Artificial intelligence

Learn how to deploy and experiment with Gemma 4, the latest open model family from Google DeepMind. This guide covers text, image, and video input, Mixture-of-Experts architecture, and more. Get started with Red Hat AI Inference Server today.

Discover how the Model Context Protocol server for Red Hat Lightspeed transforms the manual process of managing a RHEL fleet lifecycle into an AI-driven strategy.

Learn how to reduce container startup time and improve performance with OpenShift 4.22's new storage configuration options. Discover how to use additional artifact stores, image stores, and layer stores to optimize AI/ML workloads on Red Hat OpenShift.

Learn how to fine-tune large language models in enterprise environments with Training Hub, an open source library for LLM post-training. Discover the benefits of LoRA and QLoRA using Unsloth, including reduced VRAM requirements and faster training times.

Explore the spending transaction monitor AI quickstart, demonstrating agentic AI for intelligent financial monitoring on enterprise-grade infrastructure. Lower the barrier to entry for AI experimentation and refine your AI strategy.

Explore the four pillars of AI coding: vibes, secs, skills, and agents, and learn how they can improve the coding quality and reduce the encoding/decoding gap. Discover the benefits of a spec-driven approach and the importance of modular specs and skills in achieving harmony.

Learn how to integrate Anthropic's Claude Code, an agentic coding tool, using Red Hat AI Inference Server on OpenShift. Keep the inference process private on your own infrastructure while retaining the full Claude Code workflow.

Follow this 4-step process using Training Hub and OpenShift AI to transition LLM fine-tuning from local experiments to repeatable, production-grade workflows.

Learn how to set up vLLM Semantic Router locally with two models: a quantized Qwen3-Coder-Next running on Apple Silicon, and Google's Gemini 2.5 Pro as the cloud fallback. This router can significantly reduce token costs by routing common requests to a less expensive model.

Learn how to set up and run a local AI audio transcription using an Red Hat open source model.

Learn how to deploy multiple large language models (LLMs) behind a single OpenAI-compatible endpoint on OpenShift using a Model-as-a-Service (MaaS) approach. This guide demonstrates how to build an intelligent routing infrastructure that dynamically inspects the request payload and directs traffic based on the specified model field, reducing GPU waste and simplifying application logic.

Learn how to build reliable AI agents with our 8-stage evaluation framework. We explore DeepEval, multi-turn testing, and CI/CD integration for Red Hat AI.

Discover a practical solution pattern for building a modern financial application that makes loan decisions using multiple machine learning systems deployed across hybrid environments.

Headed to Devoxx UK 2026? Visit the Red Hat Developer booth on-site to speak to our expert technologists.

Headed to Devoxx France 2026? Visit the Red Hat Developer booth on-site to speak to our expert technologists.

LLM Compressor v0.10 introduces Distributed Data Parallel (DDP) for faster compression, memory management, and advanced quantization formats. Make model compression workflows more efficient for large language models.

Learn how to enable the NVIDIA RTX PRO 4500 Blackwell Server Edition on Red Hat AI for compact, power-efficient AI deployments. This hardware offers inference performance without adding unnecessary operational complexity for Red Hat AI users.

Learn how to streamline Red Hat OpenShift AI dependency management using Kustomize and GitOps. Use the odh-gitops repo to automate setup via CLI or Argo CD.

This video demonstrates how to deploy Red Hat OpenShift AI dependencies using the odh-gitops repository with Kustomize and the command line.

This video demonstrates how to deploy Red Hat OpenShift AI dependencies using Argo CD and the odh-gitops repository.

Learn how to build agentic AI workflows using cicaddy and MCP servers directly in your existing CI pipeline.

Discover how to optimize training of MoE models with fms-hf-tuning, an open source tuning library for PyTorch FSDP and Hugging Face libraries. Learn about preprocessing data, throughput and memory efficiency features, distributed training, and expert parallelism. Improve your AI and agentic applications on domain-specific enterprise tasks.

Learn how PatchPatrol, an AI-powered code review tool, helps enterprise development teams maintain high quality and security standards on Red Hat OpenShift.

Learn how to manage the security threats and access controls associated with adopting the new Agent Skills functionality.

Learn how to run high-performance computing workloads managed by Slurm within a containerized OpenShift environment using the Slinky operator.