Decentralized AI computing network Gonka has detailed significant adjustments to its Proof of Contribution (PoC) mechanism, aimed at enhancing network stability and efficiency as it scales.
Key Technical Adjustments:
*Unified Model Operation:** Merging PoC and AI inference tasks under a single, large foundational model to reduce system overhead from switching between consensus and computation.
*Faster PoC Activation:** Transitioning from a delayed to a near real-time (<5 seconds) activation, minimizing GPU idle time and resource waste.
*Refined Computing Power Weighting:** Optimizing the formula for calculating hardware contributions to better reflect the true computational cost of running different AI models, aligning incentives with actual network utility.
Strategic Rationale:
Co-founder David explained that these are not short-term fixes but necessary evolutions of the network's consensus layer to ensure stability and security under rapidly growing computational loads. The goal is to build a foundation capable of supporting larger, more complex AI workloads in the future.
Addressing Ecosystem Balance:
The team acknowledged that earlier mechanisms could favor smaller models in terms of token output. The recalibration aims to prevent long-term structural imbalances, ensuring the network incentivizes the hardware and tasks needed for its evolution toward higher computing density and complex tasks.
Inclusive Participation:
Gonka emphasized that single-card and mid-tier GPU owners can still participate effectively through mining pools, flexible epoch-based staking, and inference tasks. The long-term vision is to support a heterogeneous ecosystem of hardware within a unified network.
All major protocol changes are governed via on-chain community voting. The network plans to gradually support more model types and AI task forms, aiming to foster a sustainable, transparent global marketplace for decentralized computing power. $BTC $ETH $BNB #CryptoNews
