LLM-Optimized Cloud Architectures: Evaluating Infrastructure Patterns For Fine-Tuning And Serving Large Models

Abstract

Large Language Models have ignited a paradigm shift in the field of artificial intelligence, but their implementation comes with daunting infrastructure issues that traditional cloud architectures cannot simply address. This article proposes a complete three-layer architecture for special optimization of the entire LLM lifecycle through training, fine-tuning, and inference processes. The suggested design combines distributed GPU orchestration using Kubernetes and Ray, applies parameter-efficient adaptation mechanisms such as Low-Rank Adaptation, and utilizes sophisticated quantization strategies for optimizing inference. The design tackles system bottlenecks in memory, computational, and resource management through rigorous design patterns that facilitate end-to-end scalability across heterogeneous clouds. Experimental verification proves dramatic enhancements to operational performance, as parameter-efficient fine-tuning minimizes computational needs without sacrificing model quality, elastic orchestration improves resource efficiencies through variable workloads, and quantization methods facilitate deployment on hardware with limited resources. The architectural framework offers real-world blueprints for organizations looking to deploy LLM workloads at scale, presenting modular components that translate across various operational requirements at an affordable cost with performance standards ideal for production environments.