SpecMoE: A Fast and Efficient Mixture-of-Experts Inference via Self-Assisted Speculative Decoding

• URL: http://arxiv.org/abs/2604.10152v1
• Date: Sat, 11 Apr 2026 10:52:17 GMT
• Title: SpecMoE: A Fast and Efficient Mixture-of-Experts Inference via Self-Assisted Speculative Decoding
• Abstract summary: SpecMoE is a memory-efficient MoE inference system based on our self-assisted speculative decoding algorithm.<n>Our system improves inference throughput by up to $4.30times$, while significantly reducing bandwidth requirements of both memory and interconnect on memory-constrained systems.
• Score: 3.1396754306335652
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The Mixture-of-Experts (MoE) architecture has emerged as a promising approach to mitigate the rising computational costs of large language models (LLMs) by selectively activating parameters. However, its high memory requirements and sub-optimal parameter efficiency pose significant challenges for efficient deployment. Although CPU-offloaded MoE inference systems have been proposed in the literature, they offer limited efficiency, particularly for large batch sizes. In this work, we propose SpecMoE, a memory-efficient MoE inference system based on our self-assisted speculative decoding algorithm. SpecMoE demonstrates the effectiveness of applying speculative decoding to MoE inference without requiring additional model training or fine-tuning. Our system improves inference throughput by up to $4.30\times$, while significantly reducing bandwidth requirements of both memory and interconnect on memory-constrained systems.

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