Category: AI Stack
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llm-d Distributed Inference on Kubernetes for Granite at Scale (Red Hat Gen AI Series, Part 23)
llm-d spreads vLLM inference across a Kubernetes cluster with prefill decode disaggregation and KV cache aware routing. When it pays off, and how to deploy it behind an inference gateway.
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Model Compression and Quantization With LLM Compressor (Red Hat Gen AI Series, Part 22)
Quantizing Granite with LLM Compressor cuts the support assistant from 16 GB to under 5 GB of weights. A format by format comparison of FP8, INT8, INT4 and NVFP4, with the memory, accuracy recovery and hardware trade offs named.
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vLLM Internals, PagedAttention and Continuous Batching Explained (Red Hat Gen AI Series, Part 21)
vLLM turns model serving from a compute problem into a paging problem. Here is how PagedAttention and continuous batching actually work inside the Red Hat AI Inference Server, and where they break under load.
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Red Hat AI Inference Server, a Hardened vLLM Distribution (Red Hat Gen AI Series, Part 20)
Red Hat AI Inference Server is upstream vLLM packaged as a supported, hardened container. Here is what it adds, how to serve Granite behind it, and how to benchmark it honestly.
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Multi Tenancy, Projects and Resource Quota on OpenShift AI (Red Hat Gen AI Series, Part 19)
How to give shared GPU nodes accountable owners on OpenShift AI: data science projects, ResourceQuota, LimitRange and Kueue fair-share queues, with the failures each one hides.
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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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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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