Major upgrades to Ray Serve: Online Inference with 88% lower latency and 11.1x higher throughput

Ray Serve has announced significant upgrades aimed at enhancing its performance for scalable AI applications, particularly in reducing latency by 88% and increasing throughput by 11.1x. These improvements, achieved through collaboration with Google Kubernetes Engine, involve integrating HAProxy, a robust open-source load balancer, and enabling direct gRPC communication between Ray Serve replicas. The enhancements are demonstrated in use cases like recommendation systems and LLM inference, showcasing substantial gains in throughput and latency when these optimizations are applied. With the new features available in Ray 2.55+, Ray Serve aims to become the standard framework for developing AI applications at production scale, supporting seamless scaling of high-throughput, low-latency inference workloads.