The AI wave is sweeping the world, but the surge in computing volume has also led to a shortage of GPU supply. At the same time, Crypto and AI solutions have begun to make waves, such as Akash Network, Ritual, etc., all performing well. Just yesterday, new entrant io.net also completed a $30 million Series A round of financing, led by Hack VC, with participation from Foresight Ventures, Multicoin Capital, Delphi Digital, Solana Labs and others.
In a recent tweet, IO stated that it has launched a points reward program and will continue until the end of April. Additionally, token IO will be launched at the end of April.
io.net is a decentralized GPU network designed to provide computing resources for the field of machine learning (ML). The network brings together more than 1 million GPUs from independent data centers, cryptocurrency miners, and projects like Filecoin or Render to provide powerful computing power. The founder of the project is Ahmad Shadid, who came up with the idea in order to reduce costs when building a GPU computing network for the machine learning quantitative trading company Dark Tick, and later attracted widespread attention at the Austin Solana Hacker House.
Before proposing a solution, we might as well understand what disadvantages ordinary users or organizations face in obtaining computing resources? One is limited availability, accessing the hardware using cloud services like AWS, GCP or Azure often takes weeks, and popular GPU models on the market are often unavailable. Second, there is little choice. For example, users have almost no choice in terms of GPU hardware, location, security level, latency, etc. Third, the cost is high: Obtaining high-quality GPUs is very expensive, costing hundreds of thousands of dollars per month for training and inference.
#io.net solves the problem by aggregating GPUs that are underutilized (such as standalone data centers, crypto miners, and crypto projects like Filecoin, Render, etc.). These resources combined in DePIN enable engineers to gain significant computing power in the system. It allows ML teams to build inference and model serving workflows across distributed GPU networks and leverage distributed compute libraries to orchestrate and batch training jobs so that they can be parallelized across many distributed devices using data and model parallelism.
Additionally, io.net leverages a distributed computing library with advanced hyperparameter tuning to check for optimal results and optimize scheduling while simplifying the specification of search patterns. It also integrates open source reinforcement learning libraries, supports production-grade highly distributed RL (reinforcement learning) workloads, and provides a simple and easy-to-use API.
According to the official website, the product portfolio includes three parts: IO Cloud, IO Worker and IO Explorer. The main function of IO Cloud is to deploy and manage decentralized GPU clusters allocated on demand. It is tightly integrated with IO-SDK and provides a comprehensive solution for scaling AI and Python applications. IO Workers are designed to provide powerful computing power to support a variety of workloads, including tasks such as data analysis and deep learning. IO Explorer is a tool for exploring and visualizing data, helping users better understand and analyze their data. Together, these three parts form a comprehensive solution that provides users with flexible and efficient computing resource management and data processing tools. Through IO Cloud's GPU cluster deployment and management capabilities, users can easily expand their AI applications and Python applications, while IO Worker provides powerful computing support
IO Worker mainly provides users with comprehensive and friendly interface to effectively manage their provisioning operations on web applications, the scope of the product includes those related to user account management, computing activity monitoring, real-time data display, temperature and power consumption tracking, installation assistance, wallet management, security measures and profitability calculations function.
IO Explorer mainly provides users with comprehensive statistics and visualizations of all aspects of the GPU cloud. It enables users to easily monitor, analyze and understand every detail of the io.net network by providing complete visibility into network activity, vital statistics, data points and reward transactions.
According to the information on the official website, its token functions include allocating incentives to IO Workers, rewarding AI and ML deployment teams for continued use of the network, balancing local demand and supply, pricing IO Worker computing units, and community governance, etc. .
In order to avoid payment problems caused by IO currency price fluctuations, io.net has specially developed a stable currency IOSD, which is pegged to the US dollar. 1IOSD is always equal to 1 USD. IOSD can only be obtained by destroying IO. Additionally, io.net is considering some mechanisms to improve network functionality. For example, IO Workers might be allowed to increase their probability of being rented by staking native assets. In this case, the more assets they invest, the greater their probability of being selected. Additionally, AI engineers staking native assets can prioritize access to high-demand GPUs.
IO tokens are mainly used for two groups: the demand side and the supply side. For the demand side, each computing job is priced in US dollars, and the network will retain the payment until Homework completed. Once a node operator allocates its reward share in USD and tokens, all USD amounts will be allocated directly to the node operator, while the share allocated to tokens will be used to burn IO coins. All IO Coins minted as Compute Rewards during that period are then distributed to users based on the USD value of their coupon tokens (Compute Points).
For the supply side, it includes availability rewards and computing rewards. Among them, the reward is calculated for jobs submitted to the network. Users can select the time preference "duration of cluster deployment in hours" and receive cost estimates from the io.net pricing oracle. In terms of availability rewards, the network will randomly submit small test jobs to evaluate which nodes run regularly and are well able to accept jobs from the demand side.
It is worth mentioning that both the supply side and the demand side have a reputation system that accumulates points based on computing performance and network participation to obtain rewards or discounts.
In addition, io.net also sets up an ecological growth mechanism, including staking, invitation rewards and network fees. IO coin holders can choose to stake their IO tokens to node operators or users. Once staked, stakers will receive 1-3% of all rewards earned by participants. Users can also invite new network participants to join and share part of their future income. Network fees are set at 5%.
At present, the official airdrop method has not been clearly stated, but the author speculates that it may be distributed to some active users of Solana’s ecosystem, such as RENDER or FIL currency holders. The other method has a relatively high threshold, that is, running a node, which has certain requirements for personal computer configuration and settings. For details, please refer to the official documentation.
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