Artificial intelligence

Learn how PagedAttention solves the memory waste problem of traditional LLM systems by breaking the cache into small, on-demand blocks.

Dive into the Q2’25 edition of Camel integration quarterly digest, covering the

Learn how to deploy Open Platform for Enterprise AI ChatQnA application in OpenShift with AMD Instinct hardware.

Learn about the advantages of prompt chaining and the ReAct framework compared to simpler agent architectures for complex tasks.

Discover how Bunsen tracks and analyzes large and busy upstream projects.

Discover the comprehensive security and scalability measures for a Models-as-a-Service (MaaS) platform in an enterprise environment.

Learn how to overcome compatibility challenges when deploying OpenShift AI and OpenShift Service Mesh 3 on one cluster.

Harness Llama Stack with Python for LLM development. Explore tool calling, agents, and Model Context Protocol (MCP) for versatile integrations.

This beginner's guide to Podman AI Lab walks through setting up Podman Desktop, installing the AI Lab extension, and launching your first RAG chatbot.

Get started with AI in Node.js. This cheat sheet covers selecting models, using servers like Ollama, and client libraries like LangChain.js for AI integration.

Ollama makes it easy for developers to get started with local model experimentation, while vLLM provides a path to reliable, efficient, and scalable deployment.

Learn how to build a Model-as-a-Service platform with this simple demo. (Part 3 of 4)

Catch up on the most popular articles published on Red Hat Developer this year. Get insights on Linux, AI, Argo CD, virtualization, GCC 15, and more.

Discover the new features and tools in RHEL 10.

Learn how RamaLama's integration with libkrun and microVMs enhances AI model isolation, security, and resource efficiency for deployments.

Boost inference performance by up to 2.5X with vLLM's Eagle 3 speculative decoding integration. Discover how in this blog post.

Explore the architecture of a Models-as-a-Service (MaaS) platform and how enterprises can create a secure and scalable environment for AI models. (Part 2 of 4)

Explore essential Linux commands, package management with DNF, and the Red Hat Enterprise Linux web console on Red Hat Enterprise Linux 10. Learn how to update packages, install packages, and use the Red Hat Enterprise Linux web console for system administration tasks. Discover the benefits of Insights Image Builder for creating golden images and containers.

Discover how to communicate with vLLM using the OpenAI spec as implemented by the SwiftOpenAI and MacPaw/OpenAI open source projects.

Discover how model compression slashes LLM deployment costs for technical practitioners, covering quantization, pruning, distillation, and speculative decoding.

This article introduces Models-as-a-Service (MaaS) for enterprises, outlining the challenges, benefits, key technologies, and workflows. (Part 1 of 4)

Learn how to evaluate the performance of your LLM deployments with the open source GuideLLM toolkit to optimize cost, reliability, and user experience.

RamaLama's new multimodal feature integrates vision-language models with containers. Discover how it helps developers download and serve multimodal AI models.

Integrate Red Hat AI Inference Server with LangChain to build agentic document processing workflows. This article presents a use case and Python code.

Learn how OpenShift Lightspeed performed when asked handle complex OpenShift scenarios, such as application security and advanced configurations.