#Speculative Decoding

2026

• 05-17 EN

  PipeSD: Cloud-Edge Collaborative Pipeline Inference with Speculative Decoding — Technical Review

• 05-17 中

  PipeSD：基于推测解码的云边协同流水线推理框架 —— 阅读笔记

• 05-16 EN

  An Interpretable Latency Model for Speculative Decoding in LLM Serving — Technical Review

• 05-16 中

  用 Little 定律解释推测解码在真实服务中的提速曲线 —— 阅读笔记

• 05-14 EN

  DisagMoE: Disaggregating Attention and FFN to Beat the MoE All-to-All Bottleneck

• 05-14 中

  DisagMoE：用解耦 Attention 和 FFN 打通 MoE 训练的 all-to-all 瓶颈

• 05-10 EN

  Tutti: Making SSD-Backed KV Cache Practical for Long-Context LLM Serving

• 05-10 中

  Tutti：让基于 SSD 的 KV Cache 真正适用于长上下文 LLM Serving

• 04-19 EN

  SpecGuard: Verification-Aware Speculative Decoding for Efficient Multi-Step Reasoning

• 04-19 中

  SpecGuard：用于多步推理的验证感知推测解码

• 04-15 EN

  LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding — Deep Technical Review

• 04-15 中

  LayerSkip：让大模型“提前退出 + 自校验推理”成为可部署方案——深度阅读笔记

• 03-11 EN

  Speculative Decoding: Making LLM Inference 2-3× Faster Without Losing a Single Token