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Centralized inference serving can optimize the execution of deep learning models by reducing resource contention and improving system utilization. This experiment compares decentralized versus centralized inference using a ResNet-152 model on 1,000 images, highlighting the efficiency gains from a dedicated inference server. The setup includes TorchServe for inference serving, which significantly increases throughput and frees up CPU resources for other tasks. The post also outlines steps for setting up TorchServe and suggestions for further optimization.

The Case for Centralized AI Model Inference Serving

Estimating the Maximum Number of Processes

The Inefficiencies of Independent Model Execution