When one server's disk can't keep up with your run

OrangeFS support from Omnibond: parallel file access across many storage servers for training, simulation, and engineering workloads, on-prem or in the cloud.

Upstream Linux kernel client
In mainline since 4.6, so standard nodes can mount the filesystem from the kernel they already run

MPI-IO tuned for real HPC
Parallel reads and writes that match how your code actually runs

In production today
Research labs, national facilities, and enterprise AI/HPC deployments

Systems engineer working inside an open rack in a production storage aisle

In your cluster

Storage that keeps pace with the job

Your team stages a dataset, launches a thousand-rank MPI run, and expects every node to read in parallel. OrangeFS spreads data and metadata across servers so throughput scales at the protocol level with MPI-IO and parallel client access.

What changes: fewer "I/O wait" surprises, mounts your schedulers already understand, and a filesystem your admins can scale by adding nodes.

Built for training, fine-tuning, and simulation

OrangeFS is scale-out parallel storage for campaigns where every GPU or MPI rank hits shared files at once, including jobs that overflow a single server’s SSD early in a run.

Distributed training: parallel reads and checkpoint writes across many nodes
Fine-tuning at scale: stage large model and dataset shards once, then serve concurrent workers from aggregated storage bandwidth
Simulation campaigns: MPI-IO tuned for time-step I/O, restart files, and ensemble runs that hammer storage in sync
Upstream kernel client: ships in mainline Linux (since 4.6) with readahead, plus in-container mounts for Kubernetes and batch schedulers
Distributed metadata: architecture that elastic cloud and on-prem clusters can grow into

You don't need a full orchestration platform to start here. If parallel storage is the bottleneck, start here, and integrate with projectEureka later when governed workspaces matter.