Unified Storage
Deliver file, block and object services on a shared architecture — combining high-performance access, scale-out metadata and a highly available control plane for AI, databases, archives and shared files.
Discuss architectureOne foundation for block, file and object
Unlike fully separate systems, the platform delivers object, file and block capabilities on the same data plane and a scalable control plane, reducing data silos, duplicated operations and migration cost.
- Unified data-block layer & metadata mapping
- Unified capacity pool & data protection
- Easier cross-service data flow
Scale-out metadata for massive small files
Metadata can be partitioned and load-balanced by directory, bucket, namespace or volume, scaling throughput and capacity by adding nodes and avoiding single-point bottlenecks.
- Great for massive small files & hot directories
- Multi-tenant namespace isolation
- Metadata layer scales independently
A Raft control plane for high availability
The management layer uses Raft elections for consistency and leader failover, keeping cluster-state governance stable during node failures.
- Automatic control-plane failover
- Consistent critical configuration
- Supports audit & unified ops
Unified storage for AI and mixed workloads
Host training datasets, model files, inference caches, database volumes and archives on one foundation, reducing storage fragmentation between AI pipelines and business systems.
- Training datasets & model files
- Database & VM persistent volumes
- Tiering from performance to archive
Storage products
On one foundation, choose file, block, object and database services by scenario.
Powering the Most Demanding Data Workflows
One unified storage platform that keeps feeding AI, analytics and high-performance computing at speed.
AI Inferencing
Unifies unstructured data, prewarms datasets, and feeds GPUs at wire speed for low-latency inference.
AI Training
Combines Tier 0 and parallel file system to keep GPUs saturated, accelerating training throughput and efficiency.
Cloud Computing
Unifies data across sites and clouds, enforcing policies and minimizing egress while accelerating cloud workloads.
Data Analytics
Creates one global namespace and orchestrates datasets to compute, speeding queries, pipelines, and interactive analytics.
Machine Learning
Unifies training and inference data, automates placement, and maximizes GPU utilization for faster model iteration.
GPU Acceleration
Feeds GPUs from Tier 0 shared NVMe, eliminating bottlenecks and boosting token throughput and TTFT.
High-performance Computing (HPC)
Delivers parallel file performance across sites, keeping compute saturated while simplifying data movement and access.
Retrieval Technologies (RAG)
Unifies files and objects, accelerating retrieval augmented generation while ensuring governance.
Build a unified storage foundation for AI and business
One distributed storage carrying block, file and object together — fewer data silos and less duplicated build-out, so data stays governable, analyzable and evolvable.
Discuss architecture