David Whyte-Gray

This article explains how dynamic resource allocation (DRA) improves the performance of distributed LLM inference workloads by aligning GPU and network interface topologies. Traditional Kubernetes device plugins often assign GPUs and SR-IOV virtual functions (VFs) that are not on the same PCIe root, causing suboptimal RDMA bandwidth and increased latency for disaggregated architectures like llm-d.

Learn how to improve the performance of your vLLM deployments with a diagnostic workflow that isolates latency issues and server saturation. Discover the key metrics to monitor and techniques to alleviate memory pressure.