Retrieval Inference for scale and performance

What this post added

This post details the technical optimizations for Pinecone's retrieval inference system, focusing on embedding generation and reranking. It covers model optimization using NVIDIA TensorRT (kernel tuning, layer fusion, dynamic tensor optimization, precision reduction) with performance gains demonstrated for the bge-reranker-v2-m3 model. It also explains the use of NVIDIA Triton Inference Server with dynamic batching for maximizing GPU utilization, customized for different workloads (query vs. passage). The architecture of scaling with dedicated query and passage infrastructure is described, showing how it maintains stable query latencies even under load. Finally, it highlights the simplification of RAG and agent-based applications through integrated inference, reducing API calls and complexity.