Keep Every GPU Saturated. Cut Every Wasted Dollar.
GPU clusters are the most expensive line item in AI infrastructure, and storage bottlenecks leave them idle. Training jobs stall waiting for data. Inference hits memory walls as long-context KV caches overflow GPU memory. Enterprises stitch together multiple storage tiers for training and inference, doubling costs and breaking reproducibility.
AIStor delivers high-performance S3 storage purpose-built for NVIDIA GPU infrastructure, keeping GPUs compute-bound and unit economics predictable from edge to exascale.
High-performance storage for the full AI training and inference pipeline on NVIDIA GPUs.
AI Training at GPU Speed
AI Training at GPU Speed Eliminate the two-tiered architecture that forces data staging and copy operations. Train directly from a single S3 tier with GPUDirect RDMA—more experiments per day, faster time to model.
Inference with Deep Context
Offload long-context KV cache to an elastic, low-latency tier on BlueField-4 JBOF—preventing GPU memory overflow while maintaining microsecond access for deep AI context.
Agentic AI & Multi-Agent Systems
Distributed metadata architecture eliminates lock contention, enabling parallel agent access at 400Gbps line rate without centralized bottlenecks.
GPU-Accelerated Vector Indexing
RDMA-enabled integration with NVIDIA cuVS libraries and MilvusDB delivers ~11× faster indexing over CPU—accelerating RAG pipelines and retrieval-augmented workloads.
How It Works
AIStor deploys alongside NVIDIA GPU clusters as the unified storage foundation, replacing fragmented two-tier architectures with a single high-performance S3 tier that feeds every stage of the AI pipeline.
S3 over RDMA with GPUDirect Storage
Bypass the CPU entirely. Data moves directly from AIStor to GPU memory over RDMA, eliminating the kernel copies and context switches that throttle training throughput.
~5× gains in throughput and latency validated on NVIDIA hardware
Zero-copy data path keeps GPUs compute-bound, not I/O-bound
Aligned with NVIDIA Superpod reference architectures
Elastic KV Cache Offload
Long-context reasoning and agentic workloads push KV caches beyond GPU memory limits. AIStor provides an elastic overflow tier at microsecond latency.
Ultra-low latency KV cache on BlueField-4 JBOF
Prevents GPU memory overflow without sacrificing context depth
Scales independently of GPU memory capacity
Single Tier Replaces Enterprise + Hot Tier
Traditional AI architectures require an enterprise storage tier plus a fast hot tier—doubling cost, governance surface, and data movement. AIStor collapses both into one.
No data staging or copy operations before training jobs
Enterprise features (encryption, immutability, IAM) on the same tier that feeds GPUs
Eliminates lineage gaps between primary and hot-tier copies
Full NVIDIA Stack Integration
AIStor integrates across NVIDIA's AI Factory software and hardware ecosystem—not as an afterthought, but as a validated storage component.
Integrates with NVIDIA NIXL, Dynamo, Triton, and NIM
Validated on NVIDIA CMX/STX and ICMS platforms
Deploys on NVIDIA BlueField-4 DPU + JBOF configurations
Exascale in a Single Namespace
One logical namespace from your first petabyte to your first exabyte—no re-architecture, no cluster splits, no ceiling.
No 20-30 PB limits that force namespace fragmentation
Proven at 700PB in a single namespace across 1,088 nodes
Interoperability AIStor connects natively to the tools AI teams already use—no custom connectors, no middleware.
S3-native integration with Kubeflow, MLflow, MLRun, and Hugging Face Hub
Data lakehouse connectivity via Iceberg, Hudi, and Delta
Vector database support for Milvus, LanceDB, and Weaviate
From day one, AIStor proved itself. We moved from PoC to production in weeks, not months, with half the infrastructure and a fraction of the operational burden.
— Data Lakehouse Architect
Major Global Electric Utility
Proven Results
Quantified outcomes from AIStor customer production deployments.
50% faster deployment and single-tier training for computer vision
Microblink needed storage that could serve as both master copy and high-speed training platform. After abandoning Ceph for maintenance complexity, they moved to MinIO—gaining improved performance that lets them run more training experiments per day and accelerate time to production.
50% faster deployment and new AI use cases for financial services
A global financial institution modernized its analytics platform from legacy appliance-based storage to an AIStor-powered data lakehouse. The transition cut deployment time by 50%, boosted AI model efficiency, and enabled entirely new AI-driven use cases for fraud detection and KYC.
Organizations apply AIStor for observability across industries.
Manufacturing
Quality inspection computer vision
Predictive maintenance on IoT/sensor data
Supply chain optimization models
Media
Recommendation model training
Content personalization
Generative AI for assets
Gaming
Player behavior prediction models
Generative AI for game assets
Matchmaking and simulation training
Financial Services
Fraud detection model training
Risk scoring and KYC models
Transaction pattern analysis
Life Sciences
Medical imaging model training
Drug discovery and molecular simulation
Clinical data AI pipelines
Telecom
Network optimization models
Predictive maintenance
Customer experience AI
Saturate Your GPUs. Simplify Your Stack.
Storage should accelerate AI, not throttle it. See how AIStor keeps NVIDIA GPU clusters compute-bound with high-performance S3 from training through inference.
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