Every public LLM cost calculator we surveyed treats GPU utilization as a fixed input -- entered by the user, baked in as a preset, or silently assumed at 100% -- never measured against the operator's actual load. We show that this assumption is the dominant source of error: on identical H100 hardware, effective cost spans \$0.21 to \$15.25 per million output tokens, an underutilization penalty of 2.5-24x across low-to-moderate enterprise loads (1-10 rps) and up to 36.3x near idle -- driven by one operator-controlled variable, offered request rate lambda, which sets in-flight concurrency via Little's Law and which no open-source calculator exposes. Because calculators take utilization as a user-supplied input, any utilization-naive estimate understates true cost by exactly 1/U, systematically mispricing self-hosting -- most severely over-selling it for low-traffic workloads. We propose a measurement methodology that parameterizes the relationship as C_eff = f(H, M, Q, lambda, L), validate it with 42 benchmarks across dense, ultra-sparse MoE, and sparse MoE models, and release vllm-cost-meter, an open-source cost meter that attaches to a live vLLM server and reports real \$/M-tokens against the operator's own traffic. We further show that FP8 quantization benefits the MoE architectures we tested roughly 2.2-2.4x more than the dense model (+69 to +74% vs. +31% peak throughput; n=3, broader validation needed), and our data are consistent with active parameter count, not total model size, being a primary predictor of saturation economics. To rule out single-hardware confounding we repeat the core sweep on A100 80GB PCIe (56 runs): the load-driven spread reproduces at 7.0-11.4x, the active-parameters ordering survives at FP8, and the dense-FP8 advantage inverts on silicon without native FP8 tensor cores -- a hardware-conditional caveat the framework already accommodates.

翻译：我们调查的所有公开LLM成本计算器都将GPU利用率视为固定输入——由用户输入、作为预设值嵌入，或默认为100%——从未根据运营商的实际负载进行测量。我们证明这一假设是误差的主要来源：在相同的H100硬件上，每百万输出令牌的有效成本从0.21美元到15.25美元不等，在企业低至中等负载（1-10 rps）下，利用率不足导致的成本惩罚为2.5-24倍，在接近空闲时高达36.3倍——这取决于运营商控制的一个变量，即请求率lambda，该变量通过Little定律决定了并发飞行任务数，并且没有开源计算器公开此变量。由于计算器将利用率视为用户提供的输入，任何忽略利用率的估计都会恰好低估真实成本1/U倍，系统性地错误定价自托管方案——对低流量工作负载而言，这种低估最为严重。我们提出一种测量方法，将关系参数化为C_eff = f(H, M, Q, lambda, L)，并通过42个基准测试在密集型、超稀疏MoE和稀疏MoE模型上验证该方法，同时发布vllm-cost-meter这一开源成本计量工具，该工具可连接到在线vLLM服务器，并根据运营商自身流量报告实际的每百万令牌美元成本。我们进一步证明，FP8量化对我们测试的MoE架构的收益大约是密集型模型的2.2-2.4倍（峰值吞吐量提升+69%至+74%，相对于+31%；n=3，需更广泛验证），我们的数据与活跃参数数量（而非总模型大小）作为饱和度经济学主要预测指标这一观点一致。为排除单一硬件干扰，我们在A100 80GB PCIe上重复核心扫描（56次运行）：负载驱动成本波动在7.0-11.4倍范围内复现，活跃参数排序在FP8下得以保持，而密集型-FP8优势在未配备原生FP8张量核心的硅片上发生逆转——框架已包含这一硬件条件相关的注意事项。