Match instructions to the installed release
Red Hat's deployment guide lists prerequisites and supported accelerator runtimes. Check the documentation for your installed release rather than copying an old container tag or a manifest from another platform version.
Verify resources and model access
Confirm accelerator support, the selected model's runtime compatibility and authorized access to its files. Set resource requests from your measured workload. A Kubernetes memory limit is not the same as available GPU VRAM.
Test the endpoint before routing traffic
Verify authentication, network exposure, storage permissions and representative requests. Record model precision, context, concurrency, memory, errors and response times. These are proposed acceptance checks, not a claimed FyreLinkz cluster benchmark.
Plan recovery and capacity changes
Test a restart and model reload, decide how failures are detected, and document rollback. Load testing should include queued and failed requests. This guide does not establish a throughput multiplier or promise that a storage connection prevents downtime.
Continue with workload sizing
Use the local LLM memory guide to separate weight size from runtime demand. Follow the release-specific Red Hat documentation for implementation details.
Frequently asked questions
Does one allocated GPU guarantee an LLM will fit?
No. Model precision, context, concurrency and runtime allocations determine memory demand. Kubernetes GPU resource allocation alone does not establish sufficient VRAM.
Which OpenShift AI serving runtime should I choose?
Use the supported runtime and accelerator configuration for your installed OpenShift AI release. Validate the exact model, storage connection, endpoint security and representative workload before production use.
Sources & further reading
Primary sources checked Oct 17, 2026. Vendor statements are attributed; editorial advice is our own.
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