High-Performance Cloud Storage for AI & Machine Learning Workloads (2025 Guide)

Table of Contents

• Why AI/ML Workloads Break Traditional Storage
• What to Look for in AI-Optimized Storage
• Top High-Performance Storage Solutions for AI & ML
• Performance & Architecture Comparison
• Best Fit by AI Workflow Stage
• Final Take: Data Gravity Drives Model Gravity

Why AI/ML Workloads Break Traditional Storage

AI workloads aren’t just compute-intensive—they’re data-hungry and I/O-bound. Training a large model like GPT or LLaMA can involve reading petabytes of small files or streaming massive datasets from cloud buckets to GPU clusters.

Key stress points:

• High IOPS and throughput required for parallel model training
• Small file performance critical for image/video datasets (e.g., ImageNet, LAION)
• Low latency needed to avoid GPU underutilization
• High concurrency across nodes and pipelines

Traditional NAS or object storage simply can’t keep up.

What to Look for in AI-Optimized Storage

Top High-Performance Storage Solutions for AI & ML

1) Pure Storage FlashBlade

Best for: Enterprise GPU farms running parallel training or inference at scale

FlashBlade is built for ultra-low latency and parallel file/object workloads, with native support for AI pipelines. It integrates with NVIDIA DGX systems and supports AI data pipelines with high IOPS and linear scale-out.

Key Capabilities:

• NVMe-based scale-out file + object system
• Consistent high throughput under concurrency
• Certified for NVIDIA DGX BasePOD and SuperPOD
• Supports Splunk, Apache Spark, and Kubernetes AI stacks
• Optional AI-ready backup via Pure1

2) NetApp ONTAP AI

Best for: Enterprises standardizing on NetApp + NVIDIA reference architecture

ONTAP AI is a tightly integrated solution with NVIDIA DGX and Mellanox networking. It’s built for customers running mixed AI/analytics workloads with existing NetApp infrastructure.

Key Capabilities:

• Pre-validated NetApp + DGX + Mellanox stack
• Multi-protocol support (NFS, SMB, S3)
• ONTAP SnapMirror for replication
• Works with Kubernetes, TensorFlow, and PyTorch
• Integrated with NetApp DataOps Toolkit for ML pipelines

3) VAST Data Universal Storage

Best for: AI/ML workloads with large unstructured datasets and diverse I/O patterns

VAST’s disaggregated architecture blends performance of NVMe with cost-efficiency of QLC flash, enabling “single tier” performance across hot and cold data.

Key Capabilities:

• Global namespace with all-flash performance
• Parallel client access via RDMA or NFS over TCP
• Scales linearly across exabytes
• Optimized for generative AI training and feature store workloads
• High efficiency with erasure coding and write shaping

4) Google Cloud Filestore High Scale

Best for: GCP-native teams training models on Vertex AI or JAX/TensorFlow

Filestore High Scale is Google’s managed file storage tuned for high-performance compute clusters. It supports up to 1.2 GB/s throughput per instance and up to 1 million IOPS with strong regional durability.

Key Capabilities:

• NFS-based file storage optimized for GKE and Vertex AI
• High concurrency and throughput for AI pipelines
• Integrated with TensorFlow, JAX, Hugging Face Datasets
• Works alongside Google Cloud Storage buckets and BigQuery

5) Lambda Cloud Storage (NVMe-first AI clusters)

Best for: Startups and research labs training models on dedicated GPU clusters

Lambda offers bare-metal GPU clusters with NVMe-attached local storage—ideal for teams training open-source LLMs, vision transformers, or custom architectures.

Key Capabilities:

• Local NVMe scratch optimized for high IOPS
• Configurable file storage over NFS or SMB
• Designed for PyTorch, TensorFlow, JAX workloads
• Available in US and EU zones
• Used by LLM researchers, universities, and startups

Performance & Architecture Comparison

Best Fit by AI Workflow Stage

Final Take: Data Gravity Drives Model Gravity

In AI infrastructure, compute may be the headline—but storage is the enabler. Poor IOPS or slow file access means underutilized GPUs and slower time to model convergence.

Choosing high-performance storage for AI means aligning your architecture with:

• Dataset structure (many small files vs. big blobs)
• Pipeline concurrency
• GPU cluster design
• Hybrid vs. cloud-native deployment

Smart storage architecture won’t just speed up training—it will make your entire ML workflow reproducible, portable, and cost-effective.