OneSource Cloud
May 11, 2026
17 min read
On-premises AI infrastructure vs colocation: which is right for you?
Compare on-premises AI infrastructure vs colocation. Cost, control, time-to-deploy, and a decision framework for enterprise AI teams.
Design for Training, Inference, Preprocessing & Multi-tenant AI
Private AI storage
Why AI Needs Smarter Storage？
Design, Deploy and Operate your Private AI Infrastructure
AI Storage
AI workloads aren’t just larger IT workloads — they have fundamentally different I/O patterns, and storage performance directly impacts GPU utilization, the most expensive resource in your stack.
Artificial Intelligence workloads fundamentally differ from traditional enterprise applications in how they access and process data.
Conventional NAS and SAN systems were designed for transactional workloads and moderate concurrency — not the scale, parallelism, and throughput demands of AI training and inference.
AI training reverses those assumptions: hundreds of GPU workers may access the same dataset simultaneously, generating I/O patterns that range from millions of small random reads to multi-GB sequential streams.
Conventional NAS and SAN systems were designed for transactional workloads and moderate concurrency — not the scale, parallelism, and throughput demands of AI training and inference.
AI training reverses those assumptions: hundreds of GPU workers may access the same dataset simultaneously, generating I/O patterns that range from millions of small random reads to multi-GB sequential streams.
The storage layer directly determines GPU utilization efficiency — the most expensive resource in your stack.
A purpose-built storage tier isn't a nice-to-have. Every millisecond a GPU spends waiting on data is a millisecond of capital sitting idle. That single fact reshapes the entire design brief.
Massive throughput demand
Training jobs require continuous streaming of large datasets to hundreds of GPUs simultaneously, creating sustained bandwidth far exceeding traditional NAS.
High concurrency access
Distributed training frameworks like PyTorch and TensorFlow have many compute nodes hitting the same dataset concurrently, stressing metadata and file-access layers.
GPU sensitivity to I/O latency
GPUs process data at extremely high speeds. Any delay in data delivery results in idle GPU cycles — directly impacting performance and cost efficiency.
Mixed data patterns
AI pipelines handle millions of small files (images, tokens) and large sequential datasets (Parquet, TFRecord) — requiring storage that handles both extremes well.
Checkpointing & burst writes
Technical assistance for OnePlus™ system and developer environments.
Storage aligned to
every workload tier
Optimized for AI workloads
AI Storage
AI storage is not one-size-fits-all. We design four workload-aligned tiers optimized for bandwidth, latency, parallel access, and scalable performance.
High-Performance Tier
Training workloads
Requirements
- Ultra-high throughput (tens to hundreds of GB/s)
- Low-latency access via RDMA / InfiniBand
- Parallel file system support
Recommended design
- NVMe-based distributed storage cluster
- Parallel file system (Spectrum Scale / Lustre)
- Dedicated InfiniBand / high-speed Ethernet fabric
- GPU Direct Storage (GDS) enabled
Balanced Performance Tier
Inference workloads
Requirements
- Low latency for real-time predictions
- Moderate throughput
- High availability
Recommended design
- Hybrid storage (NVMe + SSD tiers)
- Distributed file / object storage
- Caching layer for hot models
- NVMe-based distributed storage cluster
Capacity Tier
Data lake & preprocessing
Requirements
- Large-scale data storage at PB-level
- Cost efficiency
- Flexible access (structured / unstructured)
Recommended design
- Object storage (S3-compatible)
- Tiered storage (HDD + cloud / archive integration)
- Integrated data lifecycle management
MULTI-TENANT / GOVERNANCE TIER
SHARED AI PLATFORM OPERATIONS
Requirements
- Resource isolation
- QoS and workload scheduling alignment
- Secure data segmentation
Recommended design
- Parallel file system with policy-based controls
- Integration with Kubernetes / Slurm schedulers
- Caching layer for hot models
How we design
around the workloads
AI storage design starts from the workload
We Design
AI storage design starts with the workload — translating GPU utilization goals into architecture decisions.
Understand the AI Workload Profile
First, identify whether the environment is mainly for training, fine-tuning, inference, data preprocessing, or mixed AI/HPC workloads. Training requires high throughput and low latency to feed GPUs continuously; inference requires fast access to model files, checkpoints, embeddings, and application data.
Analyze the data pipeline
Map how data moves from ingestion to preprocessing, training, checkpointing, validation, and model deployment. AI storage must support large datasets, frequent small-file access, high-speed sequential reads, and heavy checkpoint writes — without becoming a bottleneck.
Define performance requirements
Calculate required bandwidth, IOPS, latency, and concurrency based on the number of GPU servers, GPU type, batch size, dataset size, and expected number of simultaneous jobs. The goal is to keep expensive GPUs fully utilized rather than waiting on data.
Select the right storage architecture
For serious AI training clusters, a parallel file system such as IBM Spectrum Scale / GPFS, Lustre, BeeGFS, DDN EXAScaler, VAST, or WEKA is often preferred over traditional NAS. These systems are engineered for parallel access, high throughput, metadata performance, and multi-node scalability.
Design for different data types
A strong AI storage design separates tiers: high-performance NVMe flash for active training data and checkpoints, capacity storage for raw datasets and archives, and S3-compatible object storage for long-term data lakes or model repositories.
Integrate with the AI network fabric
A strong AI storage design separates tiers: high-performance NVMe flash for active training data and checkpoints, capacity storage for raw datasets and archives, and S3-compatible object storage for long-term data lakes or model repositories.
Plan for security & governance
A strong AI storage design separates tiers: high-performance NVMe flash for active training data and checkpoints, capacity storage for raw datasets and archives, and S3-compatible object storage for long-term data lakes or model repositories.
Validate before production
Before full deployment, run benchmark tests using realistic AI workloads — not only synthetic storage tests. Validate GPU utilization, data loading speed, checkpoint performance, metadata performance, and failure recovery.
Co-optimized AI infrastructure
GPU, network, and storage must work as one.
GPU+Network+Storage
A turnkey AI storage platform designed, deployed, and managed for secure, scalable, cost-efficient GPU workloads.
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Workload to production-ready AI
AI infrastructure designed for production from Day 1.
Seven-phase Delivery
Seven phases transforming GPU infrastructure into a managed private AI platform.
AI Data Storage
Frequently asked questions
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