Parallel file system provider VDURA has upgraded its software with enterprise and AI-focused enhancements, including a multi-tenant control plane, accelerated S3 object storage performance, and native S3 object tagging capabilities.
Its next-gen control plane is tailored for modern AI and HPC operational requirements, featuring a streamlined multi-tenant admin model, a revamped UI, and platform-operation REST API. The updates equip operators and integrators with robust foundations for automation and custom tooling workflows. Optimized for cloud-native AI pipelines including model checkpointing, inference serving and large-scale dataset ingestion, the enhanced S3 storage cuts operation latency and boosts aggregate throughput for concurrent read-write workloads.
“The new interface simplifies daily operation and strengthens control, while upgraded S3 tools empower teams to build advanced, automated data pipelines — addressing core customer demands for better operability and integration,” said Chris Girard, VDURA VP of Product Management.
VDURA’s S3 performance boost stems from its scale-out cloud architecture, explained Eric Salo, SVP of Marketing and Business Operations. The virtualized microservices-based system leverages stateless director nodes as its core brain paired with modular storage nodes. Virtualized S3 server agents run on directors, enabling linear performance scaling: users can double S3 capacity and throughput simply by adding more cost-effective directors, delivering nearly unlimited scalability.
Salo confirmed the platform utilizes client-to-storage-node RDMA (not S3-over-RDMA), enabling each storage node to achieve 60–90 GB/s transfer speeds. Built on fully parallel PanFS software, the system deploys sufficient S3 agent directors to deliver TB/s-level bandwidth for high-demand workloads. The solution was co-developed with several unnamed AI-focused neocloud partners, helping VDURA rapidly iterate to meet real-world cloud AI infrastructure needs.
The new native S3 object tagging function allows users to attach rich metadata to stored objects. It supports policy-driven data lifecycle management, automated tiering, and fine-grained access control for AI training data, model artifacts and scientific datasets, delivering essential metadata infrastructure for governance and full-lifecycle management of petabyte-scale object storage in HPC and AI environments.
The multi-tenant control plane, S3 performance upgrades and object tagging features will be generally available for all V5000-class systems in H2 2025. Existing users can adopt the updates via seamless online in-place upgrades.
Beijing Qianxing Jietong Technology Co., Ltd.
Sandy Yang/Global Strategy Director
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Email: yangyd@qianxingdata.com
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