From Prototype to Production: The Hidden Engineering of AI Inference

Efficient LLM serving requires teams to reason about memory pressure, request scheduling, and workload goals together. Roan Weigert follows an inference request from tokenization through generation, then explains how KV caching avoids repeated computation while consuming per-request memory. He compares static and continuous batching, outlines quantization trade-offs, and separates latency and throughput from headline GPU utilization by introducing model FLOPs utilization. There is no universal infrastructure choice: local models, shared APIs, dedicated GPUs, and hybrid workflows suit different volumes, response-time needs, and operating constraints.