Consensus-Based Deployment Protocol for Distributed LLM Adapter Management

Abstract

Parameter-efficient fine-tuning techniques enable rapid model customization through lightweight adapter artifacts; however, the distributed deployment of these artifacts introduces critical consistency challenges. Standard orchestration strategies create temporal windows where heterogeneous model versions serve production traffic simultaneously, exposing users to unvalidated or degraded model behavior. The article presents a consensus-based deployment protocol addressing fundamental gaps in continuous fine-tuning infrastructure. The protocol partitions inference clusters into validation and serving planes, establishing a clear separation between experimental evaluation and production workloads. A quorum-gated state machine controls adapter propagation, requiring majority agreement among validation nodes before authorizing cluster-wide synchronization. The beta barrier mechanism prevents premature deployment by algorithmically blocking inference nodes from loading new adapters until validation completes successfully. Formal verification demonstrates strong safety guarantees where no production request reaches untested model versions, regardless of timing variations or partial system failures. The protocol achieves eventual convergence to a uniform cluster state while tolerating minority node failures through majority voting requirements. Theoretical analysis proves monotonic progression between stable configurations without indefinite divergence or version oscillation. The architecture complements existing serving engines by functioning as a control plane protocol governing adapter lifecycle management across distributed inference fleets.