How JuiceFS Optimizes AI Storage: 90% Cost Savings and 70GB/s Throughput | JuiceFS Office Hours #9

​As large model training scales up and GPU power increases, data access bottlenecks increasingly impact system performance. Local storage provides high performance but lacks scalability, while object storage offers cost efficiency and scalability but struggles with throughput under high concurrency.

​Distributed file systems like JuiceFS strike a balance between high performance and scalability. With its distributed architecture, JuiceFS has been widely adopted in AI scenarios. 

​For example, in BioMap’s case, transitioning from SSDs to JuiceFS combined with object storage resulted in a 90% cost reduction in storage. Meanwhile, in another AI application, JuiceFS has achieved up to 70GB/s throughput.

​In this office hours session, we’ll cover how JuiceFS addresses AI needs from three perspectives:

• ​Leveraging object storage for cost efficiency

• ​Accelerating data access through data chunking and metadata separation

• ​Optimizing performance with multi-level caching

​17:00 – 17:25 | How JuiceFS Optimizes AI Storage: 90% Cost Savings and 70GB/s Throughput

​​Speaker: Min Cai, Juicedata Architect

​17:25 – 17:45 | Live Q&A – Ask the JuiceFS Team Anything

JuiceFS  Office Hours

​​​​​​​​The JuiceFS Office Hours is a recurring online event held on the fourth Thursday of each month. It serves as a dedicated platform for JuiceFS users and community members to:

• ​​​​​​​​​Share our practices and community experiences in the AI field

• ​​​​​​​​​Receive immediate responses to their questions from the JuiceFS team

• ​​​​​​​​​Learn about the latest product updates and feature releases

​​​​​​​​About JuiceFS
JuiceFS is an open-source, high-performance distributed file system for the cloud. With full POSIX compatibility, it turns object storage into massive local disks across platforms and regions, and is widely used in AI applications like autonomous driving, generative AI, quant research, and biotech.