AI inference

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.

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

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.

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

Learn how Red Hat's SastAI initiative, in collaboration with NVIDIA, automates false positive identification in static application security testing (SAST) using generative AI. By employing an agentic, multi-stage research workflow, SastAI reduces noise and improves triage efficiency. Discover the pattern harvesting methodology that greatly enhances the SastAI solution, now offering a tighter solution with better knowledge and reasoning.

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

Learn how to connect a modern Apache Iceberg lakehouse to LLM-hosted models using nothing but SQL on Red Hat OpenShift AI.

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

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

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.

Learn how Model-as-a-Service (MaaS) solves the problem of managing AI costs, security, and models for every developer in an organization.

Learn how llm-d routes each inference request to the GPU that already has the relevant data cached, cutting down on time-to-first-token, and doubling throughput without changing hardware. Discover how Red Hat's stack packages this neatly into a single Kubernetes resource.

Learn how to create a functional Red Hat pizza shop voice agent using Red Hat OpenShift AI, focusing on practical architecture choices and implementation lessons learned along the way.

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.

Red Hat and DeepLearning.AI have released a free hands-on course on the full LLM

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.

Explore the benefits of using Claude for performance analysis on CPU profiles and traces, focusing on the Go Green Tea Garbage collector as a case study. Learn about optimization opportunities and low-level code analysis.

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.

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.