arXiv Machine Learning By Hanbin Bae, Heejin Choi, Joohyung Lee, Byoung Jin Choi, Sangjun Park, Hoon-Young Cho

HyCo: Hybrid Drafting for Cost-Efficient Mobile-Cloud Speculative Decoding

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HyCo is a hybrid drafting method that assigns depth to a server model’s multi‑token prediction head for execution on a mobile NPU, while alternative branches are handled by a small language model in a single pass. By returning only the hidden state, input embedding, and key‑value entries, HyCo reduces server GPU time per token by 38.6% on Spec‑Bench and 29.2% on 32K‑token summarization, and achieves faster delivery and higher savings than an SLM‑only drafter. The approach also incorporates quantization, a restricted vocabulary, a 1K‑token window, and an ε‑threshold rule to relax exact sampling distribution preservation.

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