Why GPU Inference Optimization Is the New Bottleneck The cost of running large language models in production is dominated by GPU inference. Training gets the headlines, but inference is where enterprises spend the bulk of their AI compute budget, month after month, as every customer query, agent action, and automated workflow requires GPU cycles to generate responses. For a typical enterprise running multiple LLM-powered applications, inference costs can easily reach tens of thousands of dollars per month, and that number grows linearly with usage unless the infrastructure is actively optimized. The challenge is multidimensional. Model size determines baseline VRAM requirements: a 70B parameter model at FP16 needs roughly 140GB of GPU memory just for weights. The choice of inference engine determines how efficiently memory and compute are used. Quantization strategies trade varying degrees of quality for significant throughput improvements.…