Load Balancing and Scaling LLM Serving

Mohammad Ashar Khan explores the unique challenges and strategies involved in load balancing and scaling for Large Language Model (LLM) serving, emphasizing the importance of prompt caching in reducing input token costs and latency. Unlike traditional services, LLMs require specialized routing techniques to maintain cache efficiency as the fleet of replicas grows. The article discusses various load balancing strategies, including cache-aware and precise prefix cache-aware routing, which leverage data structures like Radix trees for rapid prefix matching. It highlights the role of inference engines such as vLLM, SGLang, and TensorRT in managing LLM workloads and improving GPU resource utilization. Khan also addresses the complexities of disaggregated serving, where the efficiency of prefill and decode stages depends on the arithmetic intensity of the hardware, and the need for high-speed KV cache transfer technologies. The future of LLM serving may involve a shared cache layer across replicas to optimize performance, although current practices focus on session affinity and prefix-aware routing due to network latency challenges.