Red Hat AI

Explore how Red Hat Developer Hub and OpenShift AI work together with OpenShift to build workbenches and accelerate AI/ML development.

This article demystifies AI/ML models by explaining how they transform raw data into actionable business insights.

Learn how to build AI applications with OpenShift AI by integrating workbenches in Red Hat Developer Hub for training models (part 1 of 2).

Discover the new Llama 4 Scout and Llama 4 Maverick models from Meta, with mixture of experts architecture, early fusion multimodality, and Day 0 model support.

Explore how RamaLama makes it easier to share data with AI models using retrieval-augmented generation (RAG), a technique for enhancing large language models.

Learning the naming conventions of large language models (LLMs) helps users select the right model for their needs.

Explore how to run tools with Node.js using Llama Stack's completions API, agent API, and support for in-line tools, local MCP tools, and remote MCP tools.

Learn how quantized vision-language models enable faster inference, lower costs, and scalable AI deployment without compromising capability.

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Headed to DevNexus 2025? Visit the Red Hat Developer booth on-site to speak to our expert technologists.

The Red Hat Developer program brings developers together to learn from each other and create more extraordinary things, faster. We serve the builders. Those who solve problems and create their careers with code.

This article demonstrates how to fine-tune LLMs in a distributed environment with open source tools and the Kubeflow Training Operator on Red Hat OpenShift AI.

Explore inference performance improvements that help vLLM serve DeepSeek AI models more efficiently in this technical deep dive.

Visit the Red Hat Developer booth to speak with our expert technologists. Building and delivering modern, innovative apps and services is more complicated and fast-moving than ever. Red Hat Developer has the answers and expertise to help you succeed.

Podman AI Lab, which integrates with Podman Desktop, provides everything you need to start developing Node.js applications that leverage large language models.

Explore new open source quantized reasoning models based on the DeepSeek-R1-Distill suite that deliver near-perfect accuracy and inference speed improvements.

Discover Sparse Llama: A 50% pruned, GPU-optimized Llama 3.1 model with 2:4 sparsity, enabling faster, cost-effective inference without sacrificing accuracy.

Explore how vLLM's new multimodal AI inference capabilities enhance performance, scalability, and flexibility across diverse hardware platforms.

Explore multimodal model quantization in LLM Compressor, a unified library for optimizing models for deployment with vLLM.

Explore how distributed inference works within vLLM in this recap of Neural Magic's vLLM Office Hours with Michael Goin and Murali Andoorveedu, a vLLM committer from CentML.

Wanting to use your personal or organizational data in AI workflows, but it's stuck in PDFs and other document formats? Docling is here to help. It’s an open-source tool from IBM Research that converts files like PDFs and DocX into easy-to-use Markdown and JSON while keeping everything structured. In this video, join developer advocate Cedric Clyburn to see how it works, We'll walk through a demo using LlamaIndex for a question-answering app, and share some interesting details and benchmarks. Let’s dig in and see how Docling can make working with your data so much easier for RAG, Fine-Tuning models, and more.

The rise of large language models (LLMs) has opened up exciting possibilities for developers looking to build intelligent applications. However, the process of adapting these models to specific use cases can be difficult, requiring deep expertise and substantial resources. In this talk, we'll introduce you to InstructLab, an open-source project that aims to make LLM tuning accessible to developers and data scientists of all skill levels, on consumer-grade hardware.We'll explore how InstructLab's innovative approach combines collaborative knowledge curation, efficient data generation, and instruction training to enable developers to refine foundation models for specific use cases. Through a live demonstration, you’ll learn how IBM Research has partnered with Red Hat to simplify the process of enhancing LLMs with new knowledge and skills for targeted applications. Join us to explore how InstructLab is making LLM tuning more accessible, empowering developers to harness the power of AI in their projects.

Learn how to run distributed AI training on Red Hat OpenShift using RoCE with