Red Hat AI

This learning path explores running AI models, specifically large language

Move larger models from code to production faster with an enterprise-grade

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

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.

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.

Integrate the Kubernetes MCP server with OpenShift and VS Code to give AI assistants a safe, intelligent way to interact with your clusters.

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.

AI agents are where things get exciting! In this episode of The Llama Stack Tutorial, we'll dive into Agentic AI with Llama Stack—showing you how to give your LLM real-world capabilities like searching the web, pulling in data, and connecting to external APIs. You'll learn how agents are built with models, instructions, tools, and safety shields, and see live demos of using the Agentic API, running local models, and extending functionality with Model Context Protocol (MCP) servers.Join Senior Developer Advocate Cedric Clyburn as we learn all things Llama Stack! Next episode? Guardrails, evals, and more!

Learn how to install Red Hat OpenShift AI to enable an on-premise inference service for Ansible Lightspeed in this step-by-step guide.

Discover the benefits of using Rust for building concurrent, scalable agentic systems, and learn how it addresses the GIL bottleneck in Python.

Learn how to deploy Red Hat AI Inference Server using vLLM and evaluate its performance with GuideLLM in a fully disconnected Red Hat OpenShift cluster.

Learn how to deploy the Qwen3-Next model on vLLM using Red Hat AI. This guide covers the steps for serving the model with Podman.

Enhance your Python AI applications with distributed tracing. Discover how to use Jaeger and OpenTelemetry for insights into Llama Stack interactions.

Learn how to deploy the lightweight AI model Llama-3.2-1B-Instruct-quantized.w8a8 using Red Hat AI Inference Server containerization.

Discover the vLLM Semantic Router, an open source system for intelligent, cost-aware request routing that ensures every token generated truly adds value.

Deploy a Llama language model using Red Hat OpenShift AI. This guide walks you through GPU setup, model deployment, and internal and external testing.

Explore a fashion AI search on Red Hat OpenShift AI with EDB Postgres AI.

Learn to run and serve OpenAI's gpt-oss models locally with RamaLama, a CLI tool that automates secure, containerized deployment and GPU optimization.

Building AI apps is one thing—but making them chat with your documents is next-level. In Part 3 of the Llama Stack Tutorial, we dive into Retrieval Augmented Generation (RAG), a pattern that lets your LLM reference external knowledge it wasn't trained on. Using the open-source Llama Stack project from Meta, you'll learn how to:- Spin up a local Llama Stack server with Podman- Create and ingest documents into a vector database- Build a RAG agent that selectively retrieves context from your data- Chat with real docs like PDFs, invoices, or project files, using Agentic RAGBy the end, you'll see how RAG brings your unique data into AI workflows and how Llama Stack makes it easy to scale from local dev to production on Kubernetes.