llm-d is a Kubernetes-native high-performance distributed LLM inference framework - a well-lit path for anyone to serve at scale, with the fastest time-to-value and competitive performance per dollar for most models across most hardware accelerators.

With llm-d, users can operationalize gen AI deployments with a modular, high-performance, end-to-end serving solution that leverages the latest distributed inference optimizations like KV-cache aware routing and disaggregated serving, co-designed and integrated with the Kubernetes operational tooling in Inference Gateway (IGW).

Join us and learn more.

Session Topic: Open Source
Industry: Cross Industry
Speaker(s): Carlos Costa