Artificial intelligence

Boost AI and analytics workloads with Kove:SDM on Red Hat OpenShift, enabling applications to access memory resources beyond local node limits.

Combine OpenShift and OpenShell to improve AI agent security, protecting against data exfiltration and container escapes.

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

Learn how to parse SEC filings into JSON objects matching a given schema with agentic-graphrag-finance.

Learn how to connect GitHub and incident detection MCP servers to OpenShift Lightspeed. Automate your cluster troubleshooting and GitOps workflows using AI.

Operationalize AI agents with OpenShift and Kubernetes primitives for manageable, cost-optimized AIOps.

Learn how to build an open cloud native architecture for AI agents. This blueprint explains how to improve workload isolation and implement inference routing.

Learn how to set up local agentic AI computer use. Run quantized models like Qwen 3.6 with Hermes to automate desktop tasks on your own terms today.

Learn how OpenShift and OpenShell combine for dual protection of AI coding agents, reducing attack surface and securing your infrastructure.

Boost AI-native Red Hat OpenShift observability with obs-mcp, automating complex data correlations for proactive incident management.

Learn about Red Hat build of Agent Sandbox, a Kubernetes-native platform for running isolated workloads with enhanced security using Kata Containers.

Deploy a self-hosted AI coding assistant with vLLM and Red Hat OpenShift AI for privacy and operational independence.

Discover how to configure EvalHub evaluation collections on Red Hat AI. Run Lighteval, Garak, and GuideLLM in parallel for a unified LLM pass/fail verdict.

Learn how to compress, serve, and benchmark LLMs with vLLM.

Learn how to protect your codebase from security vulnerabilities introduced by AI agents with Red Hat dependency analytics 1.0, an open source extension for VS Code and other editors.

Learn how smarter data generation strategies can reduce the cost and time needed to train high-quality speculator models for speculative decoding. This post shares findings on cross-distillation, training efficiency, and production inference gains that deliver faster LLM serving with no loss in output quality.

Learn how to unlock observability for Models-as-a-Service in Red Hat OpenShift AI 3.4 with the new usage dashboard.

Learn about the llm-d batch gateway, a Kubernetes-native batch inference service that plugs into the same llm-d inference stack managed by Red Hat OpenShift AI.

Learn how to isolate AI agents using the supervisor pattern and OpenShell sandboxes. Protect credentials and limit blast radius during incident response.

Explore a demo of serving a multimodal model (Qwen3-Omni) with vLLM-Omni on a single hardware accelerator.

Learn how to scale document processing with a guided example that combines Docling for structure-aware parsing, Ray Data for distributed streaming execution, and Red Hat OpenShift AI.

Discover how the MCP standardizes tool integration, how event streaming improves user experience, and how to safely deploy stochastic reasoning engines.

Learn how to implement GPU-as-a-Service on Red Hat OpenShift using Kueue, NVIDIA MIG, and a custom dashboard plug-in for self-service GPU resource booking.

Learn how to optimize deployment of vLLM for various traffic shapes, including high-concurrency chat, long-context RAG, high-throughput batch, and distributed AI-grid.

Explore the Data Governance Copilot architecture, integrating OpenShift AI with PG Airman MCP server for robust, agentic Postgres analytics.