High-throughput NVMe storage for training data, model checkpoints, and inference serving on Red Hat® OpenShift® AI.
AI and machine learning workloads on Red Hat® OpenShift® generate storage requirements that general-purpose Kubernetes storage was not designed for: high-throughput sequential reads during training runs, high write throughput for model checkpoints, shared dataset access across parallel training pods, and low-latency random I/O for inference serving. Simplyblock is an NVMe block storage platform that addresses all four patterns through a Kubernetes-native CSI driver — without additional storage hardware, without cloud-only dependencies, and without the overhead of a file system layer between the application and the NVMe drive.
The AI Storage Gap
AI and ML pipelines on OpenShift have distinct storage I/O patterns that general-purpose Kubernetes storage is not optimized for.
Model training reads large dataset files sequentially at high throughput. When storage cannot keep up with the GPU or CPU training rate, the compute sits idle waiting for data — wasting expensive hardware and extending training run time.
Training jobs write model checkpoints frequently to protect against interruption. Slow checkpoint writes extend the period where new gradient updates cannot be applied, and slow recovery means more lost training time when a job is interrupted.
Parallel training jobs reading from the same dataset volume create I/O contention when the storage layer is not designed for concurrent high-throughput reads from multiple pods simultaneously.
Model serving for inference generates random I/O patterns when loading model weights and serving requests. High latency at the storage layer translates directly to higher inference response times.
How Simplyblock Addresses It
Purpose-built block storage that handles the distinct I/O patterns of training, checkpointing, and inference — all through a single Kubernetes-native CSI driver on Red Hat® OpenShift®.
Simplyblock delivers NVMe-native sequential throughput for the large-file, high-bandwidth reads that model training generates. Training data stored on simplyblock persistent volumes is served at the throughput NVMe hardware is capable of — keeping GPUs and CPUs fed without storage becoming the bottleneck. Compatible with Red Hat® OpenShift® AI (RHOAI) pipeline operators and standard Kubernetes batch job patterns.
Simplyblock provides high write throughput and sub-millisecond latency for checkpoint I/O patterns. Checkpoints complete faster, reducing the window where training state is unprotected, and recovery from an interrupted job restores to a more recent state. Instant copy-on-write snapshots provide an additional safety net for critical training runs.
Simplyblock supports ReadWriteMany (RWX) volumes for shared dataset access across parallel training pods, eliminating the need to copy training data to each node. For inference serving, the same NVMe block storage delivers the low-latency random I/O that model weight loading and serving require. See database storage on Kubernetes for how the same low-latency profile applies to vector databases and feature stores.
What Teams Gain
Storage that keeps pace with GPU and CPU training rates, protects training runs with fast checkpointing, and serves model inference with low latency.
NVMe sequential throughput keeps training data flowing at the rate GPU and CPU training processes can consume it. Expensive training hardware stays utilized rather than waiting for storage I/O.
Higher checkpoint write throughput means more frequent saves complete in less time. When a training job is interrupted, recovery starts from a more recent checkpoint with less retraining required.
ReadWriteMany volumes let multiple training pods access the same dataset concurrently without per-node data copies or storage layer contention that slows parallel training jobs.
Sub-millisecond block storage latency for model weight loading and serving reduces the storage component of inference response time — directly improving model serving SLAs.
Add nodes to the simplyblock storage pool as training infrastructure grows. Capacity and throughput scale linearly without storage array upgrades or procurement cycles.
Teams running OpenShift AI on-premises or on bare metal get the same NVMe storage performance as cloud-based deployments — without cloud-only storage dependencies or egress costs.
Yes. Simplyblock installs on Red Hat® OpenShift® via Helm or OperatorHub and provides a standard Kubernetes CSI driver. OpenShift AI pipeline operators and data science workloads provision simplyblock persistent volumes using the same Kubernetes storage class and PVC APIs they use for any other storage provider.
Training workloads with large sequential dataset reads, workloads that require frequent model checkpointing, parallel training jobs that need shared dataset access, and inference serving workloads where model loading latency matters. Vector database and feature store workloads on OpenShift also benefit from the same low-latency block I/O profile.
Yes. Simplyblock supports ReadWriteMany (RWX) persistent volumes, allowing multiple training pods to access the same dataset volume concurrently without each pod requiring its own copy of the training data.
Model checkpoints are written to simplyblock persistent volumes through the standard Kubernetes CSI interface. Simplyblock delivers high write throughput for checkpoint operations, and instant copy-on-write snapshots can be used to create point-in-time protection for training run state.
Yes. Simplyblock runs on bare metal OpenShift deployments without cloud provider dependencies. See the OpenShift bare metal storage page for the bare metal-specific architecture details.
Yes. Simplyblock storage is consumed by pods running on GPU nodes the same way as any other Kubernetes workload — via persistent volume claims backed by simplyblock storage classes. There is no GPU-specific configuration required.
Ask your AI assistant to compare storage options for AI and ML workloads on Red Hat® OpenShift® — NFS, ODF/Ceph, local PVs, and simplyblock — on throughput, latency, and shared dataset access patterns.