IO Price(IO)

IO.net is developing a decentralized network for GPU computing that aims to make high-performance compute as easy to reach as the internet. Through the platform, customers can rent GPU and CPU capacity at costs that can be up to 90% lower than conventional providers, while people who own hardware can earn rewards by contributing their compute resources. The project is built on Ray framework technology—used by OpenAI for GPT models—and frames its decentralized, global resource pool as a way to broaden access to AI computing.

IO.net’s roadmap prioritizes scaling its decentralized GPU infrastructure and expanding platform functionality. Planned features include serverless inference, cloud gaming, and pixel streaming. The team also plans to improve AI model integration and grow a library of pre-built templates designed to make deployments easier. Additional initiatives focus on strengthening automated scaling and introducing more advanced resource allocation algorithms. Over the longer term, IO.net aims to further widen access to AI compute and deliver new tools that support increasing demand across AI applications and distributed computing.

IO.net was formed by a team with significant experience in quantitative trading systems and high-frequency trading infrastructure. Before IO.net launched in June 2022, the group built institutional-grade trading systems capable of monitoring more than 1,000 stocks and 150 cryptocurrencies in real time. Their expertise spans MLOps, DevOps, and distributed systems, including hands-on experience implementing Ray.io technology for large-scale distributed computing use cases. That background in building high-performance trading infrastructure has influenced IO.net’s architecture and the capabilities it offers.

IO.net runs on a tailored implementation of the Ray framework that has been modified for decentralized environments. It uses scheduling algorithms alongside autoscaling to allocate workloads across a worldwide supply of GPU and CPU resources, supported by a worker management layer that continuously tracks device performance, uptime, and security compliance. Network devices must meet strict uptime standards and are checked through recurring proof-of-work verification to help ensure dependable service. The stack also includes IO Explorer for real-time monitoring and visibility into network activity, while still safeguarding sensitive information. IO.net supports many GPU categories, from consumer cards to datacenter-grade units, and applies model-specific multipliers and staking requirements to structure participation. Reliability and security are reinforced through layered controls such as SOC 2 compliance checks, proof-of-timelock mechanisms, and ongoing performance monitoring. This design can expand from single-GPU tasks to distributed jobs spanning thousands of processors, supporting everything from individual AI workloads to large-scale model training.

The network economy is based on IO Coin, which functions as the main token for transactions and participant rewards. Eligible devices receive block rewards on an hourly schedule, with 95% of those rewards assigned to GPUs and 5% to CPUs. IO.net also enforces staking requirements in IO tokens, and the required stake varies by GPU model—for instance, an H100 80GB HBM3 requires staking 2000 IO tokens, while an RTX 4060 requires 200 IO tokens. Participants can generate earnings through multiple channels: rental revenue from leasing their hardware, block rewards for maintaining availability on the network, and additional incentives tied to meeting performance and uptime targets. Reward allocation is guided by a scoring system that accounts for connectivity tier, hardware capability, and proof-of-work verification.