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Ray Serve
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Ray Serve

Creator
Creator
Seonglae ChoSeonglae Cho
Created
Created
2026 Mar 24 14:13
Editor
Editor
Seonglae ChoSeonglae Cho
Edited
Edited
2026 Mar 24 14:13
Refs
Refs
Ray Serve: Scalable and Programmable Serving — Ray 2.54.0
Ray Serve is a scalable model serving library for building online inference APIs. Serve is framework-agnostic, so you can use a single toolkit to serve everything from deep learning models built with frameworks like PyTorch, TensorFlow, and Keras, to Scikit-Learn models, to arbitrary Python business logic. It has several features and performance optimizations for serving Large Language Models such as response streaming, dynamic request batching, multi-node/multi-GPU serving, etc.
Ray Serve: Scalable and Programmable Serving — Ray 2.54.0
https://docs.ray.io/en/latest/serve/index.html
 
 
 
 
 
 
 
 
 

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Ray Serve
Copyright Seonglae Cho