Speculative decoding accelerates memory-bound LLM decoding with a lightweight drafter, and parallel drafters extend this speedup by drafting a whole block in one forward pass. However, it remains uncl
SpecPilot autonomously discovers deployment-specific algorithms and system optimizations to maximize speculative decoding speedups across diverse models, drafters, and serving regimes without quality loss.
Speculative decoding accelerates memory-bound LLM decoding with a lightweight drafter, and parallel drafters extend this speedup by drafting a whole block in one forward pass. However, it remains unclear whether their released systems (stock) attain the full speedup that parallel drafting permits. We identify three challenges. First, with one request, each step accepts few tokens but reads every target weight. Second, with batching, per-request verification work roughly halves the speedup over autoregressive (AR) decoding. Third, speculative decoding is deployment-specific, and no universal optimization fits every serving regime, drafter, target model and GPU. To address these challenges,…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
SpecPilot: Autonomous Algorithm Discovery and Systems Optimization for Speculative Decoding was first seen on Hugging Face · Daily papers on Nov 4, 2026. Since then it has appeared in 12 checks on 2 different days. Its best position was #14 of 30, reached on Nov 5, 01:28 UTC. It dropped off the list after Nov 5, 01:28 UTC.
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It was first seen on Nov 4, 2026 and has been on the list on 2 days (12 checks) since.