Batch LLM Inference on Anyscale slashes AWS Bedrock costs by up to 6x
Large Language Models (LLMs) have revolutionized the technology industry, with a focus on optimizing inference costs due to high GPU prices. While online inference provides low-latency responses, batch inference for LLMs offers higher throughput and greater cost-effectiveness by optimizing GPU resource utilization. In certain cases, Anyscale can reduce costs by up to 2.9x compared to online inference providers such as AWS Bedrock and OpenAI. RayLLM-Batch is a library leveraging Ray and Anyscale components to optimize LLM batch inference at scale, offering a powerful, cost-effective solution for large-scale batch LLM inference. Experiments show that the Anyscale FP8 batch inference solution can outperform other common solutions on price-performance.
Company
Anyscale
Date published
Oct. 1, 2024
Author(s)
Cody Yu, Scott Lee, Ricky Xu, William Lin, Praveen Gorthy and Richard Liaw
Word count
1180
Hacker News points
None found.
Language
English