The architecture for serving large language model (LLM) inference has undergone a rapid transformation. Early approaches relied on shared API gateways that multiplexed requests across a pool of GPU instances, offering simplicity but suffering from noisy-neighbor effects and unpredictable latency. As demand grew, teams moved to dedicated clusters with elastic scheduling, enabling better resource isolation and cost efficiency. This evolution involves trade-offs: shared APIs are easier to manage but offer less control, while dedicated clusters require more sophisticated orchestration but deliver consistent performance. Key design patterns include request batching, speculative decoding, and dynamic GPU allocation. The article also discusses hybrid approaches that combine shared and dedicated resources to balance cost and performance. For infrastructure engineers, understanding this evolution is crucial for designing scalable and cost-effective AI serving systems. The principles apply whether using cloud services like AWS SageMaker, GCP Vertex AI, or on-premise Kubernetes clusters. As LLMs become more prevalent, the ability to serve inference efficiently will be a competitive differentiator.
This article traces the evolution of large language model inference serving architectures, from simple shared API endpoints to sophisticated dedicated clusters with elastic scheduling. It covers key design decisions around resource allocation, latency optimization, and cost management, providing a valuable reference for engineers building or scaling AI infrastructure.