Red Hat AI

Discover the advantages of using Java for AI development in regulated industries. Learn about architectural stability, performance, runtime guarantees, and more.

Whether you're just getting started with artificial intelligence or looking to deepen your knowledge, our hands-on tutorials will help you unlock the potential of AI while leveraging Red Hat's enterprise-grade solutions.

Learn how Model Context Protocol (MCP) enhances agentic AI in OpenShift AI, enabling models to call tools, services, and more from an AI application.

Take a look back at Red Hat Developer's most popular articles of 2025, covering AI coding practices, agentic systems, advanced Linux networking, and more.

Discover 2025's leading open models, including Kimi K2 and DeepSeek. Learn how these models are transforming AI applications and how you can start using them.

Learn how to deploy and test the inference capabilities of vLLM on OpenShift using GuideLLM, a specialized performance benchmarking tool.

Learn how to fine-tune a RAG model using Feast and Kubeflow Trainer. This guide covers preprocessing and scaling training on Red Hat OpenShift AI.

Learn how to implement retrieval-augmented generation (RAG) with Feast on Red Hat OpenShift AI to create highly efficient and intelligent retrieval systems.

Learn how to implement identity-based tool filtering, OAuth2 Token Exchange, and HashiCorp Vault integration for the MCP Gateway.

Learn how to migrate from Llama Stack’s deprecated Agent APIs to the modern, OpenAI-compatible Responses API without rebuilding from scratch.

Most log lines are noise. Learn how semantic anomaly detection filters out repetitive patterns—even repetitive errors—to surface the genuinely unusual events.

Integrating AutoRound into LLM Compressor delivers higher accuracy for low bit-width quantization, lightweight tuning, and compressed-tensor compatibility.

Optimize AI scheduling. Discover 3 workflows to automate RayCluster lifecycles using KubeRay and Kueue on Red Hat OpenShift AI 3.

Run the latest Mistral Large 3 and Ministral 3 models on vLLM with Red Hat AI, providing day 0 access for immediate experimentation and deployment.

Learn how to optimize AI inference costs with AWS Inferentia and Trainium chips on Red Hat OpenShift using the AWS Neuron Operator.

Use SDG Hub to generate high-quality synthetic data for your AI models. This guide provides a full, copy-pasteable Jupyter Notebook for practitioners.

This performance analysis compares KServe's SLO-driven KEDA autoscaling approach against Knative's concurrency-based autoscaling for vLLM inference.

Learn how to deploy and manage Models-as-a-Service (MaaS) in Red Hat OpenShift AI, including rate limiting for resource protection.

Use the open source SDG Hub to quickly create custom synthetic data pipelines. Train and evaluate your models faster and more efficiently.

Learn how we built a simple, rules-based algorithm to detect oversaturation in LLM performance benchmarks, reducing costs by more than a factor of 2.

Learn how the llm-d project is revolutionizing LLM inference by enabling distributed, efficient, and scalable model serving across Kubernetes clusters.

Learn how we built an algorithm to detect oversaturation in large language model (LLM) benchmarking, saving GPU minutes and reducing costs.

Simplify LLM post-training with the Training Hub library, which provides a common, pythonic interface for running language model post-training algorithms.

Speculators standardizes speculative decoding for large language models, with a unified Hugging Face format, vLLM integration, and more.

Learn why prompt engineering is the most critical and accessible method for customizing large language models.