High Performance Distributed Inference with Ray Serve LLM

Ray Serve LLM, in collaboration with the Google Kubernetes Engine (GKE) team, has achieved a significant enhancement in throughput and latency by implementing architectural upgrades such as direct streaming, a new vLLM Ray executor backend, and HAProxy integration. These optimizations have enabled up to 4.4x higher request throughput on prefill-heavy workloads and up to 24x on decode-heavy workloads. The enhancements allow Ray Serve LLM to match the performance of the rust-based vllm-router framework, showcasing improvements in orchestration overhead and efficiency. By leveraging Ray's primitives for fault tolerance and observability, the updates facilitate complex distributed computing pipelines across Kubernetes and VMs. The new direct streaming mode decouples the request routing control plane from the request/response streaming data plane, resolving bottlenecks in the legacy architecture and optimizing performance in multi-replica and agentic workloads. These developments position Ray Serve LLM as a robust solution for high-performance distributed inference, maintaining parity with existing frameworks while preserving Ray's flexibility and scalability capabilities.

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