Parallel data infrastructure for AI & HPC
VDURA Data Platform
Flash performance. Parallel scale. Durable data.
VDURA combines a true parallel file system with resilient object storage in a software-defined architecture designed for AI factories, HPC clusters, research environments, and other data-intensive computing workloads.
Applied Data Systems designs VDURA into the complete platform—compute, network, rack, storage, and software—then integrates and validates the solution before deployment.
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Why VDURA
Performance, Economics, and Simplicity in One Data Platform
VDURA is designed to keep accelerated compute fed with data while avoiding a separate high-performance tier, archive silo, and management stack for every stage of the workflow.
2.7 TB/s
Up to Per-Rack Throughput
VDURA positions its flash-first parallel architecture for demanding AI and HPC data paths where storage performance directly affects GPU and CPU utilization.
12 Nines
Up to Data Durability
Resilient object techniques, erasure coding, and self-healing data protection are designed to protect large-scale data while maintaining operational efficiency.
6 → 1500+
Storage-Node Scale
A software-defined, shared-nothing architecture allows environments to grow from smaller deployments to very large AI and HPC infrastructures.
Architecture
Separate Control and Data Planes
VDURA separates metadata coordination from the user data path. Director Nodes handle control-plane functions while Storage Nodes serve data directly to compute clients.
DirectFlow gives compute nodes parallel access to the Storage Nodes holding the data, avoiding the head-node bottlenecks common in traditional network file systems.
The result is a single global namespace spanning high-performance flash and cost-efficient capacity configurations, with policy-driven movement of data across tiers.
Director Nodes
Coordinate metadata, cluster state, recovery, and orchestration across the platform.
VeLO™ Metadata
Flash-optimized distributed metadata engine designed for high namespace and small-file operation rates.
Storage Nodes
Serve user data using all-NVMe or flash-plus-capacity configurations.
DirectFlow Client
Provides direct, parallel, POSIX-compliant access from compute clients to storage.
Platform Capabilities
Built for Modern AI and HPC Data Paths
The platform combines high-speed access with data protection and operational controls needed for shared, production-scale infrastructure.
High-Speed Data Access
RDMA-capable parallel I/O is designed to move data directly between compute and storage with low overhead and high aggregate throughput.
Parallel File System
DirectFlow presents POSIX-compliant file access across a shared namespace for AI, analytics, simulation, and research applications.
Object Access
S3-compatible access extends the platform to cloud-native workflows, bulk data, and object-oriented application patterns.
Context-Aware Tiering
Policy automation can move data across NVMe, SSD, and object-oriented capacity tiers according to access behavior.
Data Protection
Erasure coding, self-healing workflows, and AES-256 encryption are designed to protect data at rest and across tenant data paths.
Operational Simplicity
Centralized management, observability, non-disruptive updates, and automation are designed to reduce day-two operational burden.
AI Data Pipeline
Keep Data Moving From Ingest Through Inference
A shared parallel data layer helps reduce handoffs between storage silos as data moves through the AI lifecycle.
01 / TRAINING
Feed GPU Clusters
Stream large training datasets from high-performance storage while supporting checkpointing and concurrent access from many accelerator nodes.
02 / INFERENCE
Low-Latency Model Access
Support inference pipelines that need fast access to models, embeddings, features, and active application data.
03 / PERSISTENT CONTEXT
Extend Beyond GPU Memory
Provide durable shared storage for session context and pipeline state that must persist beyond local accelerator memory.
Deployment Flexibility
Software-Defined and Hardware Flexible
VDURA is designed as a shared-nothing software platform rather than a closed storage appliance. That gives ADS room to balance server platform, media, networking, rack density, power, and capacity around the workload.
ADS integrates storage as part of the complete AI or HPC system—not as an isolated appliance.
Configurations can emphasize all-flash performance, hybrid capacity economics, or a mix of the two while maintaining a common namespace and management model.
Architecture
Software-defined, shared-nothing, separate control and data planes
File Access
DirectFlow parallel, POSIX-compliant client
Object Access
S3-compatible interface
Media Options
NVMe-first flash or flash plus capacity-oriented storage nodes
Networking
High-speed Ethernet or InfiniBand with RDMA support
Protection
Erasure coding, self-healing recovery, encryption, and resilient object techniques
Scale
Designed for large client counts and scale-out growth without architectural redesign
The ADS Difference
VDURA, Integrated as a Complete System
Applied Data Systems brings the platform together with the compute and fabric it needs to perform in production.
✓
Workload-Driven Design
ADS sizes the architecture around GPU/CPU client count, throughput, metadata behavior, capacity, growth, and lifecycle requirements.
✓
Compute + Fabric Integration
Storage is validated with the server, GPU, Ethernet or InfiniBand fabric, rack, power, and software stack it will run with.
✓
Burn-In and Validation
ADS performs integration, configuration, functional checks, and system burn-in before the solution is delivered.
✓
Lifecycle Support
Customers have a single integration partner to help coordinate issues across storage, compute, networking, and the broader infrastructure stack.
Build the Data Layer
Design a VDURA Platform Around Your Workload
Bring ADS your GPU or CPU architecture, data profile, throughput targets, capacity requirements, network preferences, and growth curve. We’ll help turn them into a validated VDURA solution.
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