Multi-Node Inference Architectures for Low-Latency LLM Serving

Over the past few years, large language models have evolved to enable a wide range of applications-from natural language understanding to real-time conversational agents. However, the deployment of LLMs into production presents many significant challenges, especially with regard to low-latency responses that enable real-time interactions. This work investigates multi-node inference architectures for optimized deployment using open-source frameworks with scalability, flexibility, and cost-effectiveness. We investigate various methods, such as microbatching, tensor and pipeline parallelism, and sophisticated load balancing, that effectively distribute inference workloads across multiple nodes. We conduct extensive evaluations using popular open-source tools such as Kubernetes, Ray, and Envoy to benchmark the performance of these architectures in terms of latency, throughput, and resource utilization under diverse workloads. We also analyze model replication versus model partitioning trade-offs, giving insights into the most appropriate configuration for various deployment scenarios. As our results show, a well-orchestrated multi-node setup can be used to greatly reduce inference latency while preserving high throughputs, enabling the deployment of sophisticated LLMs in latencysensitive applications. This paper gives insights with a detailed analysis of multi-node inference strategies and integration into open-source ecosystems, therefore it will be a great guide for practitioners seeking to develop deployments of LLMs at scale. In summary, this work underlines how distributed architectures can overcome some of the inherent limitations imposed by singlenode deployments and are crucial for achieving more efficient and responsive AI-driven services.