Tag: Granite
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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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Choosing a First Model and Accelerator for Red Hat AI (Red Hat Gen AI Series, Part 6)
Sizing a first Granite model and GPU for Red Hat AI is a memory problem, not a benchmark one. Here is how weights, KV cache and the InstructLab training floor decide what you actually buy or rent.
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InstructLab and the LAB Method for Taxonomy Driven Alignment (Red Hat Gen AI Series, Part 5)
InstructLab turns a handful of hand written questions into thousands of training examples. Here is how the LAB method and a taxonomy tree tune a Granite model on your own documents.
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Granite Model Family and Choosing a Size Under Apache 2.0 (Red Hat Gen AI Series, Part 2)
Granite 4.0 comes in four practical sizes under Apache 2.0. Here is how total versus active parameters decide GPU memory, and why H-Tiny, not H-Small, is the right first model for a self hosted support assistant.
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InstructLab on watsonx, from Taxonomy to Aligned Model (IBM Gen AI Series, Part 12)
InstructLab teaches a Granite model new skills from a handful of hand written examples, using a taxonomy, synthetic data generation, and phased training. Here is how it works on watsonx.ai and when to reach for it.
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Prompt Tuning and Fine-Tuning Granite on watsonx (IBM Gen AI Series, Part 11)
watsonx.ai gives you prompt tuning, LoRA, and full fine tuning for Granite. Here is how each method works and how to pick the right one for the size of your data.
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IBM Granite Models, Third-Party Models, and Licensing on watsonx (IBM Gen AI Series, Part 3)
A plain walk through the IBM Granite 4.1 family, the third-party models sitting beside it in watsonx.ai, and why Apache 2.0 and uncapped IP indemnity, not benchmarks, usually decide which model an enterprise ships.
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watsonx.ai Studio and Prompt Lab, Your First Governed Prompt (IBM Gen AI Series, Part 2)
The watsonx.ai studio and Prompt Lab, hands on: projects and the Runtime service, Chat mode after the 2026 removal of Structured and Freeform, decoding parameters, and how prompt length drives your token bill.
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IBM watsonx Generative AI Stack, End to End (IBM Gen AI Series, Part 1)
IBM watsonx explained end to end for beginners: how watsonx.ai, watsonx.data, and watsonx.governance fit together, where the Granite models sit, and what it costs to run.
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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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