Getting started with llm-d for distributed AI inference

This blog post discusses the llm-d, a Kubernetes-native distributed inference stack designed for efficient AI inference using large language models (LLMs). It highlights the need for adaptive computation in LLM inference and describes key features of llm-d, such as smart load balancing, split-phase inference to optimize GPU usage, and disaggregated caching for enhanced performance. The post also provides a step-by-step guide for deploying llm-d, focusing on utilizing smaller models effectively while maintaining performance. Contributors from various tech giants collaborated to develop this community-driven project aimed at improving LLM efficiency in production environments.