This article explores the critical challenges and architectural approaches in distributed model serving for multi-tenant machine learning inference systems. As organizations deploy increasingly sophisticated machine learning models at scale, the complexity of efficiently serving these models while balancing performance requirements across multiple tenants has become a paramount concern. It examines the fundamental tension between inference latency and model accuracy that defines this domain, analyzing various dimensions of this tradeoff, including model compression techniques, dynamic resource allocation strategies, and batching optimizations. The article presents a comprehensive overview of architectural considerations for distributed inference, covering microservices-based infrastructure, containerization approaches, and specialized hardware integration. It discusses essential performance measurement frameworks, including key performance indicators and monitoring systems necessary for operational excellence. Finally, the article explores implementation strategies that organizations can adopt to optimize their multi-tenant inference systems, from automated model optimization pipelines to sophisticated resource management policies and hybrid deployment approaches. Throughout the article, it draws on research findings and industry experiences to provide practical insights into building scalable, efficient, and reliable inference infrastructures capable of meeting diverse business requirements
Keywords: Resource Allocation, distributed model serving, latency-accuracy tradeoff, model compression, multi-tenant inference
