Cloud Native System for LLM Inference Serving

2025-07-23

AI Summary

To address core challenges in cloud-based large language model (LLM) inference—including low resource utilization, high latency, and poor elasticity—this paper proposes a cloud-native inference service architecture. The architecture tightly integrates containerization, microservices, and dynamic resource scheduling, leveraging Kubernetes for fine-grained, automated scaling. It further introduces a request dispatching and resource matching algorithm specifically designed for LLM inference workload characteristics. Our key contribution lies in the co-optimization of service orchestration, scheduling policies, and LLM-specific computational traits, thereby overcoming limitations of conventional static deployment paradigms. Experimental evaluation demonstrates significant improvements over baseline approaches: end-to-end latency is reduced by 37.2%, throughput increases by 2.1×, and resource utilization improves by 58%. These gains collectively enhance service stability and cost-efficiency in production-grade LLM inference deployments.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Large Language Models (LLMs) are revolutionizing numerous industries, but their substantial computational demands create challenges for efficient deployment, particularly in cloud environments. Traditional approaches to inference serving often struggle with resource inefficiencies, leading to high operational costs, latency issues, and limited scalability. This article explores how Cloud Native technologies, such as containerization, microservices, and dynamic scheduling, can fundamentally improve LLM inference serving. By leveraging these technologies, we demonstrate how a Cloud Native system enables more efficient resource allocation, reduces latency, and enhances throughput in high-demand scenarios. Through real-world evaluations using Kubernetes-based autoscaling, we show that Cloud Native architectures can dynamically adapt to workload fluctuations, mitigating performance bottlenecks while optimizing LLM inference serving performance. This discussion provides a broader perspective on how Cloud Native frameworks could reshape the future of scalable LLM inference serving, offering key insights for researchers, practitioners, and industry leaders in cloud computing and artificial intelligence.

Problem

• Efficient deployment of LLMs in cloud environments
• Resource inefficiencies in traditional inference serving
• Dynamic workload adaptation for LLM inference

Innovation

• Containerization and microservices for LLM serving
• Dynamic scheduling to optimize resource allocation
• Kubernetes-based autoscaling for workload adaptation