Speculative inference (SPIN) was originally developed as an efficient architecture to accelerate Large Language Models (LLMs). In this work, we propose its distributed deployment to enable cooperative token generation in a multiuser edge system; its advantage is to effectively balance computational loads between resource-constrained devices and servers. The resulting architecture, termed Multi-access SPIN (Multi-SPIN), utilizes on-device small language models to generate and upload candidate token drafts, while an edge server operates the LLM to verify them in parallel batches. Given the severe heterogeneity in users' computation and communication capabilities, the draft length emerges as a critical control variable that influences node-level computation loads and multi-access latency, thereby governing the sum token goodput. Consequently, considering frequency-division multiple access, we investigate the problem of multi-access draft control, a joint optimization of draft-length control and bandwidth allocation to maximize sum token goodput. We examine two cases: (1) homogeneous draft lengths across users to facilitate server-side batching, and (2) heterogeneous draft lengths to introduce a new dimension for goodput enhancement. By developing decomposition methods, we reduce these complex optimizations into tractable sub-problems, which allow efficient draft control algorithms to be derived in closed form. Our analysis shows that the optimal bandwidth allocation compensates users with weaker computation-and-communication capabilities in the homogeneous case due to the batching synchronization requirements, whereas its heterogeneous-case counterpart rewards users with higher acceptance rates by relaxing such requirements. Experiments using Llama-2 and Qwen3.5 model pairs across diverse tasks demonstrate that Multi-SPIN improves goodput by up to 88% over heterogeneity-agnostic baselines.

翻译：推测推理（SPIN）最初被提出作为一种高效架构以加速大型语言模型（LLMs）。本文提出其分布式部署方案，用于在多用户边缘系统中实现协作式令牌生成，其优势在于有效平衡资源受限设备与服务器之间的计算负载。所提出的架构称为多接入推测推理（Multi-SPIN），利用设备端小语言模型生成并上传候选令牌草稿，而边缘服务器则运行大语言模型以并行批次验证这些草稿。鉴于用户计算与通信能力的严重异质性，草稿长度成为影响节点级计算负载与多接入延迟的关键控制变量，进而决定了令牌总有效吞吐量。为此，本文基于频分多址接入，研究多接入草稿控制问题——即通过联合优化草稿长度控制与带宽分配以实现令牌总有效吞吐量最大化。我们考察两种场景：（1）用户间采用同质草稿长度以促进服务器端批处理，（2）采用异质草稿长度以引入有效吞吐量提升的新维度。通过开发分解方法，我们将这些复杂优化问题简化为可处理的子问题，从而推导出闭合形式的草稿控制高效算法。分析表明，在同质场景中，由于批处理同步需求，最优带宽分配会补偿计算与通信能力较弱的用户；而在异质场景中，最优带宽分配则通过放宽同步要求来奖励具有更高接受率的用户。基于Llama-2与Qwen3.5模型对的多任务实验表明，Multi-SPIN相比忽视异质性的基线方法，有效吞吐量提升最高达88%。