AI Storage Infrastructure

Built for Throughput.
Designed for Scale.

AI workloads are only as fast as the data pipeline feeding them. From high-speed NVMe tiers to distributed parallel file systems, DiGiCOR designs AI storage architectures that eliminate bottlenecks and sustain performance at scale.

High-Speed NVMe

Parallel File Systems

GPU Optimised

Why AI Storage Is Different

Traditional Enterprise Storage

Optimised for:

Transactional workloads
Virtual machines
Structured databases

AI Storage

Must handle:

Massive datasets
High-throughput sequential reads
Parallel GPU access
Distributed training environments
Continuous model iteration

NVMe & Tiering

Performance Where It Matters Most

AI training and inference require extremely high I/O performance. We design tiered storage architectures combining:

NVMe Performance Tier

✓ Ultra-low latency
✓ High parallel throughput
✓ Ideal for active training datasets
✓ Supports multi-GPU environments

Capacity / Object Storage Tier

✓ Cost-efficient scalability
✓ Suitable for raw datasets
✓ Archive and historical model storage
✓ Long-term data retention

Intelligent Tiering Strategy

We help organisations determine:

1

Which data lives on high-performance NVMe
2

What should move to cost-efficient capacity tiers
3

How to optimise data movement between tiers

The result: performance without excessive cost.

Parallel File Systems

Feeding GPUs at Scale

Ideal for:

Large-scale model training
HPC environments
Research institutions
Enterprise AI clusters

Parallel storage ensures your GPUs are fully utilised — not waiting on I/O.

In multi-node AI clusters, multiple GPUs access data simultaneously. Traditional NAS systems can become bottlenecks.

We design parallel file system architectures that:

Distribute data across multiple nodes
Enable concurrent high-bandwidth access
Support distributed AI training
Scale linearly as compute nodes increase

Data Protection

Protecting High-Value AI Assets

AI environments generate:

Proprietary datasets
Expensive trained models
Iterative checkpoints
Experimental outputs

Storage architecture must include:

Snapshot strategies
Backup integration
Replication across sites
Disaster recovery planning

We design protection strategies aligned to:

Business continuity requirements

Compliance obligations

Recovery time objectives

Data retention policies

High performance should never compromise resilience.

Storage Architecture Considerations

Evaluating Your AI Storage Needs

Effective AI storage design requires evaluating:

Dataset size and growth rate

Plan for current and future data volumes

Training concurrency

Simultaneous model training environments

Model checkpoint frequency

I/O patterns during training iterations

GPU count and expansion plans

Current and projected compute resources

Network bandwidth

Interconnect capabilities between nodes

On-premises vs hybrid

Deployment strategy and infrastructure mix

We design storage systems that scale predictably — without disruptive redesign.

Integration with AI Infrastructure

Seamless Ecosystem Integration

AI storage must integrate seamlessly with:

GPU systems

Native acceleration support

High-speed networking

InfiniBand and low-latency connectivity

Container orchestration

Kubernetes and microservices

MLOps pipelines

Workflow automation and versioning

Our approach ensures storage is engineered as part of the AI ecosystem — not bolted on later.

Storage Options for AI Workloads

Match your AI workloads with the right storage technology, protocol, and HA model across leading enterprise platforms

HPE Alletra MP

Performance NVMe Block

Best for: LLM training, fine-tuning
Protocols: NVMe-oF, S3 object
Scale: 1–4 nodes (switchless)

→ Use block for hot training; object for data lakes

HPE Alletra 6000

All-NVMe Enterprise

Best for: Inference, embeddings
HA: Dual-controller, 6-nines
Protocols: FC, iSCSI, NVMe-oF

→ Great "always-on" tier for model-serving

TrueNAS Enterprise

Balanced NVMe/Hybrid

Best for: Vector DBs, inference
Options: NVMe-oF, NFS, S3
Integrity: ZFS snapshots

→ H30: NVMe per bay + 100 GbE

Seagate Exos

Ultra-Dense HDD JBOD

Best for: Data lakes, archives
Density: Multi-PB per enclosure
Interface: SAS-4 JBOD

→ Pair with NVMe for tiered architecture

Hitachi VSP One

Premium Multi-Controller

Best for: Mission-critical AI
Scale: Up to 12 controllers
Resilience: 8-9s availability

→ Enterprise-grade NVMe + compression

QNAP Enterprise

Cost-Effective ZFS

Best for: Edge inference, cache
Tiers: NVMe, SAS, S3-compatible
Value: Great TCO for branch

→ Front-end for larger data lakes

Quick Comparison Matrix

Platform	Primary Role	Media Type	HA Model
HPE Alletra MP	Training performance tier	All-NVMe	Switchless 1-4 nodes
HPE Alletra 6000	Critical AI tier	All-NVMe	Dual-controller HA
TrueNAS Enterprise	Balanced NVMe + HDD	NVMe/SAS/SATA	Dual-controller + Snapshots
Seagate Exos	Data lake / archive	SAS HDD	High-density JBOD
Hitachi VSP One	Mission-critical AI	NVMe (all-flash)	Multi-controller (up to 12)
QNAP Enterprise	Edge & cache tier	NVMe/SAS/S3	Dual-controller (ES)

Need Help Choosing?

Our team will map your models, data flows, GPU fabric, and HA/RPO targets to a right-sized architecture — including BOM, performance estimates, and migration plan.

Typical engagement: 45–60 min discovery → storage/HA design → BOM & proposal

Schedule Consultation