Technology Radar
VAST DataStore is the storage layer of the VAST Data platform. It provides file (NFS and SMB), object (S3) and block (NVMe/TCP) access through a unified namespace. Its disaggregated shared-everything architecture separates stateless compute nodes from shared NVMe storage, allowing capacity and performance to scale independently. We're assessing it because storage throughput increasingly affects how efficiently expensive GPU clusters operate during training, checkpointing and model loading. DataStore lets workloads retain conventional NFS/POSIX or S3 access while using NVMe and RDMA for faster data transfer, offering an alternative to specialized parallel file systems. VAST is also working with NVIDIA on S3 over RDMA and shared KV-cache storage for disaggregated inference.
Compared with Ceph, which offers open-source block, file and object storage on commodity hardware, DataStore is a proprietary platform designed for NVMe, RDMA and AI workloads. However, it’s expensive and ties you to VAST's broader data platform, so it may be more than teams need unless they're training models or running large-scale inference. We think VAST DataStore is worth assessing for teams designing large GPU clusters for AI and high-performance computing (HPC) workloads, where data loading, checkpointing and metadata-heavy access to billions of files can leave expensive GPUs waiting on storage.
