llm-d 1.0 Adds Controls for In House AI Inference

Oct 20, 2026
The Kubernetes based inference framework adds workload priorities, uninterrupted upgrades, and routing tools for organizations running large AI models on their own infrastructure.

IBM and other contributors released version 1.0 of the llm-d distributed AI inference framework, according to SiliconANGLE. The open source project helps organizations run large models on their own Kubernetes infrastructure.

Version 1.0 adds quality of service controls for multiple tenants, uninterrupted upgrades, and serving patterns for AI agents. Its routing system sends requests to replicas that already hold relevant cached context, while another feature moves cached data among GPU memory, CPU memory, and storage.

IBM, Red Hat, and Google are the lead maintainers. The framework works with inference engines including vLLM and SGLang, and is hosted as a sandbox project by the Cloud Native Computing Foundation.

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