Hardware
How to Size GPUs for AI Inference and TCO Without Overspending
Inference workloads fall into four use‑case buckets (Chatbots/Copilots, Agents, Content Generation, Translation), each with distinct token patterns that dictate the required GPU memory and compute. Si
Inference workloads fall into four use‑case buckets (Chatbots/Copilots, Agents, Content Generation, Translation), each with distinct token patterns that dictate the required GPU memory and compute. Size GPU infrastructure by quantifying concrete inputs—model choice, DAU, concurrency, I/O string lengths, cache hit rate, latency targets, contract length—and use a core‑and‑flex strategy that pairs on‑prem or reserved cloud GPUs for steady traffic with spot/on‑demand GPUs for bursts to balance CAPEX and OPEX. Model compression techniques such as FP8 post‑training quantization (e.g., 43.5 % weight shrinkage in Llama‑3.1‑8B), depth/width pruning, and distillation reduce memory footprints, enabling smaller GPU deployments and lowering total cost of ownership.
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Source: NVIDIA Developer | 2026-09-01