AI Computing Power Demand Soars, Can io.net Become the Leader of Decentralized Computing Power Platform?

With the rapid development of AI technology, especially the launch of GPT-4 by OpenAI and the emergence of various AI image generation models, the demand for computing power resources such as GPUs has surged. According to data, the AI market size grew from $13.48 billion in 2022 to $24.18 billion in 2023, and is expected to reach $73.87 billion by 2030. At the same time, the market value of cloud services has also increased by about 14%, partially due to the rapid growth in demand for GPU computing power from the AI market.

In the face of this rapidly growing market, we need to deconstruct and seek investment opportunities from the perspective of AI infrastructure. AI infrastructure is primarily aimed at handling and optimizing the massive datasets and Computing Power required for training models, addressing issues of data processing efficiency, model reliability, and application scalability from both hardware and software aspects.

AI training models and applications require a large amount of Computing Power resources, preferring low-latency cloud environments and GPU Computing Power. In terms of software, it also includes distributed computing platforms. The decentralized design concept of blockchain makes distributed nodes the norm, which is similar to the Computing Power demands of AI. Therefore, some innovators have begun to explore the use of distributed system designs to reduce the Computing Power costs for AI startups.

io.net is a distributed Computing Power provider that combines the Solana blockchain, aiming to address the computational demand challenges in the AI and machine learning fields by utilizing distributed Computing Power resources. The project gathers over 1 million GPU resources by integrating independent data centers, idle graphics cards of cryptocurrency miners, and collaborating with other related projects.

Technically, io.net is built on the distributed computing machine learning framework ray.io, providing distributed computing resources for AI applications that require computing power in areas such as reinforcement learning, deep learning, model tuning, and model operation. The network will dynamically adjust prices based on the complexity of the computing tasks, urgency, and availability of computing power resources.

$IO is the native token of the io.net system, serving as a medium of transaction between computing power providers and buyers. Using $IO can reduce the order fee by 2%. $IO also plays an important incentive role in ensuring the normal operation of the network.

The maximum total supply of $IO is 800 million tokens, with a current circulation of 95 million tokens. The token distribution includes various aspects such as ecological research and development, community building, and testnet rewards. The project also has a token buyback and burn mechanism, with funding sourced from the platform's operating profits.

Similar projects to io.net include Akash, Nosana, OctaSpace, and Clore.AI, all of which are dedicated to solving the decentralized computing power market for AI model computation needs. In contrast, io.net is currently the only project that allows anyone to join and provide computing power resources without restrictions, supporting a wider range of GPU and CPU resources.

After io.net went live on Binance, it experienced a process of opening low and rising high, ultimately returning to a relatively rational valuation range. However, for users participating in the testnet, the results are mixed. Most users who rented GPUs but did not continuously participate in each season of the testnet did not achieve ideal returns.

io.net can achieve the goal of providing various computing power demands for AI applications, and it still needs time to verify how much real demand can remain after the test network. As a potential leader in the Decentralization Computing Power platform, the development of io.net is worth ongoing attention.