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Equation 1 · Part 7 · How AI Inference Economics Actually Work

multiplication

price per token≳Cfixed+CenergyU⋅Tmax⁡+m\text{price per token} \gtrsim \frac{C_{\text{fixed}} + C_{\text{energy}}}{U \cdot T_{\max}} + m
multiplication

What this part means

Multiply the quantities on either side.

Its job in the formula

Multiply the quantities on either side.

The passage around this formula

This is the reason batching and caching matter for price rather than only for latency: a serving system that keeps a GPU at 80% average utilization across a day divides its fixed hourly cost across roughly twice as many tokens as one running at 40%, and can profitably charge roughly half as much per token for the same margin. Independent benchmarking gives some visibility into what utilization current hardware and software combinations can actually achieve under realistic load. MLCommons’ MLPerf Inference benchmark suite tests submitted systems under both a latency-bounded “server” scenario and an unconstrained “offline” scenario designed to maximize throughput through batching, and recent…

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Learn the underlying idea

Multiplication scales one quantity by another. A dot, a cross, or adjacent symbols can indicate a product.

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Sources cited in the surrounding passage

These citations provide research context; check each source for the exact claim it supports.