High Speed File Storage

High-performance File Storage powered by smarter AI

High Speed File Storage
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Overview

High Speed File Storage is a performance-tuned, POSIX-compliant shared file system designed for the AI training and inference extreme use cases. Using industry-standard NFS, it provides ultra-low latency and massive parallel throughput to keep GPU clusters continuously fed with the correct data—optimising training cycles, eliminating I/O bottlenecks, and unleashing maximum AI performance.

Built for big data and high-workload applications, it gives rapid, secure access to data in GPU clusters, VMs, and containers. Scalable, resilient, and secure by design, it fits seamlessly into AI workflows with strict access controls and robust protection of data.

Optimise GPU use, speed up AI training, and get insights sooner with storage optimised for speed and scale.

Pricing

To know more about the SKUs and pricing click below.

Core Features at a Glance 

High-Throughput NFS Access
Experience lightning-fast data delivery over the industry-standard NFS protocol, finely tuned for AI training and inference workloads.
Low-Latency Data Delivery
Stream high-bandwidth, low-latency data to large GPU clusters to maximise utilisation and dramatically shorten training times.
Scalable Parallel Access
Handle simultaneous read/write operations from multiple GPU servers and AI pipelines—without compromising performance.
Policy-Based Snapshots
Capture instant, space-efficient snapshots to protect datasets and rapidly restore AI training checkpoints or inference data.
Fine-Grained Access Control
Protect sensitive data with POSIX-compliant user and group permission controls.
Role-Based Access Management (RBAC)
Simplify administration with role-specific privileges for monitoring, configuration, and access provisioning.
Data-at-Rest Encryption
Safeguard datasets with built-in encryption, ensuring confidentiality and compliance
Multi-Protocol Data Access with S3
Work with the same dataset via both high-speed file and S3 object protocols, enabling seamless ingestion from S3-based sources and delivery to S3-compatible AI tools, analytics platforms, and archival systems—without redundant copies.

What You Get

Maximise GPU ROI
Eliminate idle cycles with high-bandwidth, low-latency file access that keeps GPUs fully engaged, accelerating both training and inference timelines.
Handle AI-Scale Data with Ease
Manage massive datasets effortlessly with scalable, parallel access that delivers consistent performance—even under the heaviest multi-node workloads.
Reduce Operational Complexity
Streamline storage configuration, access control, and performance monitoring through a single, centralized management interface.
Scale Without Disruption
Grow capacity and performance instantly to match evolving AI demands—without downtime or service interruptions.
Safeguard Critical AI Assets
Protect datasets and model outputs with space-efficient snapshots, enabling fast recovery and checkpoint restoration.
Depend on Predictable Performance
Maintain steady throughput and ultra-low latency for the most demanding AI workflows, from pre-processing to real-time inference.

Still have questions?

High-Speed File Storage is a performance-optimised, POSIX-compliant shared file system built to meet the extreme data demands of AI training and inference. Delivered over industry-standard NFS with optional S3 access, it enables ultra-fast, low-latency access to large datasets—ensuring your GPU clusters run at peak efficiency.
The service provides high-throughput file shares to GPU servers, VMs, and containerised workloads. Backed by a scale-out, parallel architecture, it supports concurrent read/write operations from multiple nodes without performance degradation, feeding your AI pipelines with data exactly when it’s needed.
Unlike conventional storage systems, the High-Speed File Storage is engineered for massive parallelism, sustained throughput, and ultra-low latency—key requirements for distributed AI workloads. It combines scalable NFS access, optional S3 compatibility, and integrated data protection in a single platform.
Benefits include maximised GPU utilisation, reduced training times, seamless scaling for large datasets, centralised management, and built-in security and data protection through encryption, access controls, and snapshots.
Typical use cases include storing and streaming large AI training datasets, pre-processed data for pipelines, model checkpoints, inference input/output data, and shared datasets for multi-user AI environments.
It works seamlessly with GPU servers, Kubernetes clusters, and HPC environments. Standard NFS protocol support ensures compatibility with most AI frameworks (such as PyTorch and TensorFlow) without code changes.
Security is built in from the ground up—featuring POSIX permissions, Role-Based Access Control (RBAC), data-at-rest encryption, and secure multi-protocol access (file and object).
Yes. The platform is designed for seamless scaling, allowing you to expand both capacity and performance without downtime or disruption to ongoing AI workloads.
Performance varies by configuration, but the system can deliver sustained multi-GB/s throughput per client and handling hundreds of concurrent connections—ideal for distributed AI training at scale.

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