Gonka Adjusts PoC Mechanism to Enhance AI Network Stability

According to Odaily, decentralized AI computing network Gonka recently explained adjustments to its PoC mechanism and model operation during a community AMA. The changes include unifying PoC and inference under a single large model, switching PoC activation from delayed to near real-time, and optimizing the calculation method for computing power weights to better reflect the actual computational costs of different models and hardware. Co-founder David stated that these adjustments are not aimed at short-term output or individual participants but are necessary evolutions of the consensus and verification structure as the network's computing power rapidly expands. The goal is to enhance stability and security under high load conditions, laying the foundation for supporting larger-scale AI workloads in the future. Addressing community discussions about the higher token output of small models at this stage, the team noted significant differences in actual computing power consumption for models of different sizes under the same token count. As the network evolves towards higher computing density and more complex tasks, Gonka is gradually aligning computing power weights with actual computational costs to prevent long-term imbalances in the computing structure, which could affect the network's overall scalability. Under the latest PoC mechanism, the network has compressed PoC activation time to within 5 seconds, reducing computing power waste from model switching and waiting, allowing GPU resources to be more effectively used for AI computation. By unifying model operation, the system overhead of switching between consensus and inference is reduced, improving overall computing power utilization efficiency. The team also emphasized that single-card and small to medium-sized GPUs can continue to earn rewards and participate in governance through mining pool collaboration, flexible participation by Epoch, and inference tasks. Gonka's long-term goal is to support the coexistence of different levels of computing power within the same network through mechanism evolution. Gonka stated that all key rule adjustments are advanced through on-chain governance and community voting. The network will gradually support more model types and AI task forms, providing a continuous and transparent participation space for GPUs of various sizes globally, promoting the long-term healthy development of decentralized AI computing infrastructure.