Blueprint for Supercharging LLM Inference With "PagedAttention over RDMA"

Name: Blueprint for Supercharging LLM Inference With "PagedAttention over RDMA"
Uploaded: 2025-04-18
Description: KV cache bottlenecks inflate inference latency in multi-node LLM serving. PAoR uses RDMA and NeuralMesh to cut latency and boost throughput with vLLM.

Apr 18, 2025

PagedAttention over RDMA" (PAoR) revolutionizes large language model serving by addressing key-value (KV) cache challenges with RDMA networking

Learn how "PagedAttention over RDMA" (PAoR) revolutionizes large language model serving by addressing key-value (KV) cache challenges with RDMA networking and NeuralMesh. This session showcases seamless integration with vLLM and TensorRT-LLM, enabling faster inference with reduced latency and increased throughput across multi-node environments.