Io.net Unveils New Token Model to Enhance Decentralized AI Compute Network

Io.net has introduced a revamped token model for its decentralized AI compute network, aiming to better align token rewards with actual usage and demand. The new framework, named the Incentive Dynamic Engine (IDE), is designed to optimize how native tokens are issued and allocated among hardware providers, users, and investors. Since June, Io.net’s distributed network of graphics processing units (GPUs) has facilitated over $20 million in compute leases, highlighting the growing demand for decentralized AI training and inference workloads.
The IDE employs a dynamic control system that adjusts token release and payout levels based on real-time network conditions. This innovative approach contrasts with previous models that relied on high token rewards to stimulate supply, which often resulted in persistent inflation and misalignment with end-user demand. By stabilizing income for GPU providers across more than 130 countries and promoting predictable pricing for users, Io.net aims to create a more reliable and open market for compute resources. Gaurav Sharma, CEO of Io.net, emphasized the importance of transitioning from centralized hyperscalers to decentralized markets for compute, asserting that IDE represents a pioneering step toward this goal.
Despite the potential of decentralized compute networks, large enterprises have been hesitant to adopt them due to concerns about reliability and transparency. Io.net’s IDE framework addresses these issues by enhancing visibility into network data, thereby fostering trust among corporate users. The model also includes deflationary mechanisms that link token flows to real-world usage, reducing income volatility for GPU providers and rewarding investors who support actual network utilization. With the IDE proposal currently open for community feedback, Io.net plans to finalize its tokenomics design by March and roll out the new model in the second quarter of 2026, positioning itself as a key player in the decentralized AI landscape.
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