标题： DSSD：通过分布式拆分推测解码实现高效边缘设备大模型部署和协同推理 显示英文标题

标题： DSSD: Efficient Edge-Device LLM Deployment and Collaborative Inference via Distributed Split Speculative Decoding

摘要： 大型语言模型（LLMs）已经改变了自然语言处理，但由于资源限制和通信开销，在设备边缘系统中面临关键的部署挑战。 为了解决这些问题，出现了协作框架，将设备上的小型语言模型（SLMs）与边缘的LLMs结合，使用推测解码（SD）来提高效率。 然而，现有解决方案往往以牺牲推理准确率为代价来降低延迟，或在验证候选标记时遭受高上行传输成本。 在本文中，我们提出了分布式分割推测解码（DSSD），这是一种新架构，不仅保留了SLM-LLM的分割，还将在设备和边缘之间划分验证阶段。 这样，DSSD用单一下行传输替代多个词汇分布的上行传输，显著降低了通信延迟，同时保持推理质量。 实验表明，我们的解决方案优于当前方法，代码位于：https://github.com/JasonNing96/DSSD-Efficient-Edge-Computing 显示英文摘要

摘要： Large language models (LLMs) have transformed natural language processing but face critical deployment challenges in device-edge systems due to resource limitations and communication overhead. To address these issues, collaborative frameworks have emerged that combine small language models (SLMs) on devices with LLMs at the edge, using speculative decoding (SD) to improve efficiency. However, existing solutions often trade inference accuracy for latency or suffer from high uplink transmission costs when verifying candidate tokens. In this paper, we propose Distributed Split Speculative Decoding (DSSD), a novel architecture that not only preserves the SLM-LLM split but also partitions the verification phase between the device and edge. In this way, DSSD replaces the uplink transmission of multiple vocabulary distributions with a single downlink transmission, significantly reducing communication latency while maintaining inference quality. Experiments show that our solution outperforms current methods, and codes are at: https://github.com/JasonNing96/DSSD-Efficient-Edge-Computing