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Equation 22 · How AI Inference Serving Actually Works

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α\alpha

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α\alpha

Symbol α

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This rises with both α\alpha and γ\gamma , but with steeply diminishing returns in γ\gamma for any α\alpha below one: drafting fifty tokens ahead does not buy anywhere near fifty accepted tokens, because the marginal proposal deep into a long draft is unlikely to be exactly what the target would have generated. The bottleneck decode started with reappears one level down — the draft model is itself a small, serial, memory-bound process, so speculative decoding is not a free lunch; it trades some of decode’s own bandwidth cost for a second model running continuously, plus wasted target-model computation on every rejected proposal. Its win is largest where the roofline argument above says spare…
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This rises with both α\alpha and γ\gamma , but with steeply diminishing returns in γ\gamma for any α\alpha below one: drafting fifty tokens ahead does not buy anywhere near fifty accepted tokens, because the marginal proposal deep into a long draft is unlikely to be exactly what the target would have generated. The bottleneck decode started with reappears one level down — the draft model is itself a small, serial, memory-bound process, so speculative decoding is not a free lunch; it trades some of decode’s own bandwidth cost for a second model running continuously, plus wasted target-model computation on every rejected proposal. Its win is largest where the roofline argument above says spare compute is most available: at low batch sizes, where the target model is memory-bound and its arithmetic units sit mostly idle during an ordinary decode step anyway. That idle arithmetic verifies the extra proposed tokens at close to no additional cost.

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