Rethinking AI TCO: Why Cost per Token Is the Only Metric That Matters

Traditional metrics like FLOPS per dollar or cost per GPU hour are insufficient for evaluating AI infrastructure. Cost per token is the true TCO metric for AI inference workloads because it accounts for hardware performance, software optimization, and real-world utilization. A comparison of NVIDIA Hopper vs. Blackwell illustrates this: while Blackwell costs roughly 2x more per GPU hour and offers only 2x better FLOPS per dollar, it delivers 65x more tokens per GPU and 35x lower cost per million tokens when running DeepSeek-R1. Key factors driving token output include FP4 precision support, speculative decoding, disaggregated serving, KV-cache optimizations, and scale-up interconnect quality for MoE models.