LLM Inference Optimization Can Cut AI Serving Costs by Up to 10x

Running large language models in production makes inference the dominant AI cost, with a meter running on every request around the clock. The gap between unoptimized and optimized serving typically amounts to a 5–10x difference in cost and a 3–5x difference in latency. Key techniques include continuous batching, which can push GPU utilization from roughly 20–30% up to 80–90%, and KV-cache management methods like PagedAttention, which nearly eliminate memory waste and allow two to three times more concurrent requests. Quantization approaches such as FP8 and INT4 reduce data movement and model footprint, while speculative decoding lowers latency without sacrificing output quality. Together, these well-established methods can determine whether an AI feature is economically viable enough to ship at all.