AI Computing Power Demand Soars, Distributed Computing Platform Becomes a New Trend

With the launch of large language models such as GPT-4 by OpenAI, and the rise of various AI image generation models, the demand for high-performance computing resources like GPUs has surged dramatically. According to statistics, the global AI market size reached $24.18 billion in 2023, and it is expected to grow to $73.87 billion by 2030. Meanwhile, the cloud services market has also grown by about 14%, partly due to the urgent demand for GPU Computing Power in the AI field.

In this context, how to efficiently utilize and optimize the large datasets and computing resources required for AI training has become a key issue. The core of AI infrastructure lies in addressing problems such as data processing efficiency, model reliability, and application scalability.

The decentralized design of blockchain provides new ideas for distributed computing. By utilizing idle GPU resources to build distributed systems, the computing power costs for AI startups can be reduced. This has prompted traditional cloud service providers to start offering GPU leasing services, while also giving rise to a number of innovative projects focused on distributed AI computing power.

io.net is a distributed Computing Power platform that integrates Solana blockchain technology. It gathers idle GPU resources from independent data centers and cryptocurrency miners, collaborating with projects like Filecoin and Render to jointly establish a massive Computing Power network with over 1 million GPUs.

io.net is built on the Ray.io machine learning framework, providing distributed computing resources for AI applications across various stages, from reinforcement learning and deep learning to model tuning and execution. The platform adopts an open access mechanism, allowing anyone to join the network as a Computing Power provider or developer. The system dynamically adjusts prices based on the complexity of computing tasks, urgency, and resource availability, achieving market-based pricing. The backend also considers factors such as GPU type, availability, requester's location, and reputation to intelligently match GPU suppliers and developers.

$IO is the native token of io.net, serving as a medium of exchange within the network. Using $IO allows for a 2% reduction in order fees. At the same time, $IO plays an important incentive role in the normal functioning of the network: holders can stake tokens to nodes, and nodes need to have stakes in order to earn idle period rewards.

Currently, the market capitalization of the $IO token is approximately $360 million, with a fully diluted valuation of about $3 billion. The maximum total supply is 800 million tokens, of which 500 million were allocated at the time of token generation, and the remaining 300 million will be gradually released over 20 years. The current circulation is 95 million tokens, mainly coming from 75 million tokens unlocked for ecological research and community building, as well as 20 million tokens from Binance Launchpool mining rewards.

io.net also has a token buyback and burn mechanism. The buyback funds come from the order reservation fees and transaction fees collected by the platform, and the specific buyback and burn quantities depend on the current price of $IO.

Projects similar to io.net include Akash, Nosana, OctaSpace, and Clore.AI, all aimed at addressing the computing power needs of AI models. However, io.net is currently the only project that allows anyone to join and provide Computing Power without barriers, supporting a variety of Computing Power resources, including consumer-grade GPUs from the 30 series. This gives io.net the potential to leverage token economics to unlock more graphics card resources.

The launch of io.net has attracted significant attention from the market. Although the token price experienced fluctuations, it ultimately returned to a relatively reasonable valuation range, many testnet participants did not achieve the desired returns. Whether io.net can truly realize its goal of providing comprehensive Computing Power support for AI applications in the future, and how much actual demand can be retained after the testnet, still remains to be seen.