Open SourceInference37k
Ray Serve / Ray
Distributed framework for scaling model serving and AI workloads.
Ray is an open-source framework for scaling Python and AI applications, and its Ray Serve library provides a scalable, framework-agnostic model serving layer. Ray Serve supports composing multiple models into deployment graphs, autoscaling, fractional GPU allocation, and batching, and includes LLM-specific serving with an OpenAI-compatible API backed by engines like vLLM. It is widely used to serve and orchestrate inference at scale across clusters.
Repository
ray-project/rayLanguage
PythonWhat you'd build with it
- Serving and autoscaling LLMs across a cluster with an OpenAI-compatible API backed by vLLM
- Composing multi-model inference pipelines (retrieval, ranking, generation) as a single deployment graph
- Running batch inference and online serving on the same Ray cluster with fractional GPU allocation
Tags
servingdistributedscalinggpu