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NFS to JuiceFS: Building a Scalable Storage Platform for LLM Training & Inference

NFS to JuiceFS: Building a Scalable Storage Platform for LLM Training & Inference
2025-06-11
Wei Sun
Learn why a leading research institution in China replaced NFS with JuiceFS to overcome storage bottlenecks in their LLM training and inference platf…
JuiceFS at Trip.com: Managing 10 PB of Data for Stable and Cost-Effective LLM Storage

JuiceFS at Trip.com: Managing 10 PB of Data for Stable and Cost-Effective LLM Storage
2025-03-13
Songlin Wu
Trip.com, a global online travel agency, utilizes JuiceFS to handle 10 PB of data for LLM storage. Find out about its stability, cost-effectiveness, …
TAL: Building a Low-Operation Model Repository Based on JuiceFS in a Multi-Cloud Environment

TAL: Building a Low-Operation Model Repository Based on JuiceFS in a Multi-Cloud Environment
2024-11-21
Longhua He
TAL, a leading smart learning solution provider, uses JuiceFS for an efficient, low-maintenance model repository supporting multi-cloud LLM training …
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.
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…
Ollama + JuiceFS: Pull Once, Run Anywhere

Ollama + JuiceFS: Pull Once, Run Anywhere
2024-09-25
Weiwei Zhu
Learn how to combine Ollama and JuiceFS to streamline large language model management.
LLM Storage: Performance, Cost, and Multi-Cloud Architecture

LLM Storage: Performance, Cost, and Multi-Cloud Architecture
2024-04-09
Rui Su
Learn how JuiceFS tackles challenges in large language model storage, balancing performance and cost and solving issues in multi-cloud environments.