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Enabling Cost-Efficient LLM Serving with Ray Serve
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Ray Serve is the cheapest and easiest way to deploy LLMs, and has served billions of tokens in Anyscale Endpoints. This talk discusses how Ray Serve reduces cost via fine-grained autoscaling, continuous batching, and model parallel inference, as well as the work we've done to make it easy to deploy any Hugging Face model with these optimizations.
Takeaways:
• Learn how Ray Serve saves costs by using fewer GPUs with finegrained autoscaling and integrating with libraries like VLLM to maximize GPU utilization.
About Anyscale
---
Anyscale is the AI Application Platform for developing, running, and scaling AI.
If you're interested in a managed Ray service, check out:
About Ray
---
Ray is the most popular open source framework for scaling and productionizing AI workloads. From Generative AI and LLMs to computer vision, Ray powers the world’s most ambitious AI workloads.
#llm #machinelearning #ray #deeplearning #distributedsystems #python #genai
Takeaways:
• Learn how Ray Serve saves costs by using fewer GPUs with finegrained autoscaling and integrating with libraries like VLLM to maximize GPU utilization.
About Anyscale
---
Anyscale is the AI Application Platform for developing, running, and scaling AI.
If you're interested in a managed Ray service, check out:
About Ray
---
Ray is the most popular open source framework for scaling and productionizing AI workloads. From Generative AI and LLMs to computer vision, Ray powers the world’s most ambitious AI workloads.
#llm #machinelearning #ray #deeplearning #distributedsystems #python #genai
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