Lossless but Not Free: An Empirical Anatomy of Speculative Decoding on Consumer Hardware

Abstract:Single-stream autoregressive decoding of large language models is bound by memory bandwidth: each generated token requires one full forward pass through the target model, and successive passes cannot be parallelized. Speculative decoding restructures this computation: a small draft model proposes $K$ tokens autoregressively, the target model scores all of them in one batched pass, and a rejection-sampling rule provably preserves the target model's output distribution. We present a from-scratch, device-agnostic (CUDA/MPS/CPU) implementation and an empirical study across five draft/target backend configurations on a consumer Apple-silicon laptop. Distribution equivalence is verified at three levels, culminating in a two-sample test over roughly 9,200 real-model tokens per method ($\chi^2 = 162.5$, dof $= 200$, $p = 0.976$) and exact greedy-sequence agreement. The best configuration reaches a measured $1.61\times$ wall-clock speedup at $K=6$, on an acceptance profile declining from 69.7% at $K=1$ to 37.8% at the optimum, while three of five configurations decelerate, either because the draft fails to out-speed a small target or because the quantized Metal backend executes "parallel" verification serially, an effect we isolate and quantify. The failures are as instructive as the successes: speculative decoding pays off only when verification is genuinely batch-parallel and the draft/target latency gap is real.

15 pages, 5 figures, 4 tables. Code, benchmark harness, and all result artifacts: this https URL

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2607.17283 [cs.AI]

(or arXiv:2607.17283v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.17283 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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