VLM

Vision-Language Model Serving in Production: Visual Token Pruning, Encoder Caching, Dynamic Resolution, and Inference Economics

Deploying Vision-Language Models (VLMs) into high-concurrency production environments introduces a distinct set of systems bottlenecks that text-only large language models do not exhibit. While text models ingest prompts with compact token densities, visual inputs require processing high-dimensional pixel arrays through vision encoders, expanding a single image into hundreds or thousands of visual tokens before autoregressive generation begins. In production architectures running models such as