Category: Red Hat Gen AI
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GPU Sharing on OpenShift AI With Time Slicing and MIG (Red Hat Gen AI Series, Part 18)
One 8B model on a whole A100 is a card billed at full price and used at a fraction. Here is how to split a GPU on OpenShift AI with time slicing and MIG, and which one to pick.
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Serving Granite With KServe and Serving Runtimes on OpenShift AI (Red Hat Gen AI Series, Part 17)
Take the production Granite version from the registry and turn it into a live OpenAI compatible endpoint with KServe on OpenShift AI, then learn where serving runtimes, deployment modes and autoscaling actually break under real traffic.
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Model Registry and Versioning on OpenShift AI (Red Hat Gen AI Series, Part 16)
A tuned model with no version is a rollback you cannot make. Stand up the OpenShift AI model registry, register every Granite version from Python, and turn an hours long re-tune into a seconds long repoint.
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Distributed Training on OpenShift AI With the Training Operator (Red Hat Gen AI Series, Part 15)
Spread the Granite retrain across GPUs with the OpenShift AI Training Operator, PyTorchJob and Kueue, and see the measurements that show when multi node is the wrong call.
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Data Science Pipelines on OpenShift AI, From Notebook to Scheduled Retrain (Red Hat Gen AI Series, Part 14)
Build the support assistant’s nightly retrain as a Kubeflow pipeline on OpenShift AI: wire object storage, move data as artifacts, and stop a green run from shipping a worse model.
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OpenShift AI Platform Architecture, Component by Component (Red Hat Gen AI Series, Part 13)
A working map of Red Hat OpenShift AI, the meta operator, the DataScienceCluster, and the components you should actually turn on, with the serving change most tutorials miss.
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Hardware and Accelerator Sizing for RHEL AI (Red Hat Gen AI Series, Part 12)
Sizing GPUs for RHEL AI is a KV cache problem, not a weights problem. A worked memory calculator, a per accelerator concurrency table for Granite 3.1 8B, and the one flag that wakes a stalled server.
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Evaluating a Tuned Granite Model With MMLU and MT-Bench (Red Hat Gen AI Series, Part 11)
How to score a tuned Granite model honestly on RHEL AI with MMLU, MT-Bench, MMLU Branch and DK-Bench, and why the branch scores, not MMLU, decide whether to ship.
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Fine Tuning Granite With InstructLab Multi Phase Alignment (Red Hat Gen AI Series, Part 10)
Run lab-multiphase training on RHEL AI to tune Granite on your own docs, read the checkpoints MT-Bench actually picks, and avoid the restart prompt that wipes hours of work.
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InstructLab Taxonomy and Synthetic Data Generation on RHEL AI (Red Hat Gen AI Series, Part 9)
Building an InstructLab knowledge taxonomy and running synthetic data generation on RHEL AI, from qna.yaml seed examples to the training JSONL that Part 10 tunes on.
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Serving Granite Locally With ilab and vLLM (Red Hat Gen AI Series, Part 8)
Serving Granite on one RHEL AI box with ilab model serve and vLLM, from the first token to a locked down endpoint, including the tensor parallel error nearly everyone hits on day one.
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Installing RHEL AI From the Bootable Image to First Boot (Red Hat Gen AI Series, Part 7)
RHEL AI ships as a whole bootable operating system, not a package you add to Linux. Here is how to stand up a first box: download the image, size the disks, write a safe Kickstart, log in to the registry and get Granite downloaded before you serve it.
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About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
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