Rob Pankow 
AI workloads on OpenShift are not only a GPU scheduling problem. They are also a storage, data movement, checkpointing, observability, and platform governance problem. The model may run on accelerators, but the platform succeeds or fails around how quickly data can be staged, reused, protected, and served to the workload.
For enterprise teams, OpenShift is attractive because it can bring AI workloads into the same private-cloud operating model used for other production systems. That only works when storage and data pipelines are designed as first-class parts of the architecture.
Best Practice 1: Separate Experimentation from Production AI Paths
AI teams often start with notebooks, ad hoc datasets, and local volumes. That is fine for early exploration. It is not a production operating model. On OpenShift, experimentation and production should use different namespaces, quotas, storage classes, and data-access controls.
Production AI workloads need clearer guarantees: where checkpoints live, how datasets are versioned, which workloads can consume high-performance storage, and what happens when a node or GPU pool is drained.
| AI storage path | Best fit | Risk if overused |
|---|---|---|
| Object storage | Large datasets and archival artifacts | Weak fit for latency-sensitive working sets |
| Local NVMe | Temporary scratch and cache-heavy jobs | Poor mobility and recovery if treated as primary state |
| Shared block storage | Checkpoints, metadata, and hot working sets | Needs policy to avoid noisy-neighbor effects |
| Database-backed state | Feature stores and application memory | Requires strong latency and HA expectations |
Best Practice 2: Design Around Data Locality and Checkpoints
GPU utilization drops when the storage and data path cannot keep up. The most expensive failure is not a slow disk in isolation. It is underused accelerator capacity because data staging, checkpoint restore, or metadata access is too slow.
A practical OpenShift AI architecture looks like this:
Keep large immutable datasets in object storage where that makes sense. Use fast block storage for hot working sets, checkpoints, metadata-heavy operations, and services where latency affects GPU utilization or application response time. Treat local NVMe as a powerful cache or scratch layer, not the only persistence story.
Best Practice 3: Make Storage Classes Part of AI Governance
OpenShift storage classes should reflect workload intent. Training jobs, inference services, vector databases, metadata stores, and pipeline systems have different persistence requirements. If all AI workloads use the same default class, the platform team loses the ability to govern cost, performance, and recovery.
Useful class boundaries often include hot low-latency block storage, balanced persistent storage, object-backed dataset paths, and scratch-oriented local storage. The point is not to create dozens of choices. The point is to make the important choices explicit and enforceable.
Planning AI workloads on OpenShift or private cloud? Talk to simplyblock about the storage path for checkpoints, hot data, vector databases, and latency-sensitive AI services. Talk to a storage architect
How Simplyblock Fits
Simplyblock fits AI workloads on OpenShift when the platform needs high-performance persistent block storage for hot data, metadata-heavy services, vector databases, checkpoints, or stateful inference components. It is not a replacement for object storage; it complements object storage where low-latency block behavior matters.
This is especially relevant for private-cloud AI. Teams want better control over data residency and infrastructure economics, but they still need performance that does not strand expensive GPU capacity. A Kubernetes-native block storage layer gives the platform team a cleaner way to express storage policy through OpenShift rather than hand-building one-off storage paths for each AI project.
For related context, see AI storage, OpenShift storage, and Kubernetes storage.
Questions and Answers
What are the most important best practices for AI workloads on OpenShift?
Separate experimentation from production, define storage classes by workload intent, protect checkpoints, validate data locality, and monitor GPU utilization alongside storage latency.
Should AI workloads on OpenShift use object storage or block storage?
Usually both. Object storage fits large datasets and artifacts, while block storage fits hot working sets, checkpoints, metadata, vector databases, and latency-sensitive services.
Why does storage affect GPU utilization?
GPUs sit idle when data staging, checkpoint loading, or metadata access is too slow. Storage latency and throughput can directly affect accelerator efficiency.
Is local NVMe enough for AI workloads on OpenShift?
Local NVMe is useful for scratch and caching, but it is risky as the only persistence model when workloads need recovery, mobility, and shared platform operations.
How does simplyblock fit AI workloads on OpenShift?
Simplyblock provides Kubernetes-native block storage for OpenShift AI workloads that need low-latency persistent storage, checkpoint performance, and private-cloud control.
