How to Size GPUs for AI Inference and TCO Without Overspending | NVIDIA Technical Blog

This post details practical strategies for sizing GPU infrastructure for AI inference workloads and optimizing Total Cost of Ownership (TCO). It introduces a framework for mapping inference workloads to specific use-case categories (AI Chatbots/Copilots, AI Agents, Content Generation, Translation Apps) and outlines key sizing inputs such as model selection, DAUs, concurrency, input/output string lengths, cache hit rate, and latency targets. It also presents a core-and-flex capacity model and emphasizes the importance of right-sizing GPUs to workload memory footprints and latency targets. Furthermore, it highlights model optimization techniques like FP8 post-training quantization (demonstrated with NVIDIA Model Optimizer), pruning, and knowledge distillation as methods to shrink memory footprint and lower TCO. Sample scenarios for financial services and life sciences are provided to illustrate these concepts.