Unify high-performance storage, GPU-optimized data access, and resilient capacity expansion in one AI data foundation. QSAN helps enterprises move AI from initial deployment to production while keeping performance, scalability, and data growth under control.
As AI expands from proof of concept to production, models, training datasets, RAG knowledge bases,
vector indexes, checkpoints, and application outputs must be shared across more GPU servers, development teams, and AI workflows. QSAN provides a consolidated AI data foundation that delivers fast access to active data, scales alongside growing compute requirements, and supports efficient data placement throughout the AI lifecycle.
Unify diverse access from file, block, and object data
across data ingestion, model development, training,
and inference. A scalable shared data layer keeps
datasets, models, checkpoints, and outputs
continuously accessible as GPU clusters and AI
workloads expand.
Native NVMe architecture and NVIDIA® GPUDirect
Storage streamline data flow between storage and GPU. By reducing CPU-mediated data transfers, the infrastructure can deliver model files, training datasets, vector indexes, and checkpoints more efficiently — helping GPU-intensive AI workflows spend less time waiting for data.
Not every AI dataset requires all SSD performance, while
historical datasets, completed projects, and long-term data
are retained t hrough cost-optimized HDD-based expansion. By adopting hybrid architecture enterprises are able to balance performance and capacity to retain growing volumes of AI data while controlling long-term infrastructure costs.
