Storage Built for AI Pipelines
TensorWave leverages software-defined, parallel file system to provide a unified data layer across GPU-dense clusters.
High-Throughput Storage That Keeps GPUs Busy
Consistent bandwidth and low latency for training, inference, and checkpointing at scale.
Mixed Multi-Modal Data in One System
Fast access to large video files, image/audio datasets, and millions of small files without bottlenecks.
Scales Cleanly as Clusters Grow
Add nodes to increase performance and capacity without re-architecting your storage layer. As capacity scales, performance scales with it.
Built-in Data Redundancy & Resiliency
Data is replicated across the system to reduce risk and keep workloads running.
Optional, Automatic Scheduled Data Snapshots to Prevent Data Loss
Snapshot scheduling adds an extra layer of protection and operational control.
Access Your Data Multiple Ways
Shared high speed NFS-style mountpoint
S3 compatible object storage
Kubernetes native PV mounts
Why Teams Choose TensorWave
Purpose-built for AI infrastructure at scale.
LOCAL STORAGE
Node Local NVMe
Low-latency, GPU-adjacent storage for temporary data and performance-sensitive stages of the pipeline.
Dataset Caching & Staging
Preprocessing & Shuffling
Fast Local Reads During Training & Inference
Temporary artifacts & intermediate outputs