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[2505.19645] MoESD: Unveil Speculative Decoding's Potential for Accelerating Sparse MoE
Summary
The paper discusses the advantages of speculative decoding (SD) in accelerating sparse mixture of experts (MoE) models, revealing that MoE benefits more from SD than dense models, especially at medium batch sizes.
Why It Matters
This research is significant as it challenges the prevailing notion that speculative decoding is only efficient for dense models. By demonstrating its effectiveness for sparse MoEs, it opens new avenues for optimizing large language models, which are increasingly used in various AI applications. Understanding these dynamics can lead to improved performance and efficiency in AI systems, particularly in resource-constrained environments.
Key Takeaways
- MoE models can achieve greater speedups from speculative decoding than dense models.
- The effectiveness of SD in MoE models broadens with increased sparsity.
- A new metric, 'target efficiency,' helps identify bottlenecks in SD acceleration.
- Experiments show up to 2.29x speedup for specific models at medium batch sizes.
- This work provides insights for improving inference in private serving scenarios.
Computer Science > Machine Learning arXiv:2505.19645 (cs) [Submitted on 26 May 2025 (v1), last revised 16 Feb 2026 (this version, v4)] Title:MoESD: Unveil Speculative Decoding's Potential for Accelerating Sparse MoE Authors:Zongle Huang, Lei Zhu, Zongyuan Zhan, Ting Hu, Weikai Mao, Xianzhi Yu, Yongpan Liu, Tianyu Zhang View a PDF of the paper titled MoESD: Unveil Speculative Decoding's Potential for Accelerating Sparse MoE, by Zongle Huang and 7 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) have achieved remarkable success across many applications, with Mixture of Experts (MoE) models demonstrating great potential. Compared to traditional dense models, MoEs achieve better performance with less computation. Speculative decoding (SD) is a widely used technique to accelerate LLM inference without accuracy loss, but it has been considered efficient only for dense models. In this work, we first demonstrate that, under medium batch sizes, MoE surprisingly benefits more from SD than dense models. Furthermore, as MoE becomes sparser -- the prevailing trend in MoE designs -- the batch size range where SD acceleration is expected to be effective becomes broader. To quantitatively understand tradeoffs involved in SD, we develop a reliable modeling based on theoretical analyses. While current SD research primarily focuses on improving acceptance rates of algorithms, changes in workload and model architecture can still lead to degraded SD acceleration...
