AI inference

Cut Llama 3.1 8B VRAM by 46% without losing accuracy. Master the mechanics of INT8 W8A8 quantization, SmoothQuant, and GPTQ using llm-compressor.

Learn how P-EAGLE in Speculators v0.6.0 uses parallel drafting to reduce LLM latency. Train and deploy custom draft models with this step-by-step guide.

Evaluate AI agents on Red Hat OpenShift AI with IBM CLEAR and EvalHub. Learn how to automate analysis for recurring failure patterns in AI agent execution.

Learn how the vLLM LoRA dynamic loading flaw enables data theft and how to enforce zero trust defenses using Red Hat OpenShift AI and cluster management.

Learn how to automate RAG document processing with Red Hat OpenShift AI for production-ready parsing, embedding, and querying of PDF documents.

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

Learn how to prioritize mixed workloads with Red Hat AI Inference 3.5's GPU-shared flow control

Learn how to optimize self-hosted LLM cost per token. Cut GPU spending and maximize real-world throughput with autoscaling, right-sizing, and vLLM tuning.

Explore LLM inference on Kubernetes using Red Hat AI on EKS. Trace requests from the Envoy gateway through the EPP scheduler down to individual vLLM pods.

Optimize LLM deployment with Neural Navigator on Red Hat OpenShift AI, reducing cost overruns and latency spikes.

Confidently deploy LLMs with Red Hat support: Learn how to determine if your model is supported by Red Hat's vLLM community.

See how a team upgraded Red Hat OpenShift AI 3.3.2 three to four times faster with an AI coding assistant, reducing engineering effort by 60%.

Reduce observability costs with Red Hat OpenShift AI summarizer, bridging the interpretation gap for cloud-native architectures.

Explore Kubernetes resources for Red Hat AI Inference on Amazon EKS, enabling intelligent routing for your model serving.

Stop guessing RAG settings. Discover how AutoRAG uses fast evaluation sweeps to optimize chunking and retrieval precision for small LLMs on your data.

Learn how to run isolated Llama 3.1 8B workloads on a single NVIDIA H100 GPU using OpenShift, Kubernetes dynamic resource allocation, and NVIDIA MIG.

Improve model reliability at inference time with its_hub: Learn how to select accurate outputs without retraining.

Optimize GPU efficiency with Red Hat OpenShift AI 3.4's flow control for llm-d, ensuring priority-based request queuing and fairness policies.

Learn how to build a distributed RAG pipeline with Ray Data on OpenShift AI for high-performance parsing, embedding, and writing to a vector database.

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

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.

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

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.