Artificial intelligence

Speculators v0.5.0 introduces DFlash support, enabling single-pass draft token generation with block diffusion for more efficient speculative decoding workflows. The release also adds unified online and offline training through vLLM’s native hidden states extraction system, improving training flexibility, version stability, and production readiness.

Learn how to use Red Hat OpenShift AI's reusable components to build modular AI pipelines, speed up development, and focus on what differentiates your applications.

Learn how to deploy Hermes Agent, a self-improving AI agent with a learning loop, on OpenShift AI with GPU-accelerated vLLM model serving.

Learn how evaluation-driven development (EDD) turns AI optimization from an art into an engineering discipline with EvalHub.

Learn how we fine-tuned the vLLM Semantic Router's embedding model to reduce misrouting rates and improve routing accuracy in enterprise deployments.

Learn about LogAn, an open source tool designed to overcome the limitations of using LLMs to analyze massive volumes of production logs.

Learn how to deploy and serve large language models (LLM) on Rebellions ATOM NPUs using Red Hat OpenShift AI and a certified vLLM container image on the Red Hat AI Inference Server. This post walks through the steps to set up the joint solution between Red Hat and Rebellions, including installing the Node Feature Discovery operator, the Rebellions NPU operator, creating the ATOM hardware profile in OpenShift AI, and creating the vLLM RBLN ServingRuntime.

A Llama Stack-dependent backend, or any rapidly-evolving upstream project faces a version-drift problem. Explore our no-cost solution that provides early warnings.

Learn how an expert red-teamed an infrastructure using Red Hat AI, OpenClaw, and abliterated models on Red Hat OpenShift on IBM Cloud.

Learn how to transform a simple chatbot into an enterprise RAG application by applying metadata filtering, hybrid search, and neural reranking using the OGX framework in Red Hat OpenShift AI.

Learn how to extend your large language model's capabilities with Model Context Protocol (MCP) servers and skills.

Discover how Red Hat OpenShift AI 3.4's Models-as-a-Service (MaaS) capability streamlines AI inference by acting as an integrated AI gateway within the platform, providing centralized governance and routing requests to both self-hosted models and external providers.

Learn how to prevent silent failures in your production AI inference stack with end-to-end benchmarking.

Learn how to prevent GPU waste and financial loss by implementing just-in-time (JIT) checkpointing with Kubeflow Training SDK on OpenShift AI.

How to use a split disk configuration to solve disk space management issues, specifically in OpenShift clusters running large AI/ML workloads.

Learn what's new in Red Hat Enterprise Linux 10.2 and 9.8, including Red Hat build of Podman Desktop, advanced AI assistance, and database and toolset updates.

Learn about the five primary structural challenges in enterprise AI evaluation and how EvalHub addresses them with a unified foundation for AI evaluation.

Learn how our team implemented CI/CD pipelines for the it-self-service-agent AI quickstart and the benefits of using CI/CD for agentic systems.

Learn how Red Hat AI can help address the security challenges of AI agents in production, from semantic malware to container escapes.

Scale agentic AI with Red Hat’s trusted software factory. Use Policy as Code and SBOMs to strengthen your development pipeline and manage software provenance.

Deploy confidential containers and CVMs for AI. Learn how the Red Hat build of Trustee automates attestation to protect patient data on OpenShift and RHEL.

Learn how Red Hat AI 3.4 uses EvalHub to orchestrate AI evaluations on Kubernetes. Scale frameworks like Garak and LightEval with built-in MLflow tracking.

Learn how Kagenti ADK, an open source toolkit, handles the complexities of managing production AI agents. It aligns with the Linux Foundation's Agent2Agent (A2A) protocol and provides a set of runtime services for easier deployment and operation.

Learn about our team's experience implementing a defense-in-depth safety architecture for AI agents using Llama Stack shields.

Learn how Red Hat made Storybook a verification engine for the access management interface on Red Hat Hybrid Cloud Console.