Discover how Ray Serve enables cost-efficient Large Language Model (LLM) serving in this 30-minute conference talk by Anyscale. Explore the capabilities of Ray Serve as the most economical and straightforward method for deploying LLMs, having processed billions of tokens in Anyscale Endpoints. Delve into the cost-reduction strategies employed by Ray Serve, including fine-grained autoscaling, continuous batching, and model parallel inference. Gain insights into the efforts made to simplify the deployment of any Hugging Face model with these optimizations. Learn how Ray Serve minimizes costs by utilizing fewer GPUs through fine-grained autoscaling and integrating with libraries like VLLM to maximize GPU utilization. Understand the significance of Ray as the leading open-source framework for scaling and productionizing AI workloads, powering the world's most ambitious AI projects across various domains.
Overview
Syllabus
Enabling Cost-Efficient LLM Serving with Ray Serve
Taught by
Anyscale
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