aiqbench analysis
This research introduces Speculative Pipeline Decoding to accelerate LLM inference by partitioning the model into parallel stages that hide draft latency through concurrent prediction. By bounding prediction difficulty across pipeline steps, this method achieves superior speedups compared to existing techniques like EAGLE-3 without compromising accuracy. The work addresses critical bottlenecks in low-concurrency scenarios, offering a viable path toward more efficient large-scale deployment of generative models.
Speculative Pipeline Decoding: Higher-Accuracy Drafting with Hidden Latency via Pipeline Parallelism
arXiv:2605.30852v3 Announce Type: replace Abstract: Speculative Decoding (SD) accelerates low-concurrency LLM inference with a draft-then-verify paradigm. Mainstream methods, however, rely on multi-token prediction, which incurs compounding prediction difficulty and exposed draft latency. We propose Speculative Pipeline Decoding (SPD), which partitions the target LLM into $n$ pipeline stages so that $n$ tokens of a single sequence advance in parallel. To keep the pipeline saturated, a Pipeline Draft Module (PDM) aggregates multi-depth target features to predict the next token and runs concurrently with each pipeline step, yielding bounded prediction difficulty, higher acceptance, and hidden draft latency. Experiments show that SPD achieves higher theoretical and wall-clock speedup than EAGLE-3 at moderate pipeline width, while more aggressive widths still leave room for further gains. Our code is available at https://github.com/yuyijiong/speculative_pipeline_decoding
Abstract:Speculative Decoding (SD) accelerates low-concurrency LLM inference with a draft-then-verify paradigm. Mainstream methods, however, rely on multi-token prediction, which incurs compounding prediction difficulty and exposed draft latency. We propose Speculative Pipeline Decoding (SPD), which partitions the target LLM into $n$ pipeline stages so that $n$ tokens of a single sequence advance in parallel. To keep the pipeline saturated, a Pipeline Draft Module (PDM) aggregates multi-depth target features to predict the next token and runs concurrently with each pipeline step, yielding bounded prediction difficulty, higher acceptance, and hidden draft latency. Experiments show that SPD achieves higher theoretical and wall-clock speedup than EAGLE-3 at moderate pipeline width, while more aggressive widths still leave room for further gains. Our code is available at this https URL
Computation and Language (cs.CL)
arXiv:2605.30852 [cs.CL]
(or arXiv:2605.30852v3 [cs.CL] for this version)
Full text under the publisher's open licence · 135 words
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