Blog
Alluxio’s MLPerf Storage v3.0 results demonstrate that organizations can keep persistent AI data in Amazon S3 while delivering high-performance training and checkpointing through a distributed cache close to compute. Alluxio achieved 147.03 GiB/s checkpoint write bandwidth for Llama 3 405B and near-linear scaling from one to 32 workers.
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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.