The efficient frontier of LLM inference

The article explains how inference engineers use the “efficient frontier” concept to balance latency, throughput, cost, and quality in large-language-model deployments. It details trade-off techniques such as batch sizing, tensor, expert, and attention data parallelism, and quantization, and describes how adjusting these parameters can shift a deployment along the frontier or push the entire frontier outward for greater overall efficiency.