Retrieval Inference for scale and performance

This post details Pinecone's optimizations for retrieval inference, focusing on embedding generation and reranking. It covers model optimization using NVIDIA TensorRT (kernel tuning, layer fusion, dynamic tensor optimization, precision reduction), hardware selection, and maximizing GPU utilization with NVIDIA Triton Inference Server's dynamic batching. The post also highlights the scaling strategy of using dedicated query and passage infrastructure to ensure low-latency query responses are not impacted by batch passage embedding operations. It emphasizes how these integrated inference capabilities simplify RAG and agent-based application development by reducing the number of API calls and external dependencies.