GPU clusters are the most expensive line item in AI infrastructure, and they're only productive when storage can keep up. Training runs stall when throughput can't match GPU demand, checkpointing disrupts training cycles, and fragmented storage forces data staging that slows iteration.
AIStor delivers high-performance, S3-compatible object storage purpose-built for AI training at scale, sustaining the throughput GPUs need while consolidating training data, checkpoints, and model artifacts in a single tier.
Feed thousands of training workers in parallel with massive throughput and sub-millisecond metadata operations, keeping GPUs consistently utilized during large-scale training runs across generative, predictive, and agentic AI workloads.
Checkpoint & Model Store
Consolidate training data, validation sets, checkpoints, and model artifacts in a single S3-compatible object store, eliminating tool sprawl and ensuring every training run is reproducible and governed.
Dataset Management at Scale
Ingest, organize, and serve the large, fast-changing datasets AI training depends on, from raw unstructured data to curated training and testing sets, without data copying or staging pipelines.
Fine-Tuning & Experimentation
Accelerate iteration cycles for model fine-tuning, hyperparameter sweeps, and domain adaptation by providing low-latency, high-throughput access to datasets and artifacts across experiments.
How It Works
AIStor sits behind your training infrastructure as the high-performance data foundation. This feeds GPUs at warp speed, consolidating all AI data in one place, and scaling linearly as datasets and clusters grow.
Multi-Hundreds of GB/s Throughput
Stateless architecture with no centralized metadata server to bottleneck ingest or reads.
Each node operates independently at near line-speed
Sub-millisecond metadata operations enable parallel data access from thousands of workers
GPUs stay consistently utilized during large-scale training runs
One Platform for All AI Data
A single, S3-compatible object store consolidates training, validation, checkpoint, and model data.
Replaces fragmented legacy storage and cloud-specific services
Standard S3 APIs integrate with KubeFlow, MLflow, Ray, DeepSpeed, and NVIDIA NeMo
Reduces tool sprawl and operational overhead across teams
Erasure Coding, Not Replication
Achieves 11 nines of durability without tripling your storage footprint.
Same protection guarantees at a fraction of the capacity cost
Linear Scale to Exabytes
AIStor scales linearly to billions of objects and exabytes of data in a single namespace.
No 20–30 PB ceilings that force re-architecture
No cluster splits that fragment training datasets
Add nodes, add throughput—performance scales with capacity
Versioning & Governance Built In
Object versioning, immutability, and fine-grained access control ensure reproducibility and compliance at scale.
Every dataset version and checkpoint is preserved and auditable
Bucket-level WORM and legal hold for regulatory requirements
IAM policies enforce data governance across teams and projects
Deploy Anywhere Your Stack Runs
Software-defined and Kubernetes-native—runs on your hardware, your way.
Commodity hardware in your data center or at the edge
Air-gapped and sovereign deployment options
Integrates with NVIDIA GPUDIrect (Reg) RDMA for S3 compatible Storage for accelerated data paths
AIStor was the perfect solution to fix a challenging operational issue around customer reporting, while also enabling us to transform our architecture to be more agile and position us to maximize our ability to leverage AI.
—Organizational Lead
National Payments and Settlements Provider
Proven Results
Quantified outcomes from AIStor customer production deployments.
Single-tier training with cost savings for computer vision AI
Microblink consolidated their master data copy and high-speed training storage into AIStor. The result: 62% lower storage costs, no more cloud sync failures, and 30 training experiments per day across identity and image recognition models.
A leading life sciences company scaled to 20+ PB across lab clusters, HPC, and public cloud—replacing NAS that couldn't keep pace with 2.2M weekly experiments generating continuous streams of microscopy images and sequencing data
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