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Bringing a large language model from its initial training to deployment requires numerous systems and components. At Zhihu, we grappled with a multi-cloud, cross-region AI platform, requiring an efficient solution to facilitate the rapid training and delivery of models for production use cases. This led us to adopt Alluxio, the high-performance data access layer for LLM. This blog provides an in-depth look at Zhihu’s challenges, journey, and solution for LLM training and deployment. Through adopting Alluxio, we’ve significantly enhanced model training performance by 2 to 3 times and can deploy updated models every minute instead of hours or days. Also, our GPU utilization has doubled, infrastructure and operation costs have been halved, and we have established a resilient, efficient infrastructure capable of meeting our escalating AI demands.
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This is a blog series talking about the design and implementation of the Cross Cluster Synchronization mechanism in Alluxio. This mechanism ensures that the metadata is consistent when running multiple Alluxio clusters. Part 1 of this blog series discusses the scenario and background.
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This is part 2 of the blog series talking about the design and implementation of the Cross Cluster Synchronization mechanism in Alluxio. In the previous blog, we discussed the scenario, background and how metadata sync is done with a single Alluxio cluster. This blog will describe how metadata sync is built upon to provide metadata consistency in a multi-cluster scenario.
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