Speculative Decoding Performance Results

In late March, a developer running a local setup on an RTX 4060 8GB published a candid account on Dev.to: every configuration of speculative decoding they tested came out slower than the baseline. Not marginally slower. Every config. For anyone who had been seduced by the theoretical elegance of speculative decoding — where a cheap draft model proposes tokens and a larger model verifies them in parallel, theoretically compressing latency — this was a cold splash. The post scored modestly but circulated in the right circles, because the honest failure report is rarer and more useful than the triumphant one.

Then, three weeks later, the picture shifted. A LocalLLaMA post in mid-April reported speculative decoding working well on Gemma 4 31B with an E2B draft model — 29% average throughput improvement, and 50% on code specifically. That gap between general and code performance is the interesting number. Code is more predictable syntactically, which means the draft model guesses right more often, which means the verifier accepts more tokens per pass. The architecture rewards the use case.

If confirmed, here is what this means. Speculative decoding is not a general-purpose acceleration technique — it is a context-sensitive one, and the hardware envelope matters enormously. The RTX 4060 8GB result suggests that memory-constrained consumer hardware may not provide the bandwidth needed for the draft-verify loop to pay off, while the Gemma 4 result suggests that larger models on adequate hardware, doing structured tasks like code generation, are exactly where the gains materialise. For vibe coders running local inference stacks, the implication is practical and immediate: the technique is not broken, but it is not free either. Model selection, draft model quality, and task type are the variables that determine whether you get 50% faster code completions or slower output than you started with. Practitioners who deployed speculative decoding wholesale and saw no improvement now have a framework for understanding why. Those building coding assistants on top of capable local hardware have a clear target configuration to replicate.

Watch for independent benchmarks on mid-range consumer GPUs — specifically anything testing draft model acceptance rates across hardware tiers — and for whether Gemma 4's architecture has properties that make it unusually well-suited to this approach, which would limit how broadly the positive result generalises.

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