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LLM Storage Selection & Detailed Performance Analysis of JuiceFS

LLM Storage Selection & Detailed Performance Analysis of JuiceFS
2024-10-23
Shaojie Li
Explore storage solutions for large language models, comparing JuiceFS performance with CephFS, Lustre, s3fs, Alluxio, and Cloud NAS.
Hugging Face + JuiceFS: Simplifying Model Sharing Across Multiple Users and Nodes

Hugging Face + JuiceFS: Simplifying Model Sharing Across Multiple Users and Nodes
2024-10-17
Herald Yu
Learn how to simplify model sharing across multiple users and nodes by using JuiceFS as the cache directory for Hugging Face Transformers.
Training LLMs: Best Practices for Storing Thousands of Nodes in K8s

Training LLMs: Best Practices for Storing Thousands of Nodes in K8s
2024-10-09
Weiwei Zhu
Learn the best practices for training large language models in Kubernetes clusters with thousands of nodes. Explore how JuiceFS optimizes storage, en…
How Clobotics Overcame Multi-Cloud and Massive File Storage Challenges

How Clobotics Overcame Multi-Cloud and Massive File Storage Challenges
2024-09-11
Jonnas
Clobotics, a global leader in computer vision technology, enhanced its storage infrastructure with JuiceFS, overcoming multi-cloud challenges and man…
MiniMax Built a Cost-Effective, High-Performance AI Platform with JuiceFS

MiniMax Built a Cost-Effective, High-Performance AI Platform with JuiceFS
2024-09-02
Juicedata Team
Learn how MiniMax used JuiceFS Enterprise Edition to build a high-performance, cost-effective AI platform for foundation models.
How JuiceFS Boosts Foundation Model Inference in Multi-Cloud Architectures

How JuiceFS Boosts Foundation Model Inference in Multi-Cloud Architectures
2024-08-29
Changjian Gao
Learn how JuiceFS Enterprise Edition enhances foundation model inference in multi-cloud setups by accelerating data access, optimizing model data dis…
Enhancing AI Training Workflows with JuiceFS

Enhancing AI Training Workflows with JuiceFS
2024-08-27
Juicedata Team
JuiceFS boosts AI training workflows with scalable storage, high-performance data handling, and seamless POSIX integration.
Metabit Trading Built a Cloud-Based Quantitative Research Platform with JuiceFS

Metabit Trading Built a Cloud-Based Quantitative Research Platform with JuiceFS
2024-08-14
Jianhong Li
Metabit Trading, an AI-based quantitative investment firm, used JuiceFS to build a cloud-based quantitative research platform. Learn how JuiceFS impr…
vivo Migrated from GlusterFS to a Distributed File System for AI Training

vivo Migrated from GlusterFS to a Distributed File System for AI Training
2024-07-18
Yige Peng
Learn why vivo's AI training platform switched from GlusterFS to a cloud-native high-performance file system.