This technical explainer analyzes the hardware mismatch between image encoding and token decoding phases in vision-language model inference. It details how splitting the pipeline between compute-bound encode and memory-bandwidth-bound decode can reduce infrastructure costs by 40%, and provides profiling guidance for deployment.