Serving LLMs#

Ray Serve LLM provides a high-performance, scalable framework for deploying Large Language Models (LLMs) in production. It specializes Ray Serve primitives for distributed LLM serving workloads, offering enterprise-grade features with OpenAI API compatibility.

Why Ray Serve LLM?#

Ray Serve LLM excels at highly distributed multi-node inference workloads:

  • Advanced parallelism strategies: Seamlessly combine pipeline parallelism, tensor parallelism, expert parallelism, and data parallel attention for models of any size.

  • Prefill-decode disaggregation: Separates and optimizes prefill and decode phases independently for better resource utilization and cost efficiency.

  • Custom request routing: Implements prefix-aware, session-aware, or custom routing logic to maximize cache hits and reduce latency.

  • Multi-node deployments: Serves massive models that span multiple nodes with automatic placement and coordination.

  • Production-ready: Has built-in autoscaling, monitoring, fault tolerance, and observability.

Features#

  • ⚡️ Automatic scaling and load balancing

  • 🌐 Unified multi-node multi-model deployment

  • 🔌 OpenAI-compatible API

  • 🔄 Multi-LoRA support with shared base models

  • 🚀 Engine-agnostic architecture (vLLM, SGLang, etc.)

  • 📊 Built-in metrics and Grafana dashboards

  • 🎯 Advanced serving patterns (PD disaggregation, data parallel attention)

Requirements#

pip install ray[serve,llm]

Next steps#