Enterprise AI inference demands two things simultaneously: performance that keeps pace with production workloads and economics that don't collapse at scale. Most storage forces a trade-off: fast enough or affordable, never both.
AIStor eliminates that choice with microsecond-latency S3 storage that scales on commodity hardware, delivering GPU-saturating throughput at a fraction of the cost of proprietary AI storage. High power when it matters. Cost efficient where it counts.
High-performance storage for production AI inference at enterprise scale.
KV Cache Offload & Shared Context Memory
Offload KV cache that exceeds GPU HBM to a shared, persistent storage tier—eliminating recomputation, reducing cost-per-token, and enabling longer context windows without adding GPUs.
Agentic AI & Multi-Agent Coordination
Provide coordinated agents with a shared, low-latency data layer for multi-step reasoning chains—so context is persisted, shared across agents, and retrieved at GPU speed.
Long-Context Reasoning
Support million-token-plus context windows for document analysis, code review, and medical synthesis by extending GPU memory into a fast, persistent storage tier with no archive penalties.
Real-Time Autonomous Decision-Making
Power fraud detection, risk scoring, compliance checks, and personalization engines with line-rate data retrieval—because a delayed decision is a missed decision.
How It Works
AIStor sits alongside your GPU clusters as the high-performance storage backend for inference workloads—offloading KV cache, feeding models at GPU speed, and eliminating the storage bottleneck that leaves expensive silicon idle.
NVIDIA GPUDirect® RDMA for S3 compatible Storage*
Kernel bypass via RDMA verbs eliminates the TCP/IP stack, delivering sub-200μs object access vs. 2–5ms conventional paths.*Currently in Tech Preview.
Single DMA transfers keep GPUs fed instead of stalling
10–25x latency improvement validated in tech preview
Sub-200μs object access keeps GPUs compute-bound instead of waiting on storage
Elastic KV Cache Tier
Offload KV cache that overflows GPU HBM to a shared, persistent object store—fully queryable, no recomputation required.
Eliminates costly context recomputation for multi-turn conversations
Shared across inference nodes for multi-agent coordination
Scales linearly with concurrency—no capacity ceilingsk list text
Distributed Architecture, Zero Bottlenecks
Stateless architecture with no centralized metadata server to bottleneck reads or writes.
Every node serves requests independently without serialization
Saturates 400Gbps Ethernet on BlueField DPUs
No degradation as context volumes grow from terabytes to petabytes
Native NVIDIA Integration
Purpose-built for the NVIDIA AI ecosystem—BlueField DPUs, GPUDIrect® RDMA for S3 Compatible Storage, Dynamo, and NIXL.
Only S3-native object storage running natively on BlueField DPUs today
GPUDIrect® RDMA for S3 Compatible Storage establishes direct GPU-to-storage data paths
Same binary runs on x86, ARM, and DPU architectures with zero code changes
Single Binary, Any Scale
A ~200MB binary with no metadata database, no background processes, and no dedicated storage controllers.
Runs on commodity hardware at S3 economics
Scales from pilot to exabyte with the same deployment
No proprietary appliances, no vendor lock-in
Deploy Anywhere Your GPUs Run
Software-defined and Kubernetes-native—runs on your hardware, your way.
Alongside GPU clusters on standard infrastructure
Deployed for fast access Context Memory Storage
Air-gapped and sovereign deployment options
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.
70–80% faster data-intensive operations
A global digital payments provider replaced legacy storage with AIStor, cutting merchant reporting from 7–10 days to under 2 days—eliminating storage performance bottlenecks at scale.
AIStor is proven at exabyte scale across more than half the Fortune 500, with distributed architecture that saturates 400Gbps Ethernet without centralized bottlenecks on BlueField DPUs.
Built for Real-World Applications
Organizations apply AIStor for observability across industries.
Manufacturing
Recommendation model training
Content personalization
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 trainingMatchmaking and simulation training
Financial ServicesFinancial 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
Lower Cost Per Token. Faster Inference. Smarter Agents.
GPU idle time is the most expensive line item in inference. Stop paying for silicon that produces nothing. See how AIStor keeps GPUs saturated and cost-per-token predictable.
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