vLLM is a high-throughput LLM inference system that utilizes components such as paged attention, continuous batching, prefix caching, and specdec. It supports multi-GPU and multi-node dynamic serving to enhance performance at scale.

This guide provides instructions for configuring a two-node AMD Strix Halo cluster using Intel E810 (RoCE v2) for distributed vLLM inference with Tensor Parallelism. It covers hardware prerequisites, host configuration for Fedora 43, toolbox installation, network verification, cluster operation, and troubleshooting steps.

The vLLM Blog provides technical articles, release announcements, model guides, and community updates related to the vLLM project. The latest update includes details about the Eagle 3.1 model release.