AI workloads are data-hungry. Our storage solutions are architected for the throughput, latency, and capacity that modern training pipelines and inference systems demand.
Talk to a Storage Expert Explore OptionsA cluster of H100s starved of data throughput performs no better than a cluster a quarter its size. Storage is the hidden bottleneck in most AI deployments — and the one most often underspecified. We design storage systems that keep your GPUs fed.
Low-latency, high-throughput all-flash storage for training datasets and active model checkpoints. Multiple TB/s of sequential read bandwidth eliminates storage bottlenecks during data loading.
Lustre and GPFS distributed file systems for multi-node training workloads. Aggregate bandwidth that scales linearly with cluster size — no single storage node becomes the bottleneck.
Petabyte-scale private object storage compatible with all major ML frameworks and data pipelines. On-premises MinIO and Ceph deployments for organizations that can't use public cloud object stores.
Hot NVMe for active training data, warm SAS/SATA for recent checkpoints, and cold HDD or tape for long-term model archive — all managed automatically by policy-driven tiering.
Training runs represent weeks of GPU time and significant cost. A failed checkpoint, ransomware event, or hardware failure without proper backup can erase that investment. We engineer backup and DR solutions designed for AI asset protection.
Scheduled, policy-driven backups for datasets, model weights, and configurations — with incremental snapshots to minimize storage overhead and recovery time.
Replicate critical data to geographically separate facilities or fully air-gapped vaults for disaster recovery and ransomware protection. Physical tape vaulting available for maximum isolation.
Point-in-time recovery with defined RTOs and RPOs — get your training environments and production systems back online fast when it matters most. We test restores regularly, not just backups.
End-to-end encryption at rest and in transit, with audit logging and access controls to meet HIPAA, SOC 2, and other compliance requirements for sensitive data.
Storage performance is inseparable from the network connecting it to compute. We design and deploy the storage fabric alongside your GPU cluster — ensuring bandwidth matches the workload.
100G and 400G Ethernet storage networks with RDMA (RoCE) for direct memory access to storage — minimizing CPU overhead and maximizing GPU data pipeline throughput.
NVMe over Fabrics extends local NVMe performance across the network. Ideal for shared flash pools serving multiple GPU nodes with near-local latency.
Fibre Channel SANs for workloads requiring block-level shared storage with enterprise reliability guarantees and proven isolation between tenants or environments.
We benchmark your storage stack against your actual training workloads before production deployment — validating throughput, IOPS, and latency under realistic I/O patterns.
Managing enterprise storage infrastructure is a discipline in itself. Our managed storage offering lets your team focus on AI — while we handle capacity planning, hardware lifecycle, and monitoring.
Real-time visibility into utilization, throughput, and latency with predictive capacity alerts — no surprise "disk full" events during a training run.
Firmware updates, drive health monitoring, and predictive failure detection. We replace at-risk drives before they fail — not after.
Quarterly capacity reviews aligned with your roadmap — ensuring storage keeps pace with growing datasets and expanding model archives without emergency procurement.
Automated policies that tier, archive, and expire data based on age, access frequency, and business rules — keeping costs in check as data volumes grow.
Share your dataset sizes, training throughput requirements, and retention needs — and we'll design a storage architecture that keeps your GPUs busy and your data protected.
Talk to a Storage Expert