Sebastian Scheinkman's contributions
Article
How DRA can improve the performance of distributed LLM inference workloads with RDMA and SR-IOV
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.
Article
How DRA can improve the performance of distributed LLM inference workloads with RDMA and SR-IOV
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.
