HyCo: Hybrid Drafting for Cost-Efficient Mobile-Cloud Speculative Decoding
Device-cloud speculative decoding reduces server computation, but growing a deep draft tree with a small language model (SLM) requires repeated passes on a mobile neural processing unit (NPU). We present HyCo, which assigns depth to the server model's native multi-token prediction (MTP) head, executed on the device, and alternative branches to one SLM pass. The server returns the hidden state, input embedding, and key-value entries needed by the head instead of executing its sequential steps. Quantization, a restricted vocabulary, and a 1K-token window fit both drafters to the NPU. An ε-threshold rule additionally relaxes exact preservation of the verifier's sampling distribution. In sing...
| Authors | Hanbin Bae, Heejin Choi, Joohyung Lee et al. |
|---|---|
| Primary category | cs.CL |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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