development of AI and the demand in the compute market are closely intertwined. As AI technology continues to advance, its applications across various industries expand, driving substantial demand for computing power.

Pain Points in Traditional AI Compute Provisioning

• Long Deployment Cycles: Traditional AI compute centers often require extensive upfront preparation and configuration, including hardware procurement, software installation, and network setup, leading to long cycles from planning to actual deployment.

• Geographical Limitations: The physical location of centralized compute centers limits their service coverage. This can result in inefficient access and use of resources, especially in areas with poor internet connectivity.

• High Costs: Building and maintaining a centralized AI compute center involves significant capital investment, including hardware costs, electricity consumption, cooling systems, and maintenance fees by professionals. These costs are ultimately passed on to users, making it difficult for small teams and startups to afford.

• High Barriers to Entry: Traditional AI compute services often require users to have a technical background to effectively utilize and manage these resources, limiting participation and innovation from non-technical users.

• Supply Chain Shortages: The rapid development of AI technology has led to a sharp increase in demand for high-performance hardware, such as GPUs, straining the supply chain and increasing hardware procurement costs.

• High Hardware Procurement Costs: To meet the demands of AI computing, users need to purchase expensive hardware, which is a significant investment for many organizations.

• Demanding Facility Requirements: AI compute centers require specific site conditions such as stable power supply, effective cooling systems, and secure physical environments, adding complexity and cost to construction and operation.

• Data Security Management: Centralized storage and processing of data may face higher security risks, including data breaches, unauthorized access, and hacking.

• Uneven Resource Utilization: In traditional AI compute provisioning models, there can be uneven utilization of resources, sometimes leading to overuse, while at other times resources may go idle, resulting in wasted resources.

These issues have prompted the exploration of new models for AI compute provisioning, such as the distributed AI compute services offered by platforms like DSC, aimed at decentralizing resources, reducing costs, and simplifying operations.

Advantages of Distributed AI Compute Services

• Cost-Effective: Distributed AI compute services reduce the need for expensive hardware investments by leveraging existing hardware resources globally. Users can purchase compute power on demand without bearing the high costs of purchasing and maintaining hardware.

• Scalability: Distributed systems naturally have better horizontal scalability. As AI compute demands grow, the service capacity can be expanded by adding more nodes, rather than making a one-time investment to establish a large centralized center.

• Flexibility and Availability: Distributed AI compute services allow users to adjust the amount of resources needed based on actual demand. This flexibility means users can increase resources when needed and reduce them when demand decreases, optimizing costs.

• Data Security and Privacy: Distributed storage and processing of data reduce the risk of single points of failure and enhance data privacy through localized data handling. Additionally, distributed systems can more easily implement data encryption and security measures to prevent unauthorized access and data leaks.

• Low Latency: Since distributed AI compute services can assign computing tasks to nodes closer to the user, they can reduce data transmission times, providing lower latency and faster response times.

• Environmentally Friendly: Distributed AI compute services can reduce energy consumption through more effective resource utilization and distributed computing loads, thereby minimizing environmental impact.

• Fault Tolerance: Distributed systems have stronger fault tolerance. Even if some nodes fail, the overall system can continue to operate, as tasks can be reassigned to other nodes.

• User-Friendly Interface: Distributed AI compute service platforms typically offer easier-to-use interfaces, making it simple for non-technical users to access and use AI compute resources.

• Promoting Innovation: The openness and ease of use of distributed AI compute services encourage more developers and innovators to participate in AI application development, thus driving innovation and development across the industry.

• Community and Ecosystem: Distributed AI compute service platforms are often built on strong communities and ecosystems that provide technical support, best practice sharing, and collaboration opportunities, further enhancing the platform's value.

Integration of AI Compute with DePIN

The DePIN track covers a wide range of categories, aiming to establish a decentralized infrastructure network that allows users to share idle hardware resources, such as storage space and GPU power, through blockchain and token incentives. This network can provide AI applications with lower-cost computing and data resources.

Cloud computing and compute market domains that integrate the DePIN concept show significant growth potential. By utilizing idle GPU power, not only can users access cheaper compute resources, but participants can also gain economic benefits.

A decentralized compute market allows compute providers and demanders to connect directly, improving transaction efficiency and transparency. This type of compute market helps to stimulate more individuals and businesses to participate, promoting the development of the entire decentralized compute field.

By using a decentralized compute network architecture, DePIN can enhance the risk-resistance capacity of the compute system. Compared to