Juicedata

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

We'll compare JuiceFS with major hyperscaler file storage and high-performance file storage offerings across AWS, GCP, Azure, and OCI and also discuss common third-party approaches such as Alluxio and CephFS. The focus won't be on product marketing or deep feature lists, but on practical trade-offs around capabilities, total cost of ownership, operational complexity, reliability, and multi-cloud support. We'll also include a few lightweight lessons learned from "AI-adjacent" domains—spanning GenAI, agentic workflows, quant, autonomous driving, embodied intelligence, and biotech—to illustrate how different requirements shape storage decisions. Join us for the talk and a live Q&A—bring your workload, scale expectations, and priorities (performance vs. cost vs. operational simplicity).

The Complete JuiceFS Architecture Guide in One Video 📺 Supercharge your object storage: Local-disk performance for 100-billion files.  The "Turbocharger": Multi-tier distributed caching. Data Sharding: High-performance writes via Chunk/Slice/Block logic. The Brain: Why decoupled Metadata is the key to massive scale. One video. Full architecture. Zero fluff. 👇 #JuiceFS #DataArchitecture #CloudStorage #Infrastructure #TechDeepDive

welcome to join our office hour this afternoon

I’m sharing more of what we’re learning about AI storage as workloads scale—through the lens of JuiceFS and real deployments. If that’s relevant to you, please follow Juicedata for ongoing updates. 😊

🤔Think #FUSE is slow for #HighPerformanceStorage? Think again. 👇Deep dive into its architecture and see how #JuiceFS builds upon it for scalable, high-performance #FileSystems. #FilesystemInUserspace #AIStorage #DistributedFileSystem #DistributedStorage #DataStorage

🚀 Join #JuiceFS Office Hours 7: Troubleshooting & Performance Optimization with JuiceFS CSI Driver Is your Kubernetes environment facing issues with persistent storage? JuiceFS CSI Driver is here to help. In this upcoming event, we’ll walk you through troubleshooting techniques and performance optimization strategies using the JuiceFS CSI Driver! Session Highlights: - Overview of common issues like storage mount failures, performance problems, and their solutions. - How to check logs and use debugging methods effectively. - Using monitoring tools to assess and improve storage performance. 📅 Feb 27, 4:00-4:45 PM PST 🎟 Register now 👉 https://luma.com/yirpycg2

JuiceFS Enterprise 5.3 just raised the bar: 500B+ files in a single file system + first-ever RDMA support to push distributed cache bandwidth higher while slashing CPU overhead—built for the AI scale we’re all heading toward.

1.45 TB/s throughput with just two cache nodes? Yes, it’s possible. 🚀 Stop letting your bare-metal NVMe sit idle—use JuiceFS to aggregate local storage into a high-performance cache group. Scale your AI workloads with S3-level costs and local-disk speeds. ⚡️

Turn your idle NVMe + RAM into a shared cache pool and get elastic, GPU-feeding throughput, this case study shows JuiceFS building a distributed cache layer that hit up to 70 GB/s without buying a bigger parallel filesystem.