"llm-d serves as the primary implementation of the Kubernetes Gateway API Inference Extension (GAIE), providing inference-aware traffic management via the Endpoint Picker (EPP). This addresses the specific bottleneck of AI serving being stateful and latency-sensitive, which traditional service routing and autoscaling do not account for."
"The latest v0.5 release of llm-d demonstrates its capability to maintain near-zero latency in a multi-tenant SaaS scenario, scaling up to approximately 120,000 tokens per second, showcasing its efficiency and performance."
llm-d has been accepted as a CNCF Sandbox project, placing it under the Linux Foundation's management. This project aims to create an open standard for AI inference across various accelerators and cloud environments. Launched in May 2025 by Red Hat, Google Cloud, IBM Research, CoreWeave, and Nvidia, llm-d has since gained support from additional partners and universities. The framework addresses AI serving challenges by implementing Kubernetes-native inferencing and providing efficient traffic management, maintaining low latency and high scalability.
Read at Techzine Global
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