Vector Search: Deployment without GPU on Triton Inference Server

This article focuses on the practical aspects of deploying machine learning models for vector search tasks. The author explores the process of deploying a model into a production environment using the Triton Inference Server without the use of graphics processing units (GPUs). The material details the stages of model preparation, performance optimization for CPU-based execution, and integration of the solution into service infrastructure. The main focus is on overcoming technical limitations when scaling ML solutions, allowing for efficient resource utilization without the need for expensive hardware. This article is useful for ML engineers and DevOps specialists dealing with production model deployment and computational cost optimization.