The surge in AI adoption is transforming everything from chatbots to content generation. Still, a common pain point remains: How can organizations confidently size GPU resources for inference workloads and optimize Total Cost of Ownership (TCO)? With a dizzying mix of latency targets, model choices, quirky traffic patterns, and budget constraints, it’s easy to feel lost in the weeds, even before you’ve deployed a single model.
Today’s inference landscape is shaped by more than just hardware specs or “tokens per second.” Teams face an ever-growing list of sizing decisions: What kind of latency actually matters, Time to First Token (TTFT) average, 99th percentile latency, intertoken latency, or something else? How will your use case’s token patterns drive GPU memory and compute needs? What’s the right balance between on-prem core capacity and cloud-based elasticity?
This post offers a practical framework for mapping your use case to the right GPU footprint, sizing inference GPU infrastructure around real workload behavior rather than guesswork. We’ll walk through the inputs that matter most, including use case, token patterns, latency targets, concurrency, cache hit rate, model choice, and deployment strategy. Along the way, developers and infrastructure teams will see how core-and-flex capacity planning, right-sized GPUs, and model optimization techniques such as quantization, pruning, and distillation can improve performance while lowering TCO.
