EFFICIENT LLM INFERENCE ON MCP SERVERS: A SCALABLE ARCHITECTURE FOR EDGE-CLOUD AI DEPLOYMENT

Organizations need to create deployable LLM models which use open frameworks with privacy-protected standards. Cloud-based inference remains popular yet it forces delays along with resource wastage and exposes security threats. The processing limitations of edge computing create an isolated drawback due to the closeness of data sources. The proposed research presents an edge-cloud joined system with MCP servers that delivers efficient LLM inference workload offloading. Research results showed that system performance delays occurred concurrently with resource consumption measurements and throughput capacity assessments and energetic performance metrics measurements. Through the system analysis tool users can evaluate performance and resource utilization and throughput performance simultaneously. Model predictions through this system achieve accurate results while the system performs at industry standards regarding latency and throughput acceleration. Within the proposed framework researchers established edge-Cloud LLM orchestration capabilities to optimize AI systems deployment in current real-world scenarios.