Artificial intelligence

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.

Learn about the three optimization levers for distributed AI inference: prefill/decode disaggregation, KV cache strategy, and speculative decoding.

Learn how to connect the EvalHub runtime to internal or external model servers using service account tokens, API keys, or custom certificates.

This video demonstrates how to deploy Open Code, an open-source AI coding assistant, as a secure, multi-user web application on Red Hat OpenShift.

Learn about the five-dimensional design space in modern LLM serving, including tensor, pipeline, expert, data, and context parallelism.

Learn how to deploy MemPalace as a production-ready Model Context Protocol server on OpenShift AI with HTTP/WebSocket transport and Kubernetes health probes.

Learn how to optimize EvalHub's benchmark evaluations by using Kueue for fair resource sharing, priority-based job scheduling, and automatic queueing.

Discover the new features of Red Hat build of Apache Camel 4.18, including AI-driven semantic processing, Camel CLI Launcher, visual integration test flows, and more.

Discover how personal AI notebooks in Red Hat Developer Lightspeed can help developers find specific details in project documents quickly, grounded in context.

Look inside Red Hat AI Inference on Amazon EKS to understand its core architectural components and Kubernetes resources.

Discover how to use EvalHub and OCI persistence to make your AI evaluation results immutable, content-addressable, and fully auditable.

Explore how modern agentic AI improves upon traditional text-to-SQL approaches.

Explore the mechanics of gradient synchronization in PyTorch distributed training, focusing on MPI primitives like All-Reduce and core techniques like pipeline parallelism, tensor parallelism, and sharded data parallelism.

Learn when to use llama.cpp and vLLM for local inference of large language models (LLMs). Discover the key differences, benchmarks, and use cases for each engine.

Learn how speculative decoding can improve the performance of large language models (LLMs) in production by using a small, fast model to generate tokens speculatively and a large model to verify them.