Batch inference on OpenShift AI with llm-d: Architecture, integration, and workflows

This blog post discusses the architecture and workflows of the llm-d batch gateway, a Kubernetes-native batch inference service that integrates with OpenShift AI. It highlights how the gateway optimizes batch workloads for Large Language Models (LLMs) by allowing teams to efficiently handle high-volume production tasks, manage GPU resources, and ensure job lifecycle management with fault tolerance. The post provides a detailed explanation of the gateway's components, integration processes, security models, and best practices for its deployment alongside OpenShift AI.