Yeah most peple still think AI scales by throwing more GPUs at one giant model. I don’t think that model survives once thousands of specializd AI agents start operating together. The bottleneck becomes memory, infrastructure cost, and coordination. That’s where OpenLedger caught my attention.
Instead of keeping every fine-tuned model permanently loaded, OpenLoRA activates lightweight adapters only when needed. It sounds simple, but economically it changes a lot. Lower GPU memory usage means cheaper deployment, faster agent switching, and more reallistic decentralized AI infrastructure.
What surprised me is how overlooked this layer still feels. The market talks endlessly about AI agents, but very little about the infrastructure required to run them efficiently at scale. In reality, scalability decides adoption.
@OpenLedger seems focused on making AI blockchain infrastructure sustainable, not just spculative. If decentralized AI grows, efficient coordination layers like this may matter far more than people expect.