Artificial intelligence

Learn how to deploy multimodal AI models on edge devices using the RamaLama CLI, from pulling your first vision language model (VLM) to serving it via an API.

Discover SDG Hub, an open framework for building, composing, and scaling synthetic data pipelines for large language models.

Learn about the 5 common stages of the inference workflow, from initial setup to edge deployment, and how AI accelerator needs shift throughout the process.

Dive into the Q3’25 edition of Camel integration quarterly digest, covering the

Learn how to implement spec coding, a structured approach to AI-assisted development that combines human expertise with AI efficiency.

Krkn-AI automates AI-assisted, objective-driven chaos testing for Kubernetes. Discover how it addresses the challenges of reliability in modern systems.

Get a comprehensive guide to profiling a vLLM inference server on a Red Hat Enterprise Linux system equipped with NVIDIA GPUs.

Maximize return on investment in GPU hardware by investing in the appropriate network infrastructure for high-performance distributed training on OpenShift.

This learning path explores running AI models, specifically large language

Learn how to use the OpenShift dynamic accelerator slicer with NVIDIA MIG to split large GPUs into smaller, just-in-time resources.

Move larger models from code to production faster with an end-to-end inference

Learn how to set up Red Hat Lightspeed MCP, a lightweight, self-hosted MCP server that lets you connect LLM-based agents to integrate with existing workflows.

Learn how to integrate incident detection with OpenShift Lightspeed, the AI-powered virtual assistant for Red Hat OpenShift.

Learn how to scale machine learning operations (MLOps) with an assembly line approach using configuration-driven pipelines, versioned artifacts, and GitOps.

Explore key updates in Red Hat Ansible Automation Platform 2.6, including the self-service automation portal and Ansible Lightspeed intelligent assistant.

The LLM Compressor 0.8.0 release introduces quantization workflow enhancements, extended support for Qwen3 models, and improved accuracy recovery.

Learn how llm-d's KV cache aware routing reduces latency and improves throughput by directing requests to pods that already hold relevant context in GPU memory.

Learn how to deploy LLMs like Qwen3-Coder-30B-A3B-Instruct on less infrastructure using Red Hat AI Inference Server's LLM Compressor and OpenShift AI.

DeepSeek-V3.2-Exp offers major long-context efficiency via vLLM on Day 0, deploying easily on the latest leading hardware and Red Hat AI platforms.

Implement cost-effective LLM serving on OpenShift AI with this step-by-step guide to configuring KServe's Serverless mode for vLLM autoscaling.

Learn how to deploy Model Context Protocol (MCP) servers on OpenShift using ToolHive, a Kubernetes-native utility that simplifies MCP server management.

Welcome back to Red Hat Dan on Tech, where Senior Distinguished Engineer Dan Walsh dives deep on all things technical, from his expertise in container technologies with tools like Podman and Buildah, to runtimes, Kubernetes, AI, and SELinux! In this episode, Eric Curtin joins to discuss Sorcery AI, a new AI code review tool that has been helping to find bugs, review PRs, and much more!

See how vLLM’s throughput and latency compare to llama.cpp's and discover which tool is right for your specific deployment needs on enterprise-grade hardware.

Deploy DialoGPT-small on OpenShift AI for internal model testing, with step-by-step instructions for setting up runtime, model storage, and inference services.

Walk through how to set up KServe autoscaling by leveraging the power of vLLM, KEDA, and the custom metrics autoscaler operator in Open Data Hub.